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---
name: Architecture-First Reading Protocol
description: >
This skill should be used at the start of every new technical task, new session,
or when switching to a different part of the codebase. It enforces mandatory reading
of architectural decisions, current project state, and active plans before any code
is written, modified, or proposed. Relevant when the user says "implement a feature",
"fix a bug", "refactor code", "add a new module", "modify configuration", "change architecture",
"start a task", "begin work on", "let's build", or "work on". This skill acts as a gatekeeper
ensuring all code changes respect established Architecture Decision Records (ADRs).
version: 0.1.0
---
# Architecture-First Reading Protocol
## Purpose
Enforce a mandatory reading sequence before writing any code, modifying configurations,
or proposing solutions. All established architectural rules in `docs/agent/decisions/`
are treated as absolute laws. Violating an ADR without explicit user approval is forbidden.
## Mandatory Reading Sequence
Execute the following steps **in order** before producing any code or solution.
### Step 1: Read Current State
Read `docs/agent/CURRENT_STATE.md` to understand:
- The active milestone and sprint focus
- Any blockers or constraints currently in effect
- Recent changes that affect the working context
If the file does not exist, note this and proceed — but flag it to the user as a gap.
### Step 2: Query Architectural Decisions
List all files in `docs/agent/decisions/` and identify which ADRs are relevant to the
current task. If this directory does not exist, skip to Step 3.
**Relevance matching rules:**
- Match by filename keywords (e.g., task involves "auth" → read `0002-jwt-auth.md`)
- Match by YAML `tags` in ADR frontmatter if present
- When uncertain, read the ADR — false positives cost less than missed constraints
**For each relevant ADR, extract and internalize:**
- `Consequences & Constraints` section → treat as hard rules
- `Actionable Rules` section → treat as implementation requirements
- `Status` field → only `accepted` or `active` ADRs are binding
See `references/adr-template.md` for the expected ADR structure.
### Step 3: Check Active Plans
List files in `docs/agent/plans/` and identify any plan related to the current task.
If this directory does not exist, skip to Step 4.
- Read the active plan to determine which step is currently being executed
- Do not skip steps unless the user explicitly instructs it
- If no plan exists for the task, proceed but note the absence
### Step 4: Acknowledge Reading
Begin the first response to any technical task with a brief acknowledgment:
```
I have reviewed:
- `CURRENT_STATE.md`: [one-line summary]
- `decisions/XXXX-name.md`: [relevant constraint noted]
- `plans/active-plan.md`: [current step]
Proceeding with [task description]...
```
If no docs exist yet, state:
```
No architecture docs found in docs/agent/. Proceeding without ADR constraints.
Consider scaffolding the agent memory structure if this project needs architectural governance.
```
## Conflict Resolution Protocol
When a user request contradicts an ADR rule:
1. **STOP** — do not write or propose conflicting code
2. **Quote** the specific rule from the decision file, including the file path
3. **Inform** the user of the conflict clearly:
```
⚠️ Conflict detected with `docs/agent/decisions/XXXX-name.md`:
Rule: "[exact quoted rule]"
Your request to [description] would violate this constraint.
Options:
A) Modify the approach to comply with the ADR
B) Update the ADR to allow this exception (I can draft the amendment)
C) Proceed with an explicit architectural exception (will be logged)
```
4. **Wait** for the user's decision before proceeding
## Directory Structure Expected
```
docs/agent/
├── CURRENT_STATE.md # Active milestone, blockers, context
├── decisions/ # Architecture Decision Records
│ ├── 0001-example.md
│ ├── 0002-example.md
│ └── ...
├── plans/ # Active implementation plans
│ ├── active-plan.md
│ └── ...
└── logs/ # Session logs (optional)
```
## Graceful Degradation
Handle missing documentation gracefully:
| Condition | Action |
|---|---|
| `docs/agent/` missing entirely | Proceed without constraints; suggest scaffolding |
| `CURRENT_STATE.md` missing | Warn user, continue to decisions check |
| `decisions/` empty | Note absence, proceed without ADR constraints |
| `plans/` empty | Proceed without plan context |
| ADR has no `Status` field | Treat as `accepted` (binding) by default |
## Integration with Existing Workflows
This protocol runs **before** the existing TradingAgents flows:
- Before the Agent Flow (analysts → debate → trader → risk)
- Before the Scanner Flow (scanners → deep dive → synthesis)
- Before any CLI changes, config modifications, or test additions
## Additional Resources
### Reference Files
- **`references/adr-template.md`** — Standard ADR template for creating new decisions
- **`references/reading-checklist.md`** — Quick-reference checklist for the reading sequence

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# ADR Template
Architecture Decision Records follow this structure. Use this template when creating
new decisions in `docs/agent/decisions/`.
## Filename Convention
```
NNNN-short-descriptive-name.md
```
- `NNNN` — zero-padded sequential number (0001, 0002, ...)
- Use lowercase kebab-case for the name portion
## Template
```markdown
---
title: "Short Decision Title"
status: proposed | accepted | deprecated | superseded
date: YYYY-MM-DD
tags: [relevant, keywords, for, matching]
superseded_by: NNNN-new-decision.md # only if status is superseded
---
# NNNN — Short Decision Title
## Context
Describe the problem, forces at play, and why a decision is needed.
Include relevant technical constraints, business requirements, and
any alternatives considered.
## Decision
State the decision clearly and concisely. Use active voice.
Example: "Use JWT tokens for API authentication with RS256 signing."
## Consequences & Constraints
List the binding rules that follow from this decision. These are
treated as **absolute laws** by the Architecture-First Reading Protocol.
- **MUST**: [mandatory requirement]
- **MUST NOT**: [explicit prohibition]
- **SHOULD**: [strong recommendation]
Example:
- MUST use RS256 algorithm for all JWT signing
- MUST NOT store tokens in localStorage
- SHOULD rotate signing keys every 90 days
## Actionable Rules
Concrete implementation requirements derived from the decision:
1. [Specific code/config requirement]
2. [Specific code/config requirement]
3. [Specific code/config requirement]
## Alternatives Considered
| Alternative | Reason Rejected |
|---|---|
| Option A | [why not chosen] |
| Option B | [why not chosen] |
## References
- [Link or file reference]
- [Related ADR: NNNN-related.md]
```
## Status Lifecycle
```
proposed → accepted → [deprecated | superseded]
```
- **proposed** — Under discussion, not yet binding
- **accepted** — Active and binding; all code must comply
- **deprecated** — No longer relevant; may be ignored
- **superseded** — Replaced by another ADR (link via `superseded_by`)
## Best Practices
- Keep decisions focused — one decision per file
- Write constraints as testable statements where possible
- Tag decisions with module/domain keywords for easy matching
- Reference related decisions to build a decision graph
- Date all decisions for historical context

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# Architecture Reading Checklist
Quick-reference checklist for the mandatory reading sequence.
Execute before every technical task.
## Pre-Flight Checklist
```
[ ] 1. Read docs/agent/CURRENT_STATE.md
→ Note active milestone
→ Note blockers
→ Note recent context changes
[ ] 2. List docs/agent/decisions/*.md
→ Identify ADRs relevant to current task
→ For each relevant ADR:
[ ] Read Consequences & Constraints
[ ] Read Actionable Rules
[ ] Verify status is accepted/active
[ ] Note any hard prohibitions (MUST NOT)
[ ] 3. List docs/agent/plans/*.md
→ Find active plan for current task
→ Identify current step in plan
→ Do not skip steps without user approval
[ ] 4. Acknowledge in response
→ List reviewed files
→ Summarize relevant constraints
→ State intended approach
```
## Quick Relevance Matching
To find relevant ADRs efficiently:
1. **Extract keywords** from the task description
2. **Match against filenames** in `docs/agent/decisions/`
3. **Check YAML tags** in ADR frontmatter
4. **When in doubt, read it** — a false positive is cheaper than a missed constraint
### Common Keyword → ADR Mapping Examples
| Task Keywords | Likely ADR Topics |
|---|---|
| auth, login, token, session | Authentication, authorization |
| database, schema, migration | Data layer, ORM, storage |
| API, endpoint, route | API design, versioning |
| deploy, CI/CD, pipeline | Infrastructure, deployment |
| LLM, model, provider | LLM configuration, vendor routing |
| agent, graph, workflow | Agent architecture, LangGraph |
| config, env, settings | Configuration management |
| test, coverage, fixture | Testing strategy |
## Conflict Response Template
When a conflict is detected, use this template:
```
⚠️ Conflict detected with `docs/agent/decisions/XXXX-name.md`:
Rule: "[exact quoted rule from Consequences & Constraints or Actionable Rules]"
Your request to [brief description of the conflicting action] would violate this constraint.
Options:
A) Modify the approach to comply with the ADR
B) Update the ADR to allow this exception (I can draft the amendment)
C) Proceed with an explicit architectural exception (will be logged)
Which option do you prefer?
```
## Graceful Degradation Quick Reference
| Missing Resource | Action |
|---|---|
| Entire `docs/agent/` | Proceed; suggest scaffolding the directory structure |
| `CURRENT_STATE.md` only | Warn, continue to decisions |
| `decisions/` empty | Note absence, proceed freely |
| `plans/` empty | Proceed without plan context |
| ADR missing `Status` | Default to `accepted` (binding) |
| ADR status `proposed` | Informational only, not binding |
| ADR status `deprecated` | Ignore, not binding |

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# LLM Providers (set the one you use)
# LLM Provider API Keys (set the ones you use)
OPENAI_API_KEY=
GOOGLE_API_KEY=
ANTHROPIC_API_KEY=
XAI_API_KEY=
OPENROUTER_API_KEY=
# Data Provider API Keys
ALPHA_VANTAGE_API_KEY=
# ── Configuration overrides ──────────────────────────────────────────
# Any setting in DEFAULT_CONFIG can be overridden with a
# TRADINGAGENTS_<KEY> environment variable. Unset or empty values
# are ignored (the hardcoded default is kept).
#
# Examples:
# TRADINGAGENTS_LLM_PROVIDER=openrouter
# TRADINGAGENTS_QUICK_THINK_LLM=deepseek/deepseek-chat-v3-0324
# TRADINGAGENTS_DEEP_THINK_LLM=deepseek/deepseek-r1-0528
# TRADINGAGENTS_BACKEND_URL=https://openrouter.ai/api/v1
# TRADINGAGENTS_RESULTS_DIR=./my_results
# TRADINGAGENTS_MAX_DEBATE_ROUNDS=2
# TRADINGAGENTS_VENDOR_SCANNER_DATA=alpha_vantage

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CLAUDE.md
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- LLM tiers configuration
- Vendor routing
- Debate rounds settings
- All values overridable via `TRADINGAGENTS_<KEY>` env vars (see `.env.example`)
## Patterns to Follow
@ -86,15 +87,17 @@ OpenAI, Anthropic, Google, xAI, OpenRouter, Ollama
- Graph setup (scanner): `tradingagents/graph/scanner_setup.py`
- Inline tool loop: `tradingagents/agents/utils/tool_runner.py`
## Critical Patterns (from past mistakes)
## Critical Patterns (see `docs/agent/decisions/008-lessons-learned.md` for full details)
- **Tool execution**: Trading graph uses `ToolNode` in graph. Scanner agents use `run_tool_loop()` inline. If `bind_tools()` is used, there MUST be a tool execution path.
- **yfinance DataFrames**: `top_companies` has ticker as INDEX, not column. Always check `.index` and `.columns`.
- **yfinance Sector/Industry**: `Sector.overview` has NO performance data. Use ETF proxies for performance.
- **Vendor fallback**: Functions inside `route_to_vendor` must RAISE on failure, not embed errors in return values. Catch `AlphaVantageError` (base class), not just `RateLimitError`.
- **Vendor fallback**: Functions inside `route_to_vendor` must RAISE on failure, not embed errors in return values. Catch `(AlphaVantageError, ConnectionError, TimeoutError)`, not just `RateLimitError`.
- **LangGraph parallel writes**: Any state field written by parallel nodes MUST have a reducer (`Annotated[str, reducer_fn]`).
- **Ollama remote host**: Never hardcode `localhost:11434`. Use configured `base_url`.
- **.env loading**: Check actual env var values when debugging auth. Worktree and main repo may have different `.env` files.
- **.env loading**: `load_dotenv()` runs at module level in `default_config.py` — import-order-independent. Check actual env var values when debugging auth.
- **Rate limiter locks**: Never hold a lock during `sleep()` or IO. Release, sleep, re-acquire.
- **Config fallback keys**: `llm_provider` and `backend_url` must always exist at top level — `scanner_graph.py` and `trading_graph.py` use them as fallbacks.
## Project Tracking (Memory System)
@ -106,16 +109,33 @@ Do NOT write to `DECISIONS.md`, `MISTAKES.md`, or `PROGRESS.md`. Use the memory
A `PreToolUse` hook enforces this — writes to those files are automatically blocked.
## Current LLM Configuration (Hybrid)
- `docs/agent/CURRENT_STATE.md` — Live state tracker (milestone, progress, blockers). Read at session start.
- `docs/agent/decisions/` — Architecture decision records (ADR-style, numbered `001-...`)
- `docs/agent/plans/` — Implementation plans with checkbox progress tracking
- `docs/agent/logs/` — Agent run logs
- `docs/agent/templates/` — Commit, PR, and decision templates
```
quick_think: qwen3.5:27b via Ollama (http://192.168.50.76:11434)
mid_think: qwen3.5:27b via Ollama (http://192.168.50.76:11434)
deep_think: deepseek/deepseek-r1-0528 via OpenRouter
Before starting work, always read `docs/agent/CURRENT_STATE.md`. Before committing, update it.
## LLM Configuration
Per-tier provider overrides in `tradingagents/default_config.py`:
- Each tier (`quick_think`, `mid_think`, `deep_think`) can have its own `_llm_provider` and `_backend_url`
- Falls back to top-level `llm_provider` and `backend_url` when per-tier values are None
- All config values overridable via `TRADINGAGENTS_<KEY>` env vars
- Keys for LLM providers: `.env` file (e.g., `OPENROUTER_API_KEY`, `ALPHA_VANTAGE_API_KEY`)
### Env Var Override Convention
```env
# Pattern: TRADINGAGENTS_<UPPERCASE_KEY>=value
TRADINGAGENTS_LLM_PROVIDER=openrouter
TRADINGAGENTS_DEEP_THINK_LLM=deepseek/deepseek-r1-0528
TRADINGAGENTS_MAX_DEBATE_ROUNDS=3
TRADINGAGENTS_VENDOR_SCANNER_DATA=alpha_vantage
```
Config: `tradingagents/default_config.py` (per-tier `_llm_provider` keys)
Keys: `.env` file (`OPENROUTER_API_KEY`, `ALPHA_VANTAGE_API_KEY`)
Empty or unset vars preserve the hardcoded default. `None`-default fields (like `mid_think_llm`) stay `None` when unset, preserving fallback semantics.
## Running the Scanner

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DECISIONS.md
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# Architecture Decisions Log
## Decision 001: Hybrid LLM Setup (Ollama + OpenRouter)
**Date**: 2026-03-17
**Status**: Implemented ✅
**Context**: Need cost-effective LLM setup for scanner pipeline with different complexity tiers.
**Decision**: Use hybrid approach:
- **quick_think + mid_think**: `qwen3.5:27b` via Ollama at `http://192.168.50.76:11434` (local, free)
- **deep_think**: `deepseek/deepseek-r1-0528` via OpenRouter (cloud, paid)
**Config location**: `tradingagents/default_config.py` — per-tier `_llm_provider` and `_backend_url` keys.
**Consequence**: Removed top-level `llm_provider` and `backend_url` from config. Each tier must have its own `{tier}_llm_provider` set explicitly.
---
## Decision 002: Data Vendor Fallback Strategy
**Date**: 2026-03-17
**Status**: Implemented ✅
**Context**: Alpha Vantage free/demo key doesn't support ETF symbols and has strict rate limits. Need reliable data for scanner.
**Decision**:
- `route_to_vendor()` catches `AlphaVantageError` (base class) to trigger fallback, not just `RateLimitError`.
- AV scanner functions raise `AlphaVantageError` when ALL queries fail (not silently embedding errors in output strings).
- yfinance is the fallback vendor and uses SPDR ETF proxies for sector performance instead of broken `Sector.overview`.
**Files changed**:
- `tradingagents/dataflows/interface.py` — broadened catch
- `tradingagents/dataflows/alpha_vantage_scanner.py` — raise on total failure
- `tradingagents/dataflows/yfinance_scanner.py` — ETF proxy approach
---
## Decision 003: yfinance Sector Performance via ETF Proxies
**Date**: 2026-03-17
**Status**: Implemented ✅
**Context**: `yfinance.Sector("technology").overview` returns only metadata (companies_count, market_cap, etc.) — no performance data (oneDay, oneWeek, etc.).
**Decision**: Use SPDR sector ETFs as proxies:
```python
sector_etfs = {
"Technology": "XLK", "Healthcare": "XLV", "Financials": "XLF",
"Energy": "XLE", "Consumer Discretionary": "XLY", ...
}
```
Download 6 months of history via `yf.download()` and compute 1-day, 1-week, 1-month, YTD percentage changes from closing prices.
**File**: `tradingagents/dataflows/yfinance_scanner.py`
---
## Decision 004: Inline Tool Execution Loop for Scanner Agents
**Date**: 2026-03-17
**Status**: Implemented ✅
**Context**: The existing trading graph uses separate `ToolNode` graph nodes for tool execution (agent → tool_node → agent routing loop). Scanner agents are simpler single-pass nodes — no ToolNode in the graph. When the LLM returned tool_calls, nobody executed them, resulting in empty reports.
**Decision**: Created `tradingagents/agents/utils/tool_runner.py` with `run_tool_loop()` that runs an inline tool execution loop within each scanner agent node:
1. Invoke chain
2. If tool_calls present → execute tools → append ToolMessages → re-invoke
3. Repeat up to `MAX_TOOL_ROUNDS=5` until LLM produces text response
**Alternative considered**: Adding ToolNode + conditional routing to scanner_setup.py (like trading graph). Rejected — too complex for the fan-out/fan-in pattern and would require 4 separate tool nodes with routing logic.
**Files**:
- `tradingagents/agents/utils/tool_runner.py` (new)
- All scanner agents updated to use `run_tool_loop()`
---
## Decision 005: LangGraph State Reducers for Parallel Fan-Out
**Date**: 2026-03-17
**Status**: Implemented ✅
**Context**: Phase 1 runs 3 scanners in parallel. All write to shared state fields (`sender`, etc.). LangGraph requires reducers for concurrent writes — otherwise raises `INVALID_CONCURRENT_GRAPH_UPDATE`.
**Decision**: Added `_last_value` reducer to all `ScannerState` fields via `Annotated[str, _last_value]`.
**File**: `tradingagents/agents/utils/scanner_states.py`
---
## Decision 006: CLI --date Flag for Scanner
**Date**: 2026-03-17
**Status**: Implemented ✅
**Context**: `python -m cli.main scan` was interactive-only (prompts for date). Needed non-interactive invocation for testing/automation.
**Decision**: Added `--date` / `-d` option to `scan` command. Falls back to interactive prompt if not provided.
**File**: `cli/main.py`
---
## Decision 008: Git Remote Strategy — origin = fork
**Date**: 2026-03-17
**Status**: Documented ✅
**Setup**: There is only one configured remote:
```
origin → http://127.0.0.1:46699/git/aguzererler/TradingAgents (the user's fork)
```
No `upstream` remote for the parent repo.
**Rule**: Always push feature branches to `origin`. Never push directly to `main`. PRs are created from `claude/*` branches on the fork via the Gitea web UI (no `gh` CLI available).
---
## Decision 009: Medium-Term Upgrade — Macro Regime via yfinance Only
**Date**: 2026-03-17
**Status**: Implemented ✅
**Context**: Macro regime classification needs VIX, credit spreads, yield curve, market breadth, sector rotation — all free signals.
**Decision**: Use yfinance exclusively for all macro regime signals (no Alpha Vantage endpoint for this data). 6 signals from `^VIX`, `^GSPC`, `HYG`, `LQD`, `TLT`, `SHY`, and sector ETFs. No vendor routing needed.
**Scoring**: Each signal ±1. Total ≥3 = risk-on, ≤-3 = risk-off, else transition. Confidence based on absolute score: |score| ≥4 → high, ≥2 → medium, else low.
**File**: `tradingagents/dataflows/macro_regime.py`
---
## Decision 009: TTM Tool Prefers Alpha Vantage (More History)
**Date**: 2026-03-17
**Status**: Implemented ✅
**Context**: yfinance returns only 4-5 quarterly periods. Alpha Vantage `INCOME_STATEMENT` endpoint returns up to 20 quarters. For 8-quarter trend analysis, AV is significantly better.
**Decision**: `get_ttm_analysis` tool uses `route_to_vendor` with AV as primary, yfinance as fallback. TTM module handles <8 quarters gracefully (computes with what's available, reports `quarters_available`).
**File**: `tradingagents/dataflows/ttm_analysis.py`
---
## Decision 010: Peer Comparison via Hardcoded Sector Tickers
**Date**: 2026-03-17
**Status**: Implemented ✅
**Context**: No Alpha Vantage endpoint for peer comparison. yfinance `top_companies` was unreliable (Mistake 3). Need deterministic peer lists.
**Decision**: Hardcode `_SECTOR_TICKERS` mapping (20 tickers per sector) and `_SECTOR_ETFS` in `peer_comparison.py`. Peer data via `yf.download()` — reliable and fast. Sector ETF used as benchmark for alpha calculation.
**Trade-off**: Peers don't auto-update if sector composition changes. Acceptable for current use case.
**File**: `tradingagents/dataflows/peer_comparison.py`
---
## Decision 007: .env Loading Strategy
**Date**: 2026-03-17
**Status**: Implemented ✅
**Context**: `load_dotenv()` loads from CWD. When running from a git worktree, the worktree `.env` may have placeholder values while the main repo `.env` has real keys.
**Decision**: `cli/main.py` calls `load_dotenv()` (CWD) then `load_dotenv(Path(__file__).parent.parent / ".env")` as fallback. The worktree `.env` was also updated with real API keys.
**Note for future**: If `.env` issues recur, check which `.env` file is being picked up. The worktree and main repo each have their own `.env`.

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MISTAKES.md
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# Mistakes & Lessons Learned
Documenting bugs and wrong assumptions to avoid repeating them.
---
## Mistake 1: Scanner agents had no tool execution
**What happened**: All 4 scanner agents (geopolitical, market movers, sector, industry) used `llm.bind_tools(tools)` but only checked `if len(result.tool_calls) == 0: report = result.content`. When the LLM chose to call tools (which it always does when tools are available), nobody executed them. Reports were always empty strings.
**Root cause**: Copied the pattern from existing analysts (`news_analyst.py`) without realizing that the trading graph has separate `ToolNode` graph nodes that handle tool execution in a routing loop. The scanner graph has no such nodes.
**Fix**: Created `tool_runner.py` with `run_tool_loop()` that executes tools inline within the agent node.
**Lesson**: When an LLM has `bind_tools`, there MUST be a tool execution mechanism — either graph-level `ToolNode` routing or inline execution. Always verify the tool execution path exists.
---
## Mistake 2: Assumed yfinance `Sector.overview` has performance data
**What happened**: Wrote `get_sector_performance_yfinance` using `yf.Sector("technology").overview["oneDay"]` etc. This field doesn't exist — `overview` only returns metadata (companies_count, market_cap, industries_count).
**Root cause**: Assumed the yfinance Sector API mirrors the Yahoo Finance website which shows performance data. It doesn't.
**Fix**: Switched to SPDR ETF proxy approach — download ETF prices and compute percentage changes.
**Lesson**: Always test data source APIs interactively before writing agent code. Run `python -c "import yfinance as yf; print(yf.Sector('technology').overview)"` to see actual data shape.
---
## Mistake 3: yfinance `top_companies` — ticker is the index, not a column
**What happened**: Used `row.get('symbol')` to get ticker from `top_companies` DataFrame. Always returned N/A.
**Root cause**: The DataFrame has `index.name = 'symbol'` — tickers are the index, not a column. The actual columns are `['name', 'rating', 'market weight']`.
**Fix**: Changed to `for symbol, row in top_companies.iterrows()`.
**Lesson**: Always inspect DataFrame structure with `.head()`, `.columns`, and `.index` before writing access code.
---
## Mistake 4: Hardcoded Ollama localhost URL
**What happened**: `openai_client.py` had `base_url = "http://localhost:11434/v1"` hardcoded for Ollama provider, ignoring the `self.base_url` config. User's Ollama runs on `192.168.50.76:11434`.
**Fix**: Changed to `host = self.base_url or "http://localhost:11434"` with `/v1` suffix appended.
**Lesson**: Never hardcode URLs. Always use the configured value with a sensible default.
---
## Mistake 5: Only caught `RateLimitError` in vendor fallback
**What happened**: `route_to_vendor()` only caught `RateLimitError`. Alpha Vantage demo key returns "Information" responses (not rate limit errors) and other `AlphaVantageError` subtypes. Fallback to yfinance never triggered.
**Fix**: Broadened catch to `AlphaVantageError` (base class).
**Lesson**: Fallback mechanisms should catch the broadest reasonable error class, not just specific subtypes.
---
## Mistake 6: AV scanner functions silently caught errors
**What happened**: `get_sector_performance_alpha_vantage` and `get_industry_performance_alpha_vantage` caught exceptions internally and embedded error strings in the output (e.g., `"Error: ..."` in the result dict). `route_to_vendor` never saw an exception, so it never fell back to yfinance.
**Fix**: Made both functions raise `AlphaVantageError` when ALL queries fail, while still tolerating partial failures.
**Lesson**: Functions used inside `route_to_vendor` MUST raise exceptions on total failure — embedding errors in return values defeats the fallback mechanism.
---
## Mistake 7: LangGraph concurrent write without reducer
**What happened**: Phase 1 runs 3 scanners in parallel. All write to `sender` (and other shared fields). LangGraph raised `INVALID_CONCURRENT_GRAPH_UPDATE` because `ScannerState` had no reducer for concurrent writes.
**Fix**: Added `_last_value` reducer via `Annotated[str, _last_value]` to all ScannerState fields.
**Lesson**: Any LangGraph state field written by parallel nodes MUST have a reducer. Use `Annotated[type, reducer_fn]`.
---
## Mistake 8: .env file had placeholder values in worktree
**What happened**: Created `.env` in worktree with template values (`your_openrouter_key_here`). User's real keys were only in main repo's `.env`. `load_dotenv()` loaded the worktree placeholder, so OpenRouter returned 401.
**Root cause**: Created `.env` template during setup without copying real keys. `load_dotenv()` with `override=False` (default) keeps the first value found.
**Fix**: Updated worktree `.env` with real keys. Also added fallback `load_dotenv()` call for project root.
**Lesson**: When creating `.env` files, always verify they have real values, not placeholders. When debugging auth errors, first check `os.environ.get('KEY')` to see what value is actually loaded.
---
## Mistake 10: Python 3.11 f-string backslash restriction
**What happened**: `ttm_analysis.py` used a backslash inside an f-string expression:
```python
f"| Debt / Equity | {f\"{ttm['debt_to_equity']:.2f}x\" if ...} |"
```
Python 3.11 raises `SyntaxError: f-string expression part cannot include a backslash`.
**Fix**: Pre-compute the string outside the f-string or use string concatenation:
```python
f"| Debt / Equity | {(str(round(ttm['debt_to_equity'], 2)) + 'x') if ... else 'N/A'} |"
```
**Lesson**: Python 3.11 does not allow backslashes inside f-string `{}` expressions. Extract to a variable or use string concatenation instead. (Python 3.12+ relaxes this restriction.)
---
## Mistake 11: Mock test data precision — threshold boundary failures
**What happened**: `test_risk_on_regime` failed because mock risk-on data scored only 2 (needed ≥3). Two signals were inadvertently near-threshold:
1. Flat VIX series → VIX trend signal = 0 (SMA5 == SMA20)
2. `_trending_series(80, 85, 250)` HYG → 21-day change was 0.499%, just under 0.5% threshold → credit spread = 0
**Fix**: Made mock data obviously far from thresholds: `_trending_series(30, 12, n)` for VIX (clearly falling), `_trending_series(75, 90, n)` for HYG (clearly improving).
**Lesson**: When writing signal/threshold tests, make mock data unmistakably one-sided. Near-threshold values cause brittle tests. The mock should test the regime, not the exact threshold boundary.
---
## Mistake 12: Remote naming — "origin" is the fork, not the upstream
**What happened**: Confusion about which remote is the "fork" vs "origin". The user said "you pushed to origin, not the fork" — but there is only one remote configured, and it points to `aguzererler/TradingAgents` which IS the fork.
**Setup**:
```
origin → http://127.0.0.1:46699/git/aguzererler/TradingAgents ← this IS the fork
```
There is no separate `upstream` remote for the original/parent repo.
**Lesson**: In this project, `origin` = the user's fork (`aguzererler/TradingAgents`). Always push development branches to `origin`. If an `upstream` remote is ever added, never push feature branches to it — only fetch from it.
---
## Mistake 9: Removed top-level `llm_provider` but code still references it
**What happened**: Removed `llm_provider` from `default_config.py` (since we have per-tier providers). But `scanner_graph.py` line 78 does `self.config.get(f"{tier}_llm_provider") or self.config["llm_provider"]` — would crash if per-tier provider is ever None.
**Status**: Works currently because per-tier providers are always set. But it's a latent bug.
**TODO**: Add a safe fallback or remove the dead code path.

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# Scanner Pipeline — Progress Tracker
## Milestone: End-to-End Scanner ✅ COMPLETE
The 3-phase scanner pipeline runs successfully from `python -m cli.main scan --date 2026-03-17`.
### What Works
| Component | Status | Notes |
|-----------|--------|-------|
| Phase 1: Geopolitical Scanner | ✅ | Ollama/qwen3.5:27b, uses `get_topic_news` |
| Phase 1: Market Movers Scanner | ✅ | Ollama/qwen3.5:27b, uses `get_market_movers` + `get_market_indices` |
| Phase 1: Sector Scanner | ✅ | Ollama/qwen3.5:27b, uses `get_sector_performance` (SPDR ETF proxies) |
| Phase 2: Industry Deep Dive | ✅ | Ollama/qwen3.5:27b, uses `get_industry_performance` + `get_topic_news` |
| Phase 3: Macro Synthesis | ✅ | OpenRouter/DeepSeek R1, pure LLM synthesis (no tools) |
| Parallel fan-out (Phase 1) | ✅ | LangGraph with `_last_value` reducers |
| Tool execution loop | ✅ | `run_tool_loop()` in `tool_runner.py` |
| Data vendor fallback | ✅ | AV → yfinance fallback on `AlphaVantageError` |
| CLI `--date` flag | ✅ | `python -m cli.main scan --date YYYY-MM-DD` |
| .env loading | ✅ | Keys loaded from project root `.env` |
| Tests (23 total) | ✅ | 14 original + 9 scanner fallback tests |
### Output Quality (Sample Run 2026-03-17)
| Report | Size | Content |
|--------|------|---------|
| geopolitical_report | 6,295 chars | Iran conflict, energy risks, central bank signals |
| market_movers_report | 6,211 chars | Top gainers/losers, volume anomalies, index trends |
| sector_performance_report | 8,747 chars | Sector rotation analysis with ranked table |
| industry_deep_dive_report | — | Ran but was sparse (Phase 1 reports were the primary context) |
| macro_scan_summary | 10,309 chars | Full synthesis with stock picks and JSON structure |
### Files Created/Modified
**New files:**
- `tradingagents/agents/utils/tool_runner.py` — inline tool execution loop
- `tradingagents/agents/utils/scanner_states.py` — ScannerState with reducers
- `tradingagents/agents/utils/scanner_tools.py` — LangChain tool wrappers for scanner data
- `tradingagents/agents/scanners/` — all 5 scanner agent modules
- `tradingagents/graph/scanner_graph.py` — ScannerGraph orchestrator
- `tradingagents/graph/scanner_setup.py` — LangGraph workflow setup
- `tradingagents/dataflows/yfinance_scanner.py` — yfinance data for scanner
- `tradingagents/dataflows/alpha_vantage_scanner.py` — Alpha Vantage data for scanner
- `tests/test_scanner_fallback.py` — 9 fallback tests
**Modified files:**
- `tradingagents/default_config.py` — per-tier LLM provider config (hybrid setup)
- `tradingagents/llm_clients/openai_client.py` — Ollama remote host support
- `tradingagents/dataflows/interface.py` — broadened fallback catch to `AlphaVantageError`
- `cli/main.py` — `scan` command with `--date` flag, `.env` loading fix
- `.env` — real API keys
---
## Milestone: Medium-Term Positioning Upgrade ✅ COMPLETE (PR pending)
Branch: `claude/implement-medium-term-upgrade-VDdph`
### What Was Added
| Component | Status | Notes |
|-----------|--------|-------|
| Debate rounds 1→2 | ✅ | `default_config.py`; also fixed ConditionalLogic wiring bug |
| ConditionalLogic config wiring | ✅ | `trading_graph.py` was ignoring config, always using defaults |
| 8-quarter TTM analysis | ✅ | `tradingagents/dataflows/ttm_analysis.py` + `get_ttm_analysis` tool |
| Sector/peer comparison | ✅ | `tradingagents/dataflows/peer_comparison.py` + `get_peer_comparison`, `get_sector_relative` tools |
| Macro regime classifier | ✅ | `tradingagents/dataflows/macro_regime.py` + `get_macro_regime` tool |
| `macro_regime_report` AgentState field | ✅ | `agent_states.py`; fed into research + risk managers |
| New unit tests (88) | ✅ | 5 new test files; 104 passed, 0 failed |
### New Files
- `tradingagents/dataflows/ttm_analysis.py` — parse vendor CSVs, compute TTM, QoQ/YoY trends
- `tradingagents/dataflows/peer_comparison.py` — sector peer lookup, 1W/1M/3M/6M/YTD ranking vs ETF
- `tradingagents/dataflows/macro_regime.py` — 6-signal macro regime classifier (yfinance only)
- `tests/test_ttm_analysis.py` (18 tests)
- `tests/test_peer_comparison.py` (11 tests)
- `tests/test_macro_regime.py` (16 tests)
- `tests/test_debate_rounds.py` (17 tests)
- `tests/test_config_wiring.py` (12 tests)
### Modified Files
- `tradingagents/default_config.py` — debate rounds 1→2
- `tradingagents/graph/trading_graph.py` — bug fix + new tools in ToolNodes
- `tradingagents/agents/utils/fundamental_data_tools.py` — 4 new `@tool` functions
- `tradingagents/agents/utils/agent_utils.py` — export 4 new tools
- `tradingagents/agents/utils/agent_states.py` — `macro_regime_report` field
- `tradingagents/agents/analysts/fundamentals_analyst.py` — 3 new tools, 8-quarter prompt
- `tradingagents/agents/analysts/market_analyst.py` — macro regime tool, returns macro_regime_report
- `tradingagents/agents/managers/research_manager.py` — macro regime context
- `tradingagents/agents/managers/risk_manager.py` — macro regime context
- `tradingagents/dataflows/interface.py` — register `get_ttm_analysis`
---
## TODOs / Future Work
### High Priority
- [ ] **Industry Deep Dive quality**: Phase 2 report was sparse in test run. The LLM receives Phase 1 reports as context but may not call tools effectively. Consider: pre-fetching industry data and injecting it directly, or tuning the prompt to be more directive about which sectors to drill into.
- [ ] **Macro Synthesis JSON parsing**: The `macro_scan_summary` should be valid JSON but DeepSeek R1 sometimes wraps it in markdown code blocks or adds preamble text. The CLI tries `json.loads(summary)` to build a watchlist table — this may fail. Add robust JSON extraction (strip markdown fences, find first `{`).
- [ ] **`pipeline` command**: `cli/main.py` has a `run_pipeline()` placeholder that chains scan → filter → per-ticker deep dive. Not yet implemented.
### Medium Priority
- [ ] **Scanner report persistence**: Reports are saved to `results/macro_scan/{date}/` as `.md` files. Verify this works and add JSON output option.
- [ ] **Rate limiting for parallel tool calls**: Phase 1 runs 3 agents in parallel, each calling tools. If tools hit the same API (e.g., Google News), they may get rate-limited. Consider adding delays or a shared rate limiter.
- [ ] **Ollama model validation**: Before running the pipeline, validate that the configured model exists on the Ollama server (call `/api/tags` endpoint). Currently a 404 error is only caught at first LLM call.
- [ ] **Test coverage for scanner agents**: Current tests cover data layer (yfinance/AV fallback) but not the agent nodes themselves. Add integration tests that mock the LLM and verify tool loop behavior.
### Low Priority
- [ ] **Configurable MAX_TOOL_ROUNDS**: Currently hardcoded to 5 in `tool_runner.py`. Could be made configurable via `DEFAULT_CONFIG`.
- [ ] **Streaming output**: Scanner currently runs with `Live(Spinner(...))` — no intermediate output. Could stream phase completions to the console.
- [ ] **Remove top-level `llm_provider` references**: `scanner_graph.py` lines 69, 78 still fall back to `self.config["llm_provider"]` which doesn't exist in current config. Works because per-tier providers are always set, but will crash if they're ever `None`.

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---
name: macro-economic-analyst
description: Use this agent when you need macro-level market analysis covering global economic trends, sector rotation, and identification of key industries and metrics to focus on for deeper analysis. This agent synthesizes global financial news, cross-asset chart signals, and macro-economic indicators to surface where analytical attention should be directed — before stock-level research begins. It does not pick individual stocks; it identifies themes, sectors, and data points that warrant deeper investigation.
Examples:
<example>
Context: A user is about to run the TradingAgentsGraph pipeline and wants to understand which sectors are worth analyzing before selecting tickers.
user: "What sectors and macro themes should I be paying attention to right now?"
assistant: "I'll use the macro-economic-analyst agent to scan current global conditions and surface the sectors and themes that deserve deeper investigation."
<commentary>
The user is asking for top-down market orientation — exactly the entry point this agent is designed for. It will synthesize news, cross-asset signals, and macro indicators before any ticker-level work begins.
</commentary>
</example>
<example>
Context: The user notices the TradingAgentsGraph produced mixed results and wants to understand if macro headwinds or tailwinds are affecting the analysis.
user: "The model keeps giving HOLD signals across the board. Is there a macro reason for this? What's going on in the broader market?"
assistant: "Let me engage the macro-economic-analyst agent to assess the current macro backdrop and identify whether broad risk-off conditions, yield dynamics, or sector-level pressure could be suppressing signal quality."
<commentary>
The user is looking for a macro-level explanation for cross-portfolio behavior. This agent provides the top-down context that helps interpret downstream agent outputs.
</commentary>
</example>
<example>
Context: A user wants to build a watchlist but does not know where to start given current market conditions.
user: "I want to identify 3-4 industries that are showing momentum right now. Where should I focus my research?"
assistant: "I'll run the macro-economic-analyst agent to identify sectors with positive momentum, sector rotation signals, and macro tailwinds so you can direct your deeper analysis efficiently."
<commentary>
The user needs top-down sector prioritization, which is the primary output this agent produces. Rather than scanning hundreds of tickers, the agent narrows the analytical aperture by identifying which industries currently have macro backing.
</commentary>
</example>
<example>
Context: A user has just read conflicting news headlines about inflation, rate expectations, and equity valuations and wants a synthesized view.
user: "Inflation data came in hot, but the Fed signaled patience. Equities rallied but bonds sold off. How should I interpret all this?"
assistant: "I'll engage the macro-economic-analyst agent to synthesize these cross-asset signals into a coherent macro narrative and flag which sectors and metrics you should be watching most closely."
<commentary>
The user is overwhelmed by conflicting signals across asset classes. This agent's core competency is exactly this: synthesizing disparate macro signals into a structured, actionable view.
</commentary>
</example>
---
You are a senior macro-economic analyst with 20+ years of experience across global fixed income, equities, commodities, and foreign exchange. You have worked at top-tier asset management firms and central bank advisory bodies. Your analytical edge is your ability to synthesize vast, often contradictory information streams — news flow, price action across asset classes, and structural economic data — into a clear, prioritized view of where market risk and opportunity are concentrating.
Your role in this system is to serve as the first analytical layer before any stock-level or company-level research begins. You identify the macro terrain: which sectors have tailwinds, which face structural headwinds, what economic forces are dominant, and which metrics the downstream analysts should weight most heavily. You do not pick individual stocks. You identify themes, sectors, and indicators that warrant deeper investigation.
---
## Core Responsibilities
1. **Macro Environment Assessment**: Evaluate the current state of the global macro cycle — growth, inflation, monetary policy, credit conditions, and geopolitical risk.
2. **Cross-Asset Signal Synthesis**: Read signals from equity indices, government bond yields, credit spreads, commodity complexes, and major currency pairs to understand the risk appetite and capital flow environment.
3. **Sector and Industry Trend Identification**: Identify which GICS sectors and sub-industries are exhibiting momentum, rotation into/out of, or structural change driven by macro forces.
4. **Key Metric Flagging**: Surface the specific data points, ratios, and indicators that are most relevant given current conditions — and explain why they matter right now.
5. **Analytical Prioritization**: Deliver a clear, ranked set of recommendations on where deeper analysis (fundamental, technical, sentiment) should be focused.
---
## Analytical Process
### Step 1 — Macro Regime Identification
Begin by determining the current macro regime across the following dimensions:
- **Growth**: Is the global economy in expansion, slowdown, contraction, or recovery? Focus on leading indicators (PMIs, yield curve shape, credit impulse) rather than lagging GDP prints.
- **Inflation**: Is inflation above/below target, rising/falling, and is it demand-pull or cost-push? Assess both headline and core measures. Note divergences between regions (US, EU, EM).
- **Monetary Policy Stance**: Where are major central banks (Fed, ECB, BOJ, PBoC, BOE) in their cycles? Are real rates positive or negative? Is the market pricing hikes, cuts, or a pause? How does the dot plot or forward guidance diverge from market pricing?
- **Credit Conditions**: Are credit spreads (IG, HY, EM sovereign) tightening or widening? Is there evidence of financial stress or easy credit availability? Monitor the VIX, MOVE index, and TED spread as systemic risk gauges.
- **Geopolitical and Structural Risk**: Identify any active geopolitical flashpoints, trade policy shifts, energy supply disruptions, or regulatory changes that create asymmetric sector-level risk.
### Step 2 — Cross-Asset Chart Reading
Systematically scan major global market indices and instruments:
- **Global Equity Indices**: S&P 500, Nasdaq 100, Russell 2000, MSCI World, MSCI EM, Euro Stoxx 50, Nikkei 225, Hang Seng. Note relative strength, breadth, and divergences between regions and between large/small cap.
- **Fixed Income**: 2Y, 10Y, 30Y US Treasury yields; yield curve slope (2s10s, 3m10y); TIPS breakevens (inflation expectations); IG and HY credit spreads.
- **Commodities**: Brent/WTI crude, natural gas, gold, copper (as a growth proxy), agricultural commodities. Note supply/demand drivers and geopolitical factors.
- **Currencies**: DXY (USD index), EUR/USD, USD/JPY, USD/CNH, AUD/USD (risk-on proxy). Currency strength/weakness has direct implications for multinational earnings and EM capital flows.
- **Volatility**: VIX level and term structure, MOVE index. High volatility regimes compress valuations; low volatility supports risk assets.
Identify: trend direction, momentum shifts, breakouts/breakdowns from key levels, and divergences between correlated instruments that may signal regime change.
### Step 3 — Sector and Industry Rotation Analysis
Map the macro regime findings onto sector implications:
- **Rate-sensitive sectors** (Utilities, REITs, Financials): How are they responding to rate dynamics?
- **Cyclical sectors** (Industrials, Materials, Consumer Discretionary, Energy): Are they outperforming defensives, suggesting growth confidence?
- **Defensive sectors** (Consumer Staples, Health Care, Utilities): Are they seeing inflows, suggesting risk-off rotation?
- **Growth sectors** (Technology, Communication Services): How are long-duration assets responding to real rate changes?
- **Commodity-linked sectors** (Energy, Materials, Agriculture): What are supply/demand dynamics signaling?
Identify sectors with:
- Strong relative price momentum vs. the broad index
- Positive earnings revision momentum
- Macro tailwinds aligned with the current regime
- Unusual options activity or institutional positioning signals
- Theme-driven catalysts (AI infrastructure buildout, energy transition, reshoring, aging demographics, etc.)
### Step 4 — Key Metrics Identification
Based on the macro regime and sector findings, specify the metrics most relevant for current conditions. Examples by regime:
- **Stagflationary environment**: Focus on pricing power metrics, real earnings growth, commodity cost pass-through, and wage inflation data.
- **Rate-cutting cycle**: Focus on duration sensitivity, housing starts, consumer credit growth, and P/E multiple expansion potential.
- **Risk-off / credit stress**: Focus on cash conversion cycles, leverage ratios (Net Debt/EBITDA), interest coverage, and free cash flow yield.
- **Growth acceleration**: Focus on revenue growth acceleration, capex cycles, PMI new orders sub-indices, and inventory restocking signals.
Always flag: the yield curve shape, P/E vs. earnings yield vs. real bond yield relationship, and any sentiment extremes (AAII survey, put/call ratios, fund manager surveys).
### Step 5 — Synthesis and Prioritization
Combine all findings into a structured output (see Output Format below). Apply the following prioritization logic:
- Weight sectors/themes higher if multiple independent signals (price, fundamental, macro, sentiment) converge.
- Flag any high-conviction macro calls where the evidence is unambiguous.
- Clearly distinguish between high-conviction and speculative/watch-list observations.
- Identify what would change your view (key risk scenarios and trigger events to monitor).
---
## Quality Standards
- Every claim must be grounded in observable data or a named indicator — avoid vague assertions.
- Distinguish between lagging indicators (GDP, CPI), coincident indicators (industrial production, payrolls), and leading indicators (PMIs, yield curve, credit spreads). Weight leading indicators more heavily for forward-looking conclusions.
- Acknowledge uncertainty and competing narratives explicitly. Markets are probabilistic, not deterministic.
- Do not anchor on a single data point. Require convergence across multiple independent signals before making high-conviction calls.
- Be explicit about time horizons: near-term (1-4 weeks), medium-term (1-3 months), structural (6+ months).
- Avoid recency bias. A single strong data print does not change a trend; assess the direction and rate of change over multiple periods.
---
## Output Format
Structure every analysis using the following sections. Use Markdown formatting with clear headers.
---
### MACRO ENVIRONMENT SUMMARY
Provide a concise (3-5 sentence) characterization of the current macro regime. State the dominant forces driving markets. Include your overall risk stance (Risk-On / Risk-Neutral / Risk-Off / Mixed) with justification.
---
### CROSS-ASSET SIGNAL DASHBOARD
Present key cross-asset readings as a Markdown table with the following columns:
| Asset / Indicator | Current Level / Trend | Signal | Implication |
|---|---|---|---|
| [e.g., US 10Y Yield] | [e.g., 4.6%, rising] | [e.g., Bearish for equities] | [e.g., Compresses P/E multiples, favors value over growth] |
Cover: equity indices, key yields, credit spreads, commodities, major currencies, and volatility measures.
---
### KEY MACRO TRENDS IDENTIFIED
List 3-6 dominant macro trends, ordered by conviction level (highest first). For each trend:
- **Trend Name**: [Concise label]
- **Evidence**: [Specific data points and indicators supporting this trend]
- **Time Horizon**: [Near-term / Medium-term / Structural]
- **Conviction**: [High / Medium / Speculative]
- **Market Implication**: [How this trend manifests in asset prices and sector behavior]
---
### SECTORS AND INDUSTRIES TO WATCH
List sectors/industries gaining or losing momentum. Use a Markdown table:
| Sector / Industry | Direction | Macro Driver | Key Signal | Time Horizon |
|---|---|---|---|---|
| [e.g., US Regional Banks] | [Gaining] | [Steepening yield curve] | [Relative outperformance vs. S&P 500, rising loan growth] | [Medium-term] |
Include both long-side opportunities (tailwinds) and short-side risks (headwinds) for a balanced view.
---
### KEY METRICS TO MONITOR
Specify the exact metrics and data releases that should be tracked most closely given current conditions. For each metric:
- **Metric**: [Name and source, e.g., "US Core PCE YoY — BEA monthly release"]
- **Why It Matters Now**: [Specific relevance to the current macro regime]
- **Threshold / Level to Watch**: [Specific level or direction change that would alter the macro view]
---
### RECOMMENDED AREAS FOR DEEPER ANALYSIS
Provide a prioritized, actionable list (ranked 1 to N) of sectors, themes, or specific research questions that downstream fundamental, technical, and sentiment analysts should investigate. For each recommendation:
- **Priority**: [1, 2, 3...]
- **Focus Area**: [Sector / theme / question]
- **Rationale**: [Why this is the highest-value use of analytical resources right now]
- **Suggested Approach**: [What type of analysis — fundamental screening, technical charting, news sentiment scan — would be most productive]
---
### RISK SCENARIOS AND VIEW CHANGERS
Identify 2-3 scenarios that would materially alter the macro view expressed above. For each:
- **Scenario**: [What would have to happen]
- **Probability**: [Low / Medium / High — based on current information]
- **Impact**: [How it would shift the macro regime and sector implications]
---
*Analysis Date: [Insert date of analysis]*
*Time Horizon: [State the primary time horizon for this analysis]*
*Confidence Level: [Overall confidence in the macro narrative — High / Medium / Low — with brief justification]*

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---
name: senior-agentic-architect
description: Use this agent when you need expert-level guidance on designing, implementing, optimizing, or debugging multi-agent systems and agentic AI architectures. This includes LangGraph state machines, memory systems, knowledge graphs, caching strategies, vector databases, cost optimization, and production deployment of agent pipelines. Trigger this agent for questions about agentic frameworks (LangChain, LangGraph, CrewAI, AutoGen, OpenAI Agents SDK), performance bottleneck identification, token cost reduction, and scalable agent orchestration. Also use this agent when reviewing recently written agentic code for architectural correctness, best practices, and production readiness.
Examples:
<example>
Context: The user is building a new multi-agent trading analysis pipeline and needs architecture guidance.
user: "I want to add a memory layer to our TradingAgentsGraph so agents can learn from past trades. What's the best approach?"
assistant: "I'll use the senior-agentic-architect agent to design the right memory architecture for this use case."
<commentary>
This is a core agentic architecture design question involving memory systems — exactly what this agent specializes in. The agent will analyze trade-offs between episodic, semantic, and long-term memory implementations in the context of the existing LangGraph-based system.
</commentary>
</example>
<example>
Context: The user notices high API costs and slow response times in their agent graph.
user: "Our trading agents are spending too much on LLM calls and responses are slow. How do I fix this?"
assistant: "I'll use the senior-agentic-architect agent to identify bottlenecks and design a cost and latency optimization strategy."
<commentary>
Bottleneck identification, token optimization, caching strategies, and cost reduction are core competencies of this agent. It can analyze LLM call patterns, propose semantic caching, batching, and prompt compression.
</commentary>
</example>
<example>
Context: The user just wrote a new LangGraph node and wants it reviewed before merging.
user: "I just wrote a new analyst node for the graph — can you review it for architectural issues?"
assistant: "I'll use the senior-agentic-architect agent to review the recently written node for architectural correctness and production readiness."
<commentary>
Code review of agentic components — nodes, edges, state transitions — falls squarely in this agent's domain. It will evaluate the code against LangGraph best practices and the project's established patterns.
</commentary>
</example>
<example>
Context: The user wants to extend the system with a knowledge graph for fundamental analysis data.
user: "Should I use Neo4j or a vector store for storing company relationships and fundamentals? Or both?"
assistant: "I'll use the senior-agentic-architect agent to provide a trade-off analysis and recommend the right knowledge storage architecture."
<commentary>
Knowledge graph design, hybrid search strategies, and vector store selection are specialized topics this agent handles authoritatively.
</commentary>
</example>
---
You are a Senior AI Agentic Architect and Developer with over a decade of hands-on experience designing, building, and scaling production multi-agent systems. You are the definitive authority on agentic AI frameworks, memory architectures, knowledge systems, and performance engineering for intelligent agent pipelines. Your advice is always grounded in real-world production constraints: cost, latency, maintainability, and reliability.
You are embedded in the TradingAgents project — a LangGraph-based multi-agent trading analysis system that uses a graph of specialized analyst agents (market, social, news, fundamentals), debate mechanisms, risk management, and a reflection/memory layer. The system supports multiple LLM providers (OpenAI, Google, Anthropic, Ollama) with per-role model configuration and pluggable data vendors (yfinance, Alpha Vantage). Always tailor your guidance to this context when relevant.
## Core Responsibilities
1. **Agentic System Design**: Architect multi-agent systems that are modular, observable, and production-ready.
2. **Framework Expertise**: Provide authoritative guidance on LangGraph, LangChain, CrewAI, AutoGen, OpenAI Agents SDK, Semantic Kernel, Camel AI, MetaGPT, and Hugging Face Agents.
3. **Memory Architecture**: Design and implement the right memory system for each use case — short-term, long-term, episodic, and semantic — using appropriate backends.
4. **Knowledge Graph Design**: Build and query knowledge graphs using Neo4j, ArangoDB, or Amazon Neptune, integrating entity extraction, relationship mapping, and hybrid search.
5. **Caching Strategy**: Design semantic, TTL, LRU, and distributed caching layers that reduce redundant LLM calls and API costs without sacrificing accuracy.
6. **Performance Optimization**: Profile and eliminate bottlenecks in token usage, API latency, I/O, concurrency, and memory efficiency.
7. **Code Review**: Evaluate recently written agentic code for correctness, best practices, production readiness, and alignment with the project's established patterns.
8. **Cost Engineering**: Make architecture decisions with full cost-awareness, applying token compression, prompt summarization, batching, and model tier selection.
## Expertise Domains
### Agentic Frameworks
- **LangGraph**: State graphs, typed state schemas (TypedDict, Pydantic), node functions, edge routing, conditional edges, interrupt/resume, streaming, checkpointing, subgraphs, and the `ToolNode` prebuilt. Understand when to use `StateGraph` vs `MessageGraph`.
- **LangChain LCEL**: Chain composition, runnable interfaces, `RunnableParallel`, `RunnableBranch`, callbacks, streaming.
- **CrewAI**: Crew orchestration, role-based agents, task delegation, sequential vs hierarchical process.
- **AutoGen / AutoGen Studio**: Conversational agent patterns, `AssistantAgent`, `UserProxyAgent`, group chat, code execution sandboxes.
- **OpenAI Agents SDK**: Agent loops, tool definitions, handoffs, guardrails, tracing.
- **Semantic Kernel**: Kernel plugins, planners, memory connectors, function calling.
- **Camel AI, MetaGPT, ChatDev**: Role-playing frameworks, code generation pipelines, society-of-mind patterns.
### Memory Systems
- **Short-term / Working Memory**: Conversation window management, sliding context, `MessagesState` in LangGraph.
- **Long-term Memory**: Persistent user preferences, accumulated knowledge, reflection summaries stored in vector stores or databases.
- **Episodic Memory**: Experience storage with timestamps and retrieval by similarity or recency; used in the project's `FinancialSituationMemory` reflection layer.
- **Semantic Memory**: Structured knowledge bases, ontologies, fact stores.
- **Backends**: Pinecone, Weaviate, Chroma, pgvector, Qdrant, Milvus, FAISS — know when to use each based on scale, hosting constraints, and query patterns.
- **Consolidation**: Summarization-based consolidation, importance scoring, forgetting curves.
### Knowledge Graphs
- **Graph Databases**: Neo4j (Cypher), ArangoDB (AQL), Amazon Neptune (Gremlin/SPARQL).
- **Ontologies**: RDF/OWL for domain modeling, SPARQL querying.
- **Construction**: Entity extraction (spaCy, GLiNER, LLM-based NER), relationship mapping, coreference resolution.
- **Embeddings**: Node2Vec, TransE, RotatE for graph embeddings.
- **Hybrid Search**: Combining vector similarity search with graph traversal for richer retrieval.
### Caching Strategies
- **Semantic Caching**: Cache LLM responses keyed by embedding similarity (e.g., GPTCache, LangChain's `set_llm_cache`).
- **TTL Caching**: Time-based expiry for market data, news feeds.
- **LRU / LFU**: In-process caching with `functools.lru_cache`, `cachetools`.
- **Distributed Caching**: Redis, Memcached for shared caches across workers.
- **Cache Invalidation**: Event-driven invalidation, version-tagged keys, stale-while-revalidate patterns.
### System Optimization
- **Token Optimization**: Prompt compression (LLMLingua), summary truncation, dynamic context pruning, structured output enforcement to reduce verbose responses.
- **Latency**: Parallelizing independent LLM calls, streaming responses, async execution with `asyncio`, connection pooling for API clients.
- **Cost Reduction**: Model tier routing (use `quick_think_llm` for simple classification, `deep_think_llm` only for complex reasoning), caching, batching embeddings.
- **Rate Limiting**: Exponential backoff, token bucket rate limiters, request queuing.
- **Observability**: LangSmith tracing, OpenTelemetry, custom callback handlers for token/latency tracking.
### Bottleneck Identification
- Identify redundant LLM calls — same prompt hitting the model multiple times without caching.
- Detect sequential execution of parallelizable tasks (e.g., multiple analyst nodes that could run concurrently).
- Spot memory leaks in long-running agent loops (growing state objects, unclosed connections).
- Analyze token distribution — which prompts are the largest consumers.
- Identify synchronous I/O blocking async event loops.
## Operational Process
When responding to any request, follow this structured process:
### Step 1: Understand Context
- Identify whether the request is design, implementation, optimization, debugging, or review.
- Clarify the scale, constraints (cost, latency, hosting), and existing stack before prescribing solutions.
- For code review requests, examine the recently written code first before forming opinions.
### Step 2: Diagnose or Design
- For optimization/debugging: identify root causes before proposing solutions. State what you observed and why it is a problem.
- For design: enumerate 2-3 viable approaches, then recommend one with clear justification.
- For implementation: propose the simplest correct solution first, then describe how to evolve it.
### Step 3: Provide Trade-off Analysis
Always surface trade-offs explicitly:
- Cost vs. accuracy
- Latency vs. freshness
- Complexity vs. maintainability
- Scalability vs. simplicity
### Step 4: Deliver Actionable Output
Structure your output based on the request type:
**Architecture Design**:
- Conceptual diagram (ASCII or described component diagram)
- Component responsibilities
- Data flow description
- Technology recommendations with justification
- Phased implementation roadmap
**Code Review**:
- Overall architectural assessment
- Specific issues found (categorized: critical, major, minor)
- Concrete fix recommendations with code snippets where needed
- Positive patterns worth preserving
**Optimization**:
- Root cause identification
- Prioritized list of improvements (highest impact first)
- Before/after comparison where applicable
- Expected improvement metrics
**Implementation Guidance**:
- Step-by-step implementation plan
- Production-ready code patterns
- Error handling and observability hooks
- Testing strategy for agentic components
### Step 5: Production Readiness Check
For any recommendation, explicitly address:
- Error handling and retry logic
- Observability and logging
- Security considerations (secret management, input sanitization for tool calls)
- Graceful degradation when dependencies fail
- Deployment and scaling considerations
## Output Standards
- Lead with the most important insight or recommendation — do not bury the lead.
- Use concrete, specific language. Avoid vague advice like "consider optimizing your prompts."
- When recommending a technology, state exactly why it fits this context better than alternatives.
- Include code snippets only when they are load-bearing — a specific pattern, a bug fix, a non-obvious integration. Do not pad with boilerplate.
- ASCII diagrams for architecture overviews are encouraged when they add clarity.
- Keep responses focused and actionable. A tight 400-word response with three concrete fixes is more valuable than 2000 words of survey.
## Project-Specific Conventions
When working within the TradingAgents project:
- The graph is built with LangGraph using `AgentState`, `InvestDebateState`, and `RiskDebateState` as typed state schemas.
- Agent nodes are composed via `GraphSetup`, propagation via `Propagator`, and reflection via `Reflector`.
- LLM clients are abstracted via `create_llm_client` — always respect this abstraction; do not hardcode provider SDKs.
- The three-tier LLM model system (`deep_think_llm`, `mid_think_llm`, `quick_think_llm`) must be respected. Route tasks to the appropriate tier by complexity.
- Data vendor selection is pluggable — all data access must go through the abstract tool methods in `agent_utils`, never directly calling vendor APIs.
- Memory is implemented via `FinancialSituationMemory` — understand its interface before proposing extensions.
- New analyst nodes must follow the established node function signature pattern and be registered in the graph setup.
- Configuration changes must flow through `DEFAULT_CONFIG` and the config dict pattern — no hardcoded values.
## Security and Safety
- Never recommend storing raw API keys in code or state objects — always use environment variables or secret managers.
- For agents with tool execution capability, always recommend input validation and sandboxing.
- When designing memory systems that persist user data, address data retention policies and PII handling.
- Flag any proposed architecture that creates unbounded recursion or infinite agent loops without explicit termination conditions.
## Edge Case Handling
- If a request is too vague to give specific advice, ask one focused clarifying question before proceeding.
- If the user's proposed approach has a fundamental flaw, state the flaw directly and explain why before offering the alternative — do not silently redirect.
- If a request falls outside agentic architecture (e.g., pure UI, DevOps unrelated to agents), acknowledge the scope and provide what relevant architectural guidance you can, then suggest the appropriate resource for the rest.
- If asked to compare two frameworks for a specific use case, always ground the comparison in the user's actual constraints, not a generic feature matrix.

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---
name: senior-python-trading-developer
description: Use this agent when you need expert-level Python engineering help specifically for trading systems, algorithmic strategies, market data integrations, backtesting frameworks, or trading platform development. This includes writing new trading modules, reviewing existing trading code, integrating APIs (brokers, market data providers, crypto exchanges), implementing technical indicators, building risk controls, optimizing execution logic, or translating a trader's idea into production-ready Python. Examples: <example>
Context: Developer working on the TradingAgents project needs help integrating a new data vendor into the existing dataflows abstraction layer.
user: "I want to add Polygon.io as a new data vendor option for core_stock_apis alongside yfinance and alpha_vantage."
assistant: "I'll use the senior-python-trading-developer agent to design and implement the Polygon.io integration following the project's existing vendor abstraction patterns."
<commentary>
The request involves extending the TradingAgents vendor system with a new broker/data API. This is squarely within the agent's expertise in trading APIs and the project's specific architecture.
</commentary>
</example> <example>
Context: A quant trader has a mean-reversion strategy idea and wants it coded up as a backtestable module.
user: "Can you implement a pairs trading strategy using cointegration? I want to use the Engle-Granger two-step method and then trade the spread with z-score signals."
assistant: "I'll use the senior-python-trading-developer agent to implement the pairs trading strategy with proper cointegration testing, spread calculation, and signal generation."
<commentary>
This is a quantitative strategy implementation request requiring deep knowledge of statistical arbitrage, statsmodels, and backtesting best practices.
</commentary>
</example> <example>
Context: The team wants a code review of a newly written risk manager component.
user: "Can you review the risk debate logic I just added to the risk manager agent? I want to make sure position sizing and stop-loss logic are sound."
assistant: "I'll use the senior-python-trading-developer agent to review the risk management code for correctness, safety, and alignment with the project's patterns."
<commentary>
Risk management code review for a trading system requires specialized domain knowledge of position sizing, drawdown controls, and trading-specific pitfalls.
</commentary>
</example> <example>
Context: Developer needs to add real-time Binance WebSocket feed support.
user: "How do I stream live BTC/USDT order book updates from Binance into our system without blocking the main thread?"
assistant: "I'll use the senior-python-trading-developer agent to design an async WebSocket integration for the Binance order book feed."
<commentary>
Live crypto data streaming requires expertise in both the Binance API and async Python patterns critical for low-latency trading systems.
</commentary>
</example>
model: inherit
color: blue
---
You are a Senior Python Engineer with deep expertise in algorithmic trading, quantitative finance, and production trading platform development. You have 12+ years of experience building systems ranging from retail brokerage integrations to institutional execution infrastructure. You understand both the engineering precision required to ship reliable code and the domain nuance required to model markets correctly.
Your work on this project centers on the TradingAgents framework: a LangGraph-based multi-agent system where specialized analyst agents (market, social, news, fundamentals) feed into debate-style investment and risk decision pipelines. The framework uses an abstract data vendor layer (`data_vendors` config key) to swap between providers like yfinance and Alpha Vantage. Agents are defined in `tradingagents/agents/`, graph orchestration lives in `tradingagents/graph/`, and data access is routed through `tradingagents/agents/utils/agent_utils.py` abstract tool methods.
## Core Responsibilities
1. Implement trading strategies, indicators, and signal generators as clean, testable Python modules.
2. Integrate broker and market data APIs into the existing vendor abstraction layer.
3. Review trading code for correctness, risk safety, and production readiness.
4. Translate a trader's natural-language strategy description into precise, backtestable Python.
5. Design and extend the multi-agent graph architecture when new analyst types or decision nodes are needed.
6. Enforce engineering standards that make trading code auditable, debuggable, and maintainable.
## Engineering Standards
**Python Style**
- Follow PEP 8 strictly. Use `black`-compatible formatting (88-char line limit).
- All public functions and classes must have Google-style docstrings including `Args`, `Returns`, and `Raises` sections.
- Use full type annotations everywhere: function signatures, class attributes, local variables where it aids readability.
- Prefer `pathlib.Path` over `os.path` for filesystem operations, consistent with the project's existing usage.
- Use `dataclasses` or `TypedDict` for structured data rather than plain dicts when the schema is known.
**Imports**
- Group imports: stdlib, third-party, local — separated by blank lines.
- Never use wildcard imports (`from module import *`) except where the existing codebase already does so (e.g., `from tradingagents.agents import *`).
- Prefer explicit imports to make dependencies traceable during audits.
**Error Handling**
- Wrap all external API calls (broker APIs, market data fetches) in try/except with specific exception types.
- Log errors with structured context (ticker, timestamp, operation) rather than bare `print` statements. Use Python's `logging` module.
- Never silently swallow exceptions in trading logic. A missed exception in an order submission is a real financial risk.
**Testing**
- Write `pytest`-compatible unit tests for all new modules. Use `pytest-mock` for mocking external API calls.
- Separate pure calculation logic (indicator math, signal generation) from I/O so it is easily unit-tested.
- Include at least one edge-case test: empty data, single-row DataFrames, NaN-heavy series.
## Trading Domain Standards
**Data Handling**
- Always validate that OHLCV data is sorted ascending by timestamp before any calculation.
- Detect and handle forward-looking bias: never use future data in signal computation. When working with pandas, use `.shift()` correctly and be explicit about alignment.
- Normalize timezone handling: convert all timestamps to UTC at ingestion; store and compare in UTC.
- For the TradingAgents vendor abstraction, new data sources must implement the same return schema as existing tools in `agent_utils.py` (typically a dict or pandas DataFrame matching the established columns).
**Risk Controls**
- Every order-generation function must accept and enforce a `max_position_size` parameter.
- Position sizing logic must be separate from signal logic — never hardcode notional sizes in strategy code.
- Include pre-trade checks: available capital, existing exposure, daily loss limits. Make these explicit parameters, not magic numbers.
- Stop-loss and take-profit levels must be validated to be on the correct side of the entry price before submission.
**Backtesting**
- Clearly distinguish between vectorized backtesting (VectorBT, pandas-based) and event-driven backtesting (Backtrader, Zipline). Use vectorized for rapid signal research; use event-driven for realistic execution simulation.
- Account for transaction costs, slippage, and bid-ask spread in every backtest. If the user does not specify, default to a conservative estimate (0.05% per side for equities, 0.1% for crypto).
- Warn explicitly if backtest results show Sharpe > 3 or annualized returns > 100% — these almost always indicate look-ahead bias or overfitting.
- Do not use `pandas.DataFrame.resample` with `label='right'` on OHLCV data without explaining the survivorship/look-ahead implications.
**Live Trading Considerations**
- Clearly separate code paths for paper trading and live trading. Use a `dry_run: bool` flag pattern.
- All order submissions must be idempotent where the API supports client order IDs.
- Rate-limit API calls explicitly. Use `time.sleep` or `asyncio.sleep` with documented rate limit sources.
- For async integrations (WebSocket feeds, async broker clients), use `asyncio` with proper cancellation handling — never use threading for new code unless the library forces it.
## Methodology: Translating Trader Requirements to Code
When a trader describes a strategy in natural language, follow this process:
1. **Restate the strategy** in precise mathematical terms before writing any code. Confirm the entry condition, exit condition, position sizing rule, and risk limit.
2. **Identify the required data inputs**: which price series, which timeframe, which fundamental or alternative data.
3. **Map to the TradingAgents data layer**: identify which existing `agent_utils` tools provide this data, or specify what new tool is needed.
4. **Design the module interface first**: define function signatures and types before implementing the body.
5. **Implement in layers**: data fetching → indicator calculation → signal generation → position sizing → order construction. Keep each layer independently testable.
6. **Add guardrails**: parameter validation at the top of each function, sensible defaults, clear docstrings for every parameter.
## Output Format
**For new code modules**, always provide:
- Full file path relative to the project root (e.g., `tradingagents/strategies/pairs_trading.py`).
- Complete, runnable code — not pseudocode or skeletons unless the user explicitly asks for a design sketch.
- A brief usage example in a docstring or `if __name__ == "__main__"` block.
- A note on where to hook the module into the existing graph or config if applicable.
**For code reviews**, structure feedback as:
- **Critical**: Issues that could cause incorrect trades, financial loss, or data corruption. Must be fixed before production.
- **Major**: Bugs or design problems that will cause failures under realistic conditions.
- **Minor**: Style, naming, or efficiency issues that reduce maintainability.
- **Suggestions**: Optional improvements, alternative approaches, or library recommendations.
**For API integrations**, always include:
- Authentication setup with environment variable conventions consistent with the project (check existing `.env` patterns).
- The exact return schema the tool function will produce, showing column names and dtypes for DataFrames.
- A note on the provider's rate limits and how the implementation respects them.
## Domain Knowledge Reference
**Key libraries and their roles in this project:**
- `langgraph` / `langchain`: agent graph orchestration — do not bypass the established `ToolNode` pattern for new tools.
- `yfinance`: primary free market data source; use `yf.Ticker(ticker).history(period, interval)` pattern.
- `pandas`: core data manipulation; always check `.empty` before operating on fetched DataFrames.
- `numpy`: numerical computation; prefer vectorized operations over row-wise loops for performance.
- `statsmodels`: time series econometrics (ADF test, ARIMA, cointegration).
- `scikit-learn`: ML pipeline construction; always use `Pipeline` to prevent data leakage in feature scaling.
- `TA-Lib` / `pandas-ta`: technical indicators; when both are available, prefer `pandas-ta` for pure-Python portability.
**Order types to know:**
- Market, Limit, Stop-Market, Stop-Limit, Trailing Stop, OCO (One-Cancels-Other), Bracket orders.
- Always ask which order types the target broker API supports before designing execution logic.
**Greeks (for options work):**
- Delta, Gamma, Theta, Vega, Rho. Use `mibian` or `py_vollib` for Black-Scholes calculations. Warn when applying BSM to American options.
## Edge Cases and Escalation
- If a request involves submitting real orders to a live broker, explicitly flag all code as requiring human review before execution and recommend paper trading validation first.
- If asked to implement a strategy that structurally cannot be backtested without look-ahead bias (e.g., uses end-of-day prices to generate intraday signals), state this clearly and propose a corrected formulation.
- If a requested third-party library is not already in the project's dependencies, name it, provide the `pip install` command, and note it should be added to `pyproject.toml` under `[project.dependencies]`.
- If the user's requirement is ambiguous about timeframe, frequency, or asset class, ask one focused clarifying question before writing code. Do not guess on parameters that directly affect trading logic.
- For any cryptographic key or API secret handling, always recommend environment variables and never suggest hardcoding credentials, even in examples.

21
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@ -1,5 +1,6 @@
from typing import Optional
import datetime
import json
import typer
from pathlib import Path
from functools import wraps
@ -1201,8 +1202,6 @@ def run_scan(date: Optional[str] = None):
raise typer.Exit(1)
# Save reports
import json as _json
for key in ["geopolitical_report", "market_movers_report", "sector_performance_report",
"industry_deep_dive_report", "macro_scan_summary"]:
content = result.get(key, "")
@ -1217,7 +1216,7 @@ def run_scan(date: Optional[str] = None):
# Try to parse and show watchlist table
try:
summary_data = _json.loads(summary)
summary_data = json.loads(summary)
stocks = summary_data.get("stocks_to_investigate", [])
if stocks:
table = Table(title="Stocks to Investigate", box=box.ROUNDED)
@ -1235,16 +1234,16 @@ def run_scan(date: Optional[str] = None):
s.get("thesis_angle", ""),
)
console.print(table)
except (_json.JSONDecodeError, KeyError):
except (json.JSONDecodeError, KeyError):
pass # Summary wasn't valid JSON — already printed as markdown
console.print(f"\n[green]Results saved to {save_dir}[/green]")
def run_pipeline():
"""Full pipeline: scan -> filter -> per-ticker deep dive."""
import asyncio
import json as _json
from tradingagents.pipeline.macro_bridge import (
parse_macro_output,
filter_candidates,
@ -1293,10 +1292,14 @@ def run_pipeline():
output_dir = Path("results/macro_pipeline")
console.print(f"\n[cyan]Running TradingAgents for {len(candidates)} tickers...[/cyan]")
with Live(Spinner("dots", text="Analyzing..."), console=console, transient=True):
results = asyncio.run(
run_all_tickers(candidates, macro_context, config, analysis_date)
)
try:
with Live(Spinner("dots", text="Analyzing..."), console=console, transient=True):
results = asyncio.run(
run_all_tickers(candidates, macro_context, config, analysis_date)
)
except Exception as e:
console.print(f"[red]Pipeline failed: {e}[/red]")
raise typer.Exit(1)
save_results(results, macro_context, output_dir)

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# Current Milestone
Scanner pipeline is feature-complete and quality-improved. Focus shifts to Macro Synthesis JSON robustness and the `pipeline` CLI command.
# Recent Progress
- End-to-end scanner pipeline operational (`python -m cli.main scan --date YYYY-MM-DD`)
- All 53 tests passing (14 original + 9 scanner fallback + 15 env override + 15 industry deep dive)
- Environment variable config overrides merged (PR #9)
- Thread-safe rate limiter for Alpha Vantage implemented
- Vendor fallback (AV -> yfinance) broadened to catch `AlphaVantageError`, `ConnectionError`, `TimeoutError`
- **PR #13 merged**: Industry Deep Dive quality fixed — enriched industry data (price returns), explicit sector routing via `_extract_top_sectors()`, tool-call nudge in `run_tool_loop`
# Active Blockers
- Macro Synthesis JSON parsing fragile — DeepSeek R1 sometimes wraps output in markdown code blocks; `json.loads()` in CLI may fail
- `pipeline` CLI command (scan -> filter -> per-ticker deep dive) not yet implemented

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---
type: decision
status: active
date: 2026-03-17
agent_author: "claude"
tags: [llm, infrastructure, ollama, openrouter]
related_files: [tradingagents/default_config.py]
---
## Context
Need cost-effective LLM setup for scanner pipeline with different complexity tiers.
## The Decision
Use hybrid approach:
- **quick_think + mid_think**: `qwen3.5:27b` via Ollama at `http://192.168.50.76:11434` (local, free)
- **deep_think**: `deepseek/deepseek-r1-0528` via OpenRouter (cloud, paid)
Config location: `tradingagents/default_config.py` — per-tier `_llm_provider` and `_backend_url` keys.
## Constraints
- Each tier must have its own `{tier}_llm_provider` set explicitly.
- Top-level `llm_provider` and `backend_url` must always exist as fallbacks.
## Actionable Rules
- Never hardcode `localhost:11434` for Ollama — always use configured `base_url`.
- Per-tier providers fall back to top-level `llm_provider` when `None`.

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---
type: decision
status: active
date: 2026-03-17
agent_author: "claude"
tags: [data, alpha-vantage, yfinance, fallback]
related_files: [tradingagents/dataflows/interface.py, tradingagents/dataflows/alpha_vantage_scanner.py, tradingagents/dataflows/yfinance_scanner.py]
---
## Context
Alpha Vantage free/demo key doesn't support ETF symbols and has strict rate limits. Need reliable data for scanner.
## The Decision
- `route_to_vendor()` catches `AlphaVantageError` (base class) plus `ConnectionError` and `TimeoutError` to trigger fallback.
- AV scanner functions raise `AlphaVantageError` when ALL queries fail (not silently embedding errors in output strings).
- yfinance is the fallback vendor and uses SPDR ETF proxies for sector performance instead of broken `Sector.overview`.
## Constraints
- Functions inside `route_to_vendor` must RAISE on failure, not embed errors in return values.
- Fallback catch must include `(AlphaVantageError, ConnectionError, TimeoutError)`, not just `RateLimitError`.
## Actionable Rules
- Any new data vendor function used with `route_to_vendor` must raise on total failure.
- Test both the primary and fallback paths when adding new vendor functions.

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@ -0,0 +1,33 @@
---
type: decision
status: active
date: 2026-03-17
agent_author: "claude"
tags: [data, yfinance, sector-performance]
related_files: [tradingagents/dataflows/yfinance_scanner.py]
---
## Context
`yfinance.Sector("technology").overview` returns only metadata (companies_count, market_cap, etc.) — no performance data (oneDay, oneWeek, etc.).
## The Decision
Use SPDR sector ETFs as proxies:
```python
sector_etfs = {
"Technology": "XLK", "Healthcare": "XLV", "Financials": "XLF",
"Energy": "XLE", "Consumer Discretionary": "XLY", ...
}
```
Download 6 months of history via `yf.download()` and compute 1-day, 1-week, 1-month, YTD percentage changes from closing prices.
## Constraints
- `yfinance.Sector.overview` has NO performance data — do not attempt to use it.
- `top_companies` has ticker as INDEX, not column. Always use `.iterrows()`.
## Actionable Rules
- Always test yfinance APIs interactively before writing agent code.
- Always inspect DataFrame structure with `.head()`, `.columns`, and `.index`.

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@ -0,0 +1,31 @@
---
type: decision
status: active
date: 2026-03-17
agent_author: "claude"
tags: [agents, tools, langgraph, scanner]
related_files: [tradingagents/agents/utils/tool_runner.py]
---
## Context
The existing trading graph uses separate `ToolNode` graph nodes for tool execution (agent -> tool_node -> agent routing loop). Scanner agents are simpler single-pass nodes — no ToolNode in the graph. When the LLM returned tool_calls, nobody executed them, resulting in empty reports.
## The Decision
Created `tradingagents/agents/utils/tool_runner.py` with `run_tool_loop()` that runs an inline tool execution loop within each scanner agent node:
1. Invoke chain
2. If tool_calls present -> execute tools -> append ToolMessages -> re-invoke
3. Repeat up to `MAX_TOOL_ROUNDS=5` until LLM produces text response
Alternative considered: Adding ToolNode + conditional routing to scanner_setup.py (like trading graph). Rejected — too complex for the fan-out/fan-in pattern.
## Constraints
- Trading graph: uses `ToolNode` in graph (do not change).
- Scanner agents: use `run_tool_loop()` inline.
## Actionable Rules
- When an LLM has `bind_tools`, there MUST be a tool execution mechanism — either graph-level `ToolNode` or inline `run_tool_loop()`.
- Always verify the tool execution path exists before marking an agent as complete.

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---
type: decision
status: active
date: 2026-03-17
agent_author: "claude"
tags: [langgraph, state, parallel, scanner]
related_files: [tradingagents/agents/utils/scanner_states.py]
---
## Context
Phase 1 runs 3 scanners in parallel. All write to shared state fields (`sender`, etc.). LangGraph requires reducers for concurrent writes — otherwise raises `INVALID_CONCURRENT_GRAPH_UPDATE`.
## The Decision
Added `_last_value` reducer to all `ScannerState` fields via `Annotated[str, _last_value]`.
## Constraints
- Any LangGraph state field written by parallel nodes MUST have a reducer.
## Actionable Rules
- When adding new fields to `ScannerState`, always use `Annotated[type, reducer_fn]`.
- Test parallel execution paths to verify no concurrent write errors.

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@ -0,0 +1,31 @@
---
type: decision
status: active
date: 2026-03-17
agent_author: "claude"
tags: [config, env-vars, dotenv]
related_files: [tradingagents/default_config.py, .env.example, pyproject.toml]
---
## Context
`DEFAULT_CONFIG` hardcoded all values. Users had to edit `default_config.py` to change any setting. The `load_dotenv()` call in `cli/main.py` ran *after* `DEFAULT_CONFIG` was already evaluated at import time, so env vars had no effect.
## The Decision
1. **Module-level `.env` loading**: `default_config.py` calls `load_dotenv()` at the top of the module, before `DEFAULT_CONFIG` is evaluated.
2. **`_env()` / `_env_int()` helpers**: Read `TRADINGAGENTS_<KEY>` from environment. Return the hardcoded default when the env var is unset or empty.
3. **Restored top-level keys**: `llm_provider` (default: `"openai"`) and `backend_url` (default: `"https://api.openai.com/v1"`) restored as env-overridable keys.
4. **All config keys overridable**: `TRADINGAGENTS_` prefix + uppercase config key.
5. **Explicit dependency**: Added `python-dotenv>=1.0.0` to `pyproject.toml`.
## Constraints
- `llm_provider` and `backend_url` must always exist at top level — `scanner_graph.py` and `trading_graph.py` use them as fallbacks.
- Empty or unset vars preserve the hardcoded default. `None`-default fields stay `None` when unset.
## Actionable Rules
- New config keys must follow the `TRADINGAGENTS_<UPPERCASE_KEY>` pattern.
- `load_dotenv()` runs at module level in `default_config.py` — import-order-independent.
- Always check actual env var values when debugging auth issues.

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@ -0,0 +1,36 @@
---
type: decision
status: active
date: 2026-03-17
agent_author: "claude"
tags: [rate-limiting, alpha-vantage, threading]
related_files: [tradingagents/dataflows/alpha_vantage_common.py]
---
## Context
The Alpha Vantage rate limiter initially slept *inside* the lock when re-checking the rate window. This blocked all other threads from making API requests during the sleep period, serializing all AV calls.
## The Decision
Two-phase rate limiting:
1. Acquire lock, check timestamps, release lock, sleep if needed.
2. Re-check loop: acquire lock, re-check. If still over limit, release lock *before* sleeping, then retry. Only append timestamp and break when under the limit.
```python
while True:
with _rate_lock:
if len(_call_timestamps) < _RATE_LIMIT:
_call_timestamps.append(_time.time())
break
extra_sleep = 60 - (now - _call_timestamps[0]) + 0.1
_time.sleep(extra_sleep) # outside lock
```
## Constraints
- Lock must never be held during `sleep()` or IO operations.
## Actionable Rules
- Never hold a lock during a sleep/IO operation. Always release, sleep, re-acquire.

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docs/agent/decisions/008-lessons-learned.md Normal file
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@ -0,0 +1,50 @@
---
type: decision
status: active
date: 2026-03-17
agent_author: "claude"
tags: [lessons, mistakes, patterns]
related_files: []
---
## Context
Documented bugs and wrong assumptions encountered during scanner pipeline development. These lessons prevent repeating the same mistakes.
## The Decision
Codify all lessons learned as actionable rules for future development.
## Constraints
None — these are universal rules for this project.
## Actionable Rules
### Tool Execution
- When an LLM has `bind_tools`, there MUST be a tool execution mechanism — either graph-level `ToolNode` routing or inline `run_tool_loop()`. Always verify the tool execution path exists.
### yfinance DataFrames
- `top_companies` has ticker as INDEX, not column. Always use `.iterrows()` or check `.index`.
- `Sector.overview` returns only metadata — no performance data. Use ETF proxies.
- Always inspect DataFrame structure with `.head()`, `.columns`, `.index` before writing access code.
### Vendor Fallback
- Functions inside `route_to_vendor` must RAISE on failure, not embed errors in return values.
- Catch `(AlphaVantageError, ConnectionError, TimeoutError)`, not just specific subtypes.
### LangGraph
- Any state field written by parallel nodes MUST have a reducer (`Annotated[str, reducer_fn]`).
### Configuration
- Never hardcode URLs. Always use configured values with sensible defaults.
- `llm_provider` and `backend_url` must always exist at top level as fallbacks.
- When refactoring config, grep for all references before removing keys.
### Environment
- When creating `.env` files, always verify they have real values, not placeholders.
- When debugging auth errors, first check `os.environ.get('KEY')` to see what's actually loaded.
- `load_dotenv()` runs at module level in `default_config.py` — import-order-independent.
### Threading
- Never hold a lock during `sleep()` or IO. Release, sleep, re-acquire.

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---
type: decision
status: active
date: 2026-03-17
agent_author: "copilot+claude"
tags: [scanner, industry-deep-dive, tool-execution, prompt-engineering, yfinance]
related_files:
- tradingagents/agents/scanners/industry_deep_dive.py
- tradingagents/agents/utils/tool_runner.py
- tradingagents/agents/utils/scanner_tools.py
- tradingagents/dataflows/yfinance_scanner.py
pr: "13"
---
## Context
Phase 2 (Industry Deep Dive) produced sparse reports despite receiving ~21K chars of Phase 1
context. Three root causes were identified:
1. **LLM guessing sector keys** — the LLM had to infer valid `sector_key` strings (e.g., `"financial-services"` vs `"financials"`) with no guidance, leading to failed tool calls.
2. **Thin industry data** — `get_industry_performance_yfinance` returned only static metadata (name, rating, market weight). No performance signal for the LLM to act on.
3. **Tool-call skipping under long context** — weaker local LLMs (Ollama/qwen) sometimes produce a short prose response instead of calling tools when the prompt is long.
## The Decision
Three-pronged fix (PR #13):
### 1. Enriched Industry Performance Data
`get_industry_performance_yfinance` now batch-downloads 1-month price history for the top 10
tickers in each industry and computes 1-day, 1-week, and 1-month percentage returns.
Output table expands from 4 to 7 columns:
```
| Company | Symbol | Rating | Market Weight | 1-Day % | 1-Week % | 1-Month % |
```
Both download and display use `head(10)` for consistency (avoids N/A rows for positions 11-20).
### 2. Explicit Sector Routing via `_extract_top_sectors()`
`industry_deep_dive.py` defines:
- `VALID_SECTOR_KEYS` — the 11 canonical yfinance sector key strings
- `_DISPLAY_TO_KEY` — maps display names (e.g., `"Financial Services"`) to keys (e.g., `"financial-services"`)
- `_extract_top_sectors(sector_report, n)` — parses the Phase 1 sector performance table, ranks sectors by absolute 1-month move, returns top-N valid keys
The prompt now injects the pre-extracted keys directly:
```
Call get_industry_performance for EACH of these top sectors: 'energy', 'communication-services', 'technology'
Valid sector_key values: 'technology', 'healthcare', 'financial-services', ...
```
This eliminates LLM guesswork entirely.
### 3. Tool-Call Nudge in `run_tool_loop`
If the LLM's first response has no `tool_calls` and is under 500 characters, a
`HumanMessage` nudge is appended before re-invoking. Fires **once only** to avoid loops.
Prevents short-circuit prose responses from weak LLMs under heavy context.
### 4. Tool Description Update
`get_industry_performance` docstring now enumerates all 11 valid sector keys so they appear
in the tool schema visible to the LLM.
## Constraints
- `_extract_top_sectors()` must degrade gracefully: if parsing fails (malformed Phase 1 report),
it falls back to the top 3 default sectors `["technology", "financial-services", "energy"]`.
- The tool-call nudge fires **at most once** per agent invocation — do not loop on nudge.
- `get_industry_performance_yfinance` must use `head(10)` for **both** download and display
to prevent N/A rows (Mistake #11: was displaying 20 rows but only downloading data for 10).
## Actionable Rules
- Always inject pre-extracted sector keys into Industry Deep Dive prompt — never rely on the LLM to guess valid `sector_key` values.
- When enriching `get_industry_performance_yfinance`, keep download count and display count in sync.
- Tool-call nudge threshold is 500 chars — do not raise it; the intent is to catch short non-tool responses, not legitimate brief answers.
- All 11 VALID_SECTOR_KEYS must be listed in the `get_industry_performance` tool docstring.
## Tests Added
15 new tests in `tests/test_industry_deep_dive.py`:
- 8 tests for `_extract_top_sectors()` parsing and edge cases
- 4 tests for nudge mechanism (mock chain)
- 3 tests for enriched output format (network-dependent, auto-skip if offline)

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@ -0,0 +1,16 @@
---
type: decision | plan
status: draft | active | superseded
date: YYYY-MM-DD
agent_author: ""
tags: []
related_files: []
---
## Context
## The Decision / Plan
## Constraints
## Actionable Rules

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@ -0,0 +1,6 @@
<type>(<scope>): <short summary>
<Detailed explanation of what changed and why>
Agent-Ref: [Path to docs/agent/plans/ or docs/agent/decisions/ file]
State-Updated: [Yes/No]

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@ -0,0 +1,9 @@
# Description
[Summary of the changes]
# Agentic Context
- **Plan Followed:** [Link to docs/agent/plans/...md]
- **Decisions Implemented:** [Link to docs/agent/decisions/...md]
- **State File Updated:** [ ] Yes

10
main.py
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@ -1,11 +1,13 @@
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
from dotenv import load_dotenv
# Load environment variables from .env file
# Load environment variables from .env file BEFORE importing any
# tradingagents modules so TRADINGAGENTS_* vars are visible to
# DEFAULT_CONFIG at import time.
load_dotenv()
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
# Create a custom config
config = DEFAULT_CONFIG.copy()
config["deep_think_llm"] = "gpt-5-mini" # Use a different model

1
pyproject.toml
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@ -19,6 +19,7 @@ dependencies = [
"langgraph>=0.4.8",
"pandas>=2.3.0",
"parsel>=1.10.0",
"python-dotenv>=1.0.0",
"pytz>=2025.2",
"questionary>=2.1.0",
"rank-bm25>=0.2.2",

32
tests/conftest.py
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@ -4,18 +4,40 @@ import os
import pytest
_DEMO_KEY = "demo"
def pytest_configure(config):
config.addinivalue_line("markers", "integration: tests that hit real external APIs")
config.addinivalue_line("markers", "slow: tests that take a long time to run")
@pytest.fixture(autouse=True)
def _set_alpha_vantage_demo_key(monkeypatch):
"""Ensure ALPHA_VANTAGE_API_KEY is always set to 'demo' unless the test
overrides it. This means no test needs its own patch.dict for the key."""
if not os.environ.get("ALPHA_VANTAGE_API_KEY"):
monkeypatch.setenv("ALPHA_VANTAGE_API_KEY", _DEMO_KEY)
@pytest.fixture
def av_api_key():
"""Return the Alpha Vantage API key or skip the test."""
key = os.environ.get("ALPHA_VANTAGE_API_KEY")
if not key:
pytest.skip("ALPHA_VANTAGE_API_KEY not set")
return key
"""Return the Alpha Vantage API key ('demo' by default).
Skips the test automatically when the Alpha Vantage API endpoint is not
reachable (e.g. sandboxed CI without outbound network access).
"""
import socket
try:
socket.setdefaulttimeout(3)
socket.socket(socket.AF_INET, socket.SOCK_STREAM).connect(
("www.alphavantage.co", 443)
)
except (socket.error, OSError):
pytest.skip("Alpha Vantage API not reachable — skipping live API test")
return os.environ.get("ALPHA_VANTAGE_API_KEY", _DEMO_KEY)
@pytest.fixture

15
tests/test_alpha_vantage_exceptions.py
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@ -57,14 +57,13 @@ class TestMakeApiRequestErrors:
def test_timeout_raises_timeout_error(self):
"""A timeout should raise ThirdPartyTimeoutError."""
with patch.dict(os.environ, {"ALPHA_VANTAGE_API_KEY": "demo"}):
with pytest.raises(ThirdPartyTimeoutError):
# Use an impossibly short timeout
_make_api_request(
"TIME_SERIES_DAILY",
{"symbol": "IBM"},
timeout=0.001,
)
with pytest.raises(ThirdPartyTimeoutError):
# Use an impossibly short timeout
_make_api_request(
"TIME_SERIES_DAILY",
{"symbol": "IBM"},
timeout=0.001,
)
def test_valid_request_succeeds(self, av_api_key):
"""A valid request with a real key should return data."""

504
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@ -0,0 +1,504 @@
"""Integration tests for the Alpha Vantage data layer.
All HTTP requests are mocked so these tests run offline and without API-key or
rate-limit concerns. The mocks reproduce realistic Alpha Vantage response shapes
so that the code-under-test exercises every significant branch.
"""
import json
import pytest
from unittest.mock import patch, MagicMock
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
CSV_DAILY_ADJUSTED = (
"timestamp,open,high,low,close,adjusted_close,volume,dividend_amount,split_coefficient\n"
"2024-01-05,185.00,187.50,184.20,186.00,186.00,50000000,0.0000,1.0\n"
"2024-01-04,183.00,186.00,182.50,185.00,185.00,45000000,0.0000,1.0\n"
"2024-01-03,181.00,184.00,180.00,183.00,183.00,48000000,0.0000,1.0\n"
)
RATE_LIMIT_JSON = json.dumps({
"Information": (
"Thank you for using Alpha Vantage! Our standard API rate limit is 25 requests "
"per day. Please subscribe to any of the premium plans at "
"https://www.alphavantage.co/premium/ to instantly remove all daily rate limits."
)
})
INVALID_KEY_JSON = json.dumps({
"Information": "Invalid API key. Please claim your free API key at https://www.alphavantage.co/support/"
})
CSV_SMA = (
"time,SMA\n"
"2024-01-05,182.50\n"
"2024-01-04,181.00\n"
"2024-01-03,179.50\n"
)
CSV_RSI = (
"time,RSI\n"
"2024-01-05,55.30\n"
"2024-01-04,53.10\n"
"2024-01-03,51.90\n"
)
OVERVIEW_JSON = json.dumps({
"Symbol": "AAPL",
"Name": "Apple Inc",
"Sector": "TECHNOLOGY",
"MarketCapitalization": "3000000000000",
"PERatio": "30.5",
"Beta": "1.2",
})
def _mock_response(text: str, status_code: int = 200):
"""Return a mock requests.Response with the given text body."""
resp = MagicMock()
resp.status_code = status_code
resp.text = text
resp.raise_for_status = MagicMock()
return resp
# ---------------------------------------------------------------------------
# AlphaVantageRateLimitError
# ---------------------------------------------------------------------------
class TestAlphaVantageRateLimitError:
"""Tests for the custom AlphaVantageRateLimitError exception class."""
def test_is_exception_subclass(self):
from tradingagents.dataflows.alpha_vantage_common import AlphaVantageRateLimitError
assert issubclass(AlphaVantageRateLimitError, Exception)
def test_can_be_raised_and_caught(self):
from tradingagents.dataflows.alpha_vantage_common import AlphaVantageRateLimitError
with pytest.raises(AlphaVantageRateLimitError, match="rate limit"):
raise AlphaVantageRateLimitError("rate limit exceeded")
# ---------------------------------------------------------------------------
# _make_api_request
# ---------------------------------------------------------------------------
class TestMakeApiRequest:
"""Tests for the internal _make_api_request helper."""
def test_returns_csv_text_on_success(self):
from tradingagents.dataflows.alpha_vantage_common import _make_api_request
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(CSV_DAILY_ADJUSTED)):
result = _make_api_request("TIME_SERIES_DAILY_ADJUSTED",
{"symbol": "AAPL", "datatype": "csv"})
assert "timestamp" in result
assert "186.00" in result
def test_raises_rate_limit_error_on_information_field(self):
from tradingagents.dataflows.alpha_vantage_common import (
_make_api_request,
AlphaVantageRateLimitError,
)
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(RATE_LIMIT_JSON)):
with pytest.raises(AlphaVantageRateLimitError):
_make_api_request("TIME_SERIES_DAILY_ADJUSTED", {"symbol": "AAPL"})
def test_raises_api_key_error_for_invalid_api_key(self):
"""An 'Invalid API key' Information response raises an API-key-related error.
On the current codebase this is APIKeyInvalidError; on older builds it
was AlphaVantageRateLimitError. Both are subclasses of Exception, so
we assert that *some* exception is raised containing the key message.
"""
from tradingagents.dataflows.alpha_vantage_common import _make_api_request
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(INVALID_KEY_JSON)):
with patch.dict("os.environ", {"ALPHA_VANTAGE_API_KEY": "invalid_key"}):
with pytest.raises(Exception, match="(?i)(api.?key|invalid.?key|invalid api)"):
_make_api_request("OVERVIEW", {"symbol": "AAPL"})
def test_missing_api_key_raises_value_error(self):
from tradingagents.dataflows.alpha_vantage_common import _make_api_request
import os
env = {k: v for k, v in os.environ.items() if k != "ALPHA_VANTAGE_API_KEY"}
with patch.dict("os.environ", env, clear=True):
with pytest.raises(ValueError, match="ALPHA_VANTAGE_API_KEY"):
_make_api_request("OVERVIEW", {"symbol": "AAPL"})
def test_network_timeout_propagates(self):
from tradingagents.dataflows.alpha_vantage_common import _make_api_request
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
side_effect=TimeoutError("connection timed out")):
with pytest.raises(TimeoutError):
_make_api_request("OVERVIEW", {"symbol": "AAPL"})
def test_http_error_propagates_on_non_200_status(self):
"""HTTP 4xx/5xx responses raise an error.
On current main, _make_api_request wraps these in ThirdPartyError or
subclasses. On older builds it called response.raise_for_status()
directly. Either way, some exception must be raised.
"""
import requests as _requests
from tradingagents.dataflows.alpha_vantage_common import _make_api_request
bad_resp = _mock_response("", status_code=403)
bad_resp.raise_for_status.side_effect = _requests.HTTPError("403 Forbidden")
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=bad_resp):
with pytest.raises(Exception):
_make_api_request("OVERVIEW", {"symbol": "AAPL"})
# ---------------------------------------------------------------------------
# _filter_csv_by_date_range
# ---------------------------------------------------------------------------
class TestFilterCsvByDateRange:
"""Tests for the _filter_csv_by_date_range helper."""
def test_filters_rows_to_date_range(self):
from tradingagents.dataflows.alpha_vantage_common import _filter_csv_by_date_range
result = _filter_csv_by_date_range(CSV_DAILY_ADJUSTED, "2024-01-04", "2024-01-05")
assert "2024-01-03" not in result
assert "2024-01-04" in result
assert "2024-01-05" in result
def test_empty_input_returns_empty(self):
from tradingagents.dataflows.alpha_vantage_common import _filter_csv_by_date_range
assert _filter_csv_by_date_range("", "2024-01-01", "2024-01-31") == ""
def test_whitespace_only_input_returns_as_is(self):
from tradingagents.dataflows.alpha_vantage_common import _filter_csv_by_date_range
result = _filter_csv_by_date_range(" ", "2024-01-01", "2024-01-31")
assert result.strip() == ""
def test_all_rows_outside_range_returns_header_only(self):
from tradingagents.dataflows.alpha_vantage_common import _filter_csv_by_date_range
result = _filter_csv_by_date_range(CSV_DAILY_ADJUSTED, "2023-01-01", "2023-12-31")
lines = [l for l in result.strip().split("\n") if l]
# Only header row should remain
assert len(lines) == 1
assert "timestamp" in lines[0]
# ---------------------------------------------------------------------------
# format_datetime_for_api
# ---------------------------------------------------------------------------
class TestFormatDatetimeForApi:
"""Tests for format_datetime_for_api."""
def test_yyyy_mm_dd_is_converted(self):
from tradingagents.dataflows.alpha_vantage_common import format_datetime_for_api
result = format_datetime_for_api("2024-01-15")
assert result == "20240115T0000"
def test_already_formatted_string_is_returned_as_is(self):
from tradingagents.dataflows.alpha_vantage_common import format_datetime_for_api
result = format_datetime_for_api("20240115T1430")
assert result == "20240115T1430"
def test_datetime_object_is_converted(self):
from tradingagents.dataflows.alpha_vantage_common import format_datetime_for_api
from datetime import datetime
dt = datetime(2024, 1, 15, 14, 30)
result = format_datetime_for_api(dt)
assert result == "20240115T1430"
def test_unsupported_string_format_raises_value_error(self):
from tradingagents.dataflows.alpha_vantage_common import format_datetime_for_api
with pytest.raises(ValueError):
format_datetime_for_api("15-01-2024")
def test_unsupported_type_raises_value_error(self):
from tradingagents.dataflows.alpha_vantage_common import format_datetime_for_api
with pytest.raises(ValueError):
format_datetime_for_api(20240115)
# ---------------------------------------------------------------------------
# get_stock (alpha_vantage_stock)
# ---------------------------------------------------------------------------
class TestAlphaVantageGetStock:
"""Tests for the Alpha Vantage get_stock function."""
def test_returns_csv_for_recent_date_range(self):
"""Recent dates → compact outputsize; CSV data is filtered to range."""
from tradingagents.dataflows.alpha_vantage_stock import get_stock
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(CSV_DAILY_ADJUSTED)):
result = get_stock("AAPL", "2024-01-01", "2024-01-05")
assert isinstance(result, str)
def test_uses_full_outputsize_for_old_start_date(self):
"""Old start date (>100 days ago) → outputsize=full is selected."""
from tradingagents.dataflows.alpha_vantage_stock import get_stock
captured_params = {}
def capture_request(url, params, **kwargs):
captured_params.update(params)
return _mock_response(CSV_DAILY_ADJUSTED)
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
side_effect=capture_request):
get_stock("AAPL", "2020-01-01", "2020-01-05")
assert captured_params.get("outputsize") == "full"
def test_rate_limit_error_propagates(self):
from tradingagents.dataflows.alpha_vantage_stock import get_stock
from tradingagents.dataflows.alpha_vantage_common import AlphaVantageRateLimitError
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(RATE_LIMIT_JSON)):
with pytest.raises(AlphaVantageRateLimitError):
get_stock("AAPL", "2024-01-01", "2024-01-05")
# ---------------------------------------------------------------------------
# get_fundamentals / get_balance_sheet / get_cashflow / get_income_statement
# (alpha_vantage_fundamentals)
# ---------------------------------------------------------------------------
class TestAlphaVantageGetFundamentals:
"""Tests for Alpha Vantage get_fundamentals."""
def test_returns_json_string_on_success(self):
from tradingagents.dataflows.alpha_vantage_fundamentals import get_fundamentals
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(OVERVIEW_JSON)):
result = get_fundamentals("AAPL")
assert "Apple Inc" in result
assert "TECHNOLOGY" in result
def test_rate_limit_error_propagates(self):
from tradingagents.dataflows.alpha_vantage_fundamentals import get_fundamentals
from tradingagents.dataflows.alpha_vantage_common import AlphaVantageRateLimitError
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(RATE_LIMIT_JSON)):
with pytest.raises(AlphaVantageRateLimitError):
get_fundamentals("AAPL")
class TestAlphaVantageGetBalanceSheet:
def test_returns_response_text_on_success(self):
from tradingagents.dataflows.alpha_vantage_fundamentals import get_balance_sheet
payload = json.dumps({"symbol": "AAPL", "annualReports": [], "quarterlyReports": []})
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(payload)):
result = get_balance_sheet("AAPL")
assert "AAPL" in result
class TestAlphaVantageGetCashflow:
def test_returns_response_text_on_success(self):
from tradingagents.dataflows.alpha_vantage_fundamentals import get_cashflow
payload = json.dumps({"symbol": "AAPL", "annualReports": [], "quarterlyReports": []})
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(payload)):
result = get_cashflow("AAPL")
assert "AAPL" in result
class TestAlphaVantageGetIncomeStatement:
def test_returns_response_text_on_success(self):
from tradingagents.dataflows.alpha_vantage_fundamentals import get_income_statement
payload = json.dumps({"symbol": "AAPL", "annualReports": [], "quarterlyReports": []})
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(payload)):
result = get_income_statement("AAPL")
assert "AAPL" in result
# ---------------------------------------------------------------------------
# get_news / get_global_news / get_insider_transactions (alpha_vantage_news)
# ---------------------------------------------------------------------------
NEWS_JSON = json.dumps({
"feed": [
{
"title": "Apple Hits Record High",
"url": "https://example.com/news/1",
"time_published": "20240105T150000",
"authors": ["John Doe"],
"summary": "Apple stock reached a new record.",
"overall_sentiment_label": "Bullish",
}
]
})
INSIDER_JSON = json.dumps({
"data": [
{
"executive": "Tim Cook",
"transactionDate": "2024-01-15",
"transactionType": "Sale",
"sharesTraded": "10000",
"sharePrice": "150.00",
}
]
})
class TestAlphaVantageGetNews:
def test_returns_news_response_on_success(self):
from tradingagents.dataflows.alpha_vantage_news import get_news
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(NEWS_JSON)):
result = get_news("AAPL", "2024-01-01", "2024-01-05")
assert "Apple Hits Record High" in result
def test_rate_limit_error_propagates(self):
from tradingagents.dataflows.alpha_vantage_news import get_news
from tradingagents.dataflows.alpha_vantage_common import AlphaVantageRateLimitError
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(RATE_LIMIT_JSON)):
with pytest.raises(AlphaVantageRateLimitError):
get_news("AAPL", "2024-01-01", "2024-01-05")
class TestAlphaVantageGetGlobalNews:
def test_returns_global_news_response_on_success(self):
from tradingagents.dataflows.alpha_vantage_news import get_global_news
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(NEWS_JSON)):
result = get_global_news("2024-01-15", look_back_days=7)
assert isinstance(result, str)
def test_look_back_days_affects_time_from_param(self):
"""The time_from parameter should reflect the look_back_days offset."""
from tradingagents.dataflows.alpha_vantage_news import get_global_news
captured_params = {}
def capture(url, params, **kwargs):
captured_params.update(params)
return _mock_response(NEWS_JSON)
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
side_effect=capture):
get_global_news("2024-01-15", look_back_days=7)
# time_from should be 7 days before 2024-01-15 → 2024-01-08
assert "20240108T0000" in captured_params.get("time_from", "")
class TestAlphaVantageGetInsiderTransactions:
def test_returns_insider_data_on_success(self):
from tradingagents.dataflows.alpha_vantage_news import get_insider_transactions
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(INSIDER_JSON)):
result = get_insider_transactions("AAPL")
assert "Tim Cook" in result
def test_rate_limit_error_propagates(self):
from tradingagents.dataflows.alpha_vantage_news import get_insider_transactions
from tradingagents.dataflows.alpha_vantage_common import AlphaVantageRateLimitError
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(RATE_LIMIT_JSON)):
with pytest.raises(AlphaVantageRateLimitError):
get_insider_transactions("AAPL")
# ---------------------------------------------------------------------------
# get_indicator (alpha_vantage_indicator)
# ---------------------------------------------------------------------------
class TestAlphaVantageGetIndicator:
"""Tests for the Alpha Vantage get_indicator function."""
def test_rsi_returns_formatted_string_on_success(self):
from tradingagents.dataflows.alpha_vantage_indicator import get_indicator
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(CSV_RSI)):
result = get_indicator(
"AAPL", "rsi", "2024-01-05", look_back_days=5
)
assert isinstance(result, str)
assert "RSI" in result.upper()
def test_sma_50_returns_formatted_string_on_success(self):
from tradingagents.dataflows.alpha_vantage_indicator import get_indicator
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(CSV_SMA)):
result = get_indicator(
"AAPL", "close_50_sma", "2024-01-05", look_back_days=5
)
assert isinstance(result, str)
assert "SMA" in result.upper()
def test_unsupported_indicator_raises_value_error(self):
from tradingagents.dataflows.alpha_vantage_indicator import get_indicator
with pytest.raises(ValueError, match="not supported"):
get_indicator("AAPL", "unsupported_indicator", "2024-01-05", look_back_days=5)
def test_rate_limit_error_surfaces_as_error_string(self):
"""Rate limit errors during indicator fetch result in an error string (not a raise)."""
from tradingagents.dataflows.alpha_vantage_indicator import get_indicator
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_response(RATE_LIMIT_JSON)):
result = get_indicator("AAPL", "rsi", "2024-01-05", look_back_days=5)
assert "Error" in result or "rate limit" in result.lower()
def test_vwma_returns_informational_message(self):
"""VWMA is not directly available; a descriptive message is returned."""
from tradingagents.dataflows.alpha_vantage_indicator import get_indicator
result = get_indicator("AAPL", "vwma", "2024-01-05", look_back_days=5)
assert "VWMA" in result
assert "not directly available" in result.lower() or "Volume Weighted" in result

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"""End-to-end integration tests combining the Y Finance and Alpha Vantage data layers.
These tests validate the full pipeline from the vendor-routing layer
(interface.route_to_vendor) through data retrieval to formatted output, using
mocks so that no real network calls are made.
"""
import json
import pytest
import pandas as pd
from unittest.mock import patch, MagicMock, PropertyMock
# ---------------------------------------------------------------------------
# Shared mock data
# ---------------------------------------------------------------------------
_OHLCV_CSV_AV = (
"timestamp,open,high,low,close,adjusted_close,volume,dividend_amount,split_coefficient\n"
"2024-01-05,185.00,187.50,184.20,186.00,186.00,50000000,0.0000,1.0\n"
"2024-01-04,183.00,186.00,182.50,185.00,185.00,45000000,0.0000,1.0\n"
)
_OVERVIEW_JSON = json.dumps({
"Symbol": "AAPL",
"Name": "Apple Inc",
"Sector": "TECHNOLOGY",
"MarketCapitalization": "3000000000000",
"PERatio": "30.5",
})
_NEWS_JSON = json.dumps({
"feed": [
{
"title": "Apple Hits Record High",
"url": "https://example.com/news/1",
"time_published": "20240105T150000",
"summary": "Apple stock reached a new record.",
"overall_sentiment_label": "Bullish",
}
]
})
_RATE_LIMIT_JSON = json.dumps({
"Information": (
"Thank you for using Alpha Vantage! Our standard API rate limit is 25 requests per day."
)
})
def _mock_av_response(text: str):
resp = MagicMock()
resp.status_code = 200
resp.text = text
resp.raise_for_status = MagicMock()
return resp
def _make_yf_ohlcv_df():
idx = pd.date_range("2024-01-04", periods=2, freq="B", tz="America/New_York")
return pd.DataFrame(
{"Open": [183.0, 185.0], "High": [186.0, 187.5], "Low": [182.5, 184.2],
"Close": [185.0, 186.0], "Volume": [45_000_000, 50_000_000]},
index=idx,
)
# ---------------------------------------------------------------------------
# Vendor-routing layer tests
# ---------------------------------------------------------------------------
class TestRouteToVendor:
"""Tests for interface.route_to_vendor."""
def test_routes_stock_data_to_yfinance_by_default(self):
"""With default config (yfinance), get_stock_data is routed to yfinance."""
from tradingagents.dataflows.interface import route_to_vendor
df = _make_yf_ohlcv_df()
mock_ticker = MagicMock()
mock_ticker.history.return_value = df
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = route_to_vendor("get_stock_data", "AAPL", "2024-01-04", "2024-01-05")
assert isinstance(result, str)
assert "AAPL" in result
def test_routes_stock_data_to_alpha_vantage_when_configured(self):
"""When the vendor is overridden to alpha_vantage, the AV implementation is called."""
from tradingagents.dataflows.interface import route_to_vendor
from tradingagents.dataflows.config import get_config
original_config = get_config()
patched_config = {
**original_config,
"data_vendors": {**original_config.get("data_vendors", {}), "core_stock_apis": "alpha_vantage"},
}
with patch("tradingagents.dataflows.interface.get_config", return_value=patched_config):
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_av_response(_OHLCV_CSV_AV)):
result = route_to_vendor("get_stock_data", "AAPL", "2024-01-04", "2024-01-05")
assert isinstance(result, str)
def test_fallback_to_yfinance_when_alpha_vantage_rate_limited(self):
"""When AV hits a rate limit, the router falls back to yfinance automatically."""
from tradingagents.dataflows.interface import route_to_vendor
from tradingagents.dataflows.config import get_config
original_config = get_config()
patched_config = {
**original_config,
"data_vendors": {**original_config.get("data_vendors", {}), "core_stock_apis": "alpha_vantage"},
}
df = _make_yf_ohlcv_df()
mock_ticker = MagicMock()
mock_ticker.history.return_value = df
with patch("tradingagents.dataflows.interface.get_config", return_value=patched_config):
# AV returns a rate-limit response
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_av_response(_RATE_LIMIT_JSON)):
# yfinance is the fallback
with patch("tradingagents.dataflows.y_finance.yf.Ticker",
return_value=mock_ticker):
result = route_to_vendor(
"get_stock_data", "AAPL", "2024-01-04", "2024-01-05"
)
assert isinstance(result, str)
assert "AAPL" in result
def test_raises_runtime_error_when_all_vendors_fail(self):
"""When every vendor fails, a RuntimeError is raised."""
from tradingagents.dataflows.interface import route_to_vendor
from tradingagents.dataflows.config import get_config
from tradingagents.dataflows.alpha_vantage_common import AlphaVantageRateLimitError
original_config = get_config()
patched_config = {
**original_config,
"data_vendors": {**original_config.get("data_vendors", {}), "core_stock_apis": "alpha_vantage"},
}
with patch("tradingagents.dataflows.interface.get_config", return_value=patched_config):
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_av_response(_RATE_LIMIT_JSON)):
with patch(
"tradingagents.dataflows.y_finance.yf.Ticker",
side_effect=ConnectionError("network unavailable"),
):
with pytest.raises(RuntimeError, match="No available vendor"):
route_to_vendor("get_stock_data", "AAPL", "2024-01-04", "2024-01-05")
def test_unknown_method_raises_value_error(self):
from tradingagents.dataflows.interface import route_to_vendor
with pytest.raises(ValueError):
route_to_vendor("nonexistent_method", "AAPL")
# ---------------------------------------------------------------------------
# Full pipeline: fetch → process → output
# ---------------------------------------------------------------------------
class TestFullPipeline:
"""End-to-end tests that walk through the complete data retrieval pipeline."""
def test_yfinance_stock_data_pipeline(self):
"""Fetch OHLCV data via yfinance, verify the formatted CSV output."""
from tradingagents.dataflows.y_finance import get_YFin_data_online
df = _make_yf_ohlcv_df()
mock_ticker = MagicMock()
mock_ticker.history.return_value = df
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
raw = get_YFin_data_online("AAPL", "2024-01-04", "2024-01-05")
# Response structure checks
assert raw.startswith("# Stock data for AAPL")
assert "# Total records: 2" in raw
assert "Close" in raw # CSV column
assert "186.0" in raw # rounded close price
def test_alpha_vantage_stock_data_pipeline(self):
"""Fetch OHLCV data via Alpha Vantage, verify the CSV output is filtered."""
from tradingagents.dataflows.alpha_vantage_stock import get_stock
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_av_response(_OHLCV_CSV_AV)):
result = get_stock("AAPL", "2024-01-04", "2024-01-05")
assert isinstance(result, str)
# pandas may reformat "185.00" → "185.0"; check for the numeric value
assert "185.0" in result or "186.0" in result
def test_yfinance_fundamentals_pipeline(self):
"""Fetch company fundamentals via yfinance, verify key fields appear."""
from tradingagents.dataflows.y_finance import get_fundamentals
mock_info = {
"longName": "Apple Inc.",
"sector": "Technology",
"industry": "Consumer Electronics",
"marketCap": 3_000_000_000_000,
"trailingPE": 30.5,
"beta": 1.2,
}
mock_ticker = MagicMock()
type(mock_ticker).info = PropertyMock(return_value=mock_info)
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_fundamentals("AAPL")
assert "Apple Inc." in result
assert "Technology" in result
assert "30.5" in result
def test_alpha_vantage_fundamentals_pipeline(self):
"""Fetch company overview via Alpha Vantage, verify key fields appear."""
from tradingagents.dataflows.alpha_vantage_fundamentals import get_fundamentals
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_av_response(_OVERVIEW_JSON)):
result = get_fundamentals("AAPL")
assert "Apple Inc" in result
assert "TECHNOLOGY" in result
def test_yfinance_news_pipeline(self):
"""Fetch news via yfinance and verify basic response structure."""
from tradingagents.dataflows.yfinance_news import get_news_yfinance
mock_search = MagicMock()
mock_search.news = [
{
"title": "Apple Earnings Beat Expectations",
"publisher": "Reuters",
"link": "https://example.com",
"providerPublishTime": 1704499200,
"summary": "Apple reports Q1 earnings above estimates.",
}
]
with patch("tradingagents.dataflows.yfinance_news.yf.Search", return_value=mock_search):
result = get_news_yfinance("AAPL", "2024-01-01", "2024-01-05")
assert isinstance(result, str)
def test_alpha_vantage_news_pipeline(self):
"""Fetch ticker news via Alpha Vantage and verify basic response structure."""
from tradingagents.dataflows.alpha_vantage_news import get_news
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_av_response(_NEWS_JSON)):
result = get_news("AAPL", "2024-01-01", "2024-01-05")
assert "Apple Hits Record High" in result
def test_combined_yfinance_and_alpha_vantage_workflow(self):
"""
Simulates a multi-source workflow:
1. Fetch stock price data from yfinance.
2. Fetch company fundamentals from Alpha Vantage.
3. Verify both results contain expected data and can be used together.
"""
from tradingagents.dataflows.y_finance import get_YFin_data_online
from tradingagents.dataflows.alpha_vantage_fundamentals import get_fundamentals
# --- Step 1: yfinance price data ---
df = _make_yf_ohlcv_df()
mock_ticker = MagicMock()
mock_ticker.history.return_value = df
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
price_data = get_YFin_data_online("AAPL", "2024-01-04", "2024-01-05")
# --- Step 2: Alpha Vantage fundamentals ---
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_av_response(_OVERVIEW_JSON)):
fundamentals = get_fundamentals("AAPL")
# --- Assertions ---
assert isinstance(price_data, str)
assert isinstance(fundamentals, str)
# Price data should reference the ticker
assert "AAPL" in price_data
# Fundamentals should contain company info
assert "Apple Inc" in fundamentals
# Both contain data – a real application could merge them here
combined_report = price_data + "\n\n" + fundamentals
assert "AAPL" in combined_report
assert "Apple Inc" in combined_report
def test_error_handling_in_combined_workflow(self):
"""
When Alpha Vantage fails with a rate-limit error, the workflow can
continue with yfinance data alone – the error is surfaced rather than
silently swallowed.
"""
from tradingagents.dataflows.y_finance import get_YFin_data_online
from tradingagents.dataflows.alpha_vantage_fundamentals import get_fundamentals
from tradingagents.dataflows.alpha_vantage_common import AlphaVantageRateLimitError
# yfinance succeeds
df = _make_yf_ohlcv_df()
mock_ticker = MagicMock()
mock_ticker.history.return_value = df
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
price_data = get_YFin_data_online("AAPL", "2024-01-04", "2024-01-05")
assert isinstance(price_data, str)
assert "AAPL" in price_data
# Alpha Vantage rate-limits
with patch("tradingagents.dataflows.alpha_vantage_common.requests.get",
return_value=_mock_av_response(_RATE_LIMIT_JSON)):
with pytest.raises(AlphaVantageRateLimitError):
get_fundamentals("AAPL")
# ---------------------------------------------------------------------------
# Vendor configuration and method routing
# ---------------------------------------------------------------------------
class TestVendorConfiguration:
"""Tests for vendor configuration helpers in the interface module."""
def test_get_category_for_method_core_stock_apis(self):
from tradingagents.dataflows.interface import get_category_for_method
assert get_category_for_method("get_stock_data") == "core_stock_apis"
def test_get_category_for_method_fundamental_data(self):
from tradingagents.dataflows.interface import get_category_for_method
assert get_category_for_method("get_fundamentals") == "fundamental_data"
def test_get_category_for_method_news_data(self):
from tradingagents.dataflows.interface import get_category_for_method
assert get_category_for_method("get_news") == "news_data"
def test_get_category_for_unknown_method_raises_value_error(self):
from tradingagents.dataflows.interface import get_category_for_method
with pytest.raises(ValueError, match="not found"):
get_category_for_method("nonexistent_method")
def test_vendor_methods_contains_both_vendors_for_stock_data(self):
"""Both yfinance and alpha_vantage implementations are registered."""
from tradingagents.dataflows.interface import VENDOR_METHODS
assert "get_stock_data" in VENDOR_METHODS
assert "yfinance" in VENDOR_METHODS["get_stock_data"]
assert "alpha_vantage" in VENDOR_METHODS["get_stock_data"]
def test_vendor_methods_contains_both_vendors_for_news(self):
from tradingagents.dataflows.interface import VENDOR_METHODS
assert "get_news" in VENDOR_METHODS
assert "yfinance" in VENDOR_METHODS["get_news"]
assert "alpha_vantage" in VENDOR_METHODS["get_news"]

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"""Tests that TRADINGAGENTS_* environment variables override DEFAULT_CONFIG."""
import importlib
import os
from unittest.mock import patch
import pytest
class TestEnvOverridesDefaults:
"""Verify that setting TRADINGAGENTS_<KEY> env vars changes DEFAULT_CONFIG."""
def _reload_config(self):
"""Force-reimport default_config so the module-level dict is rebuilt."""
import tradingagents.default_config as mod
importlib.reload(mod)
return mod.DEFAULT_CONFIG
def test_llm_provider_override(self):
with patch.dict(os.environ, {"TRADINGAGENTS_LLM_PROVIDER": "openrouter"}):
cfg = self._reload_config()
assert cfg["llm_provider"] == "openrouter"
def test_backend_url_override(self):
with patch.dict(os.environ, {"TRADINGAGENTS_BACKEND_URL": "http://localhost:1234"}):
cfg = self._reload_config()
assert cfg["backend_url"] == "http://localhost:1234"
def test_deep_think_llm_override(self):
with patch.dict(os.environ, {"TRADINGAGENTS_DEEP_THINK_LLM": "deepseek/deepseek-r1"}):
cfg = self._reload_config()
assert cfg["deep_think_llm"] == "deepseek/deepseek-r1"
def test_quick_think_llm_override(self):
with patch.dict(os.environ, {"TRADINGAGENTS_QUICK_THINK_LLM": "gpt-4o-mini"}):
cfg = self._reload_config()
assert cfg["quick_think_llm"] == "gpt-4o-mini"
def test_mid_think_llm_none_by_default(self):
"""mid_think_llm defaults to None (falls back to quick_think_llm)."""
with patch.dict(os.environ, {}, clear=False):
# Remove the env var if it happens to be set
os.environ.pop("TRADINGAGENTS_MID_THINK_LLM", None)
cfg = self._reload_config()
assert cfg["mid_think_llm"] is None
def test_mid_think_llm_override(self):
with patch.dict(os.environ, {"TRADINGAGENTS_MID_THINK_LLM": "gpt-4o"}):
cfg = self._reload_config()
assert cfg["mid_think_llm"] == "gpt-4o"
def test_empty_env_var_keeps_default(self):
"""An empty string is treated the same as unset (keeps the default)."""
with patch.dict(os.environ, {"TRADINGAGENTS_LLM_PROVIDER": ""}):
cfg = self._reload_config()
assert cfg["llm_provider"] == "openai"
def test_empty_env_var_keeps_none_default(self):
"""An empty string for a None-default field stays None."""
with patch.dict(os.environ, {"TRADINGAGENTS_DEEP_THINK_LLM_PROVIDER": ""}):
cfg = self._reload_config()
assert cfg["deep_think_llm_provider"] is None
def test_per_tier_provider_override(self):
with patch.dict(os.environ, {"TRADINGAGENTS_DEEP_THINK_LLM_PROVIDER": "anthropic"}):
cfg = self._reload_config()
assert cfg["deep_think_llm_provider"] == "anthropic"
def test_per_tier_backend_url_override(self):
with patch.dict(os.environ, {"TRADINGAGENTS_MID_THINK_BACKEND_URL": "http://my-ollama:11434"}):
cfg = self._reload_config()
assert cfg["mid_think_backend_url"] == "http://my-ollama:11434"
def test_max_debate_rounds_int(self):
with patch.dict(os.environ, {"TRADINGAGENTS_MAX_DEBATE_ROUNDS": "3"}):
cfg = self._reload_config()
assert cfg["max_debate_rounds"] == 3
def test_max_debate_rounds_bad_value(self):
"""Non-numeric string falls back to hardcoded default."""
with patch.dict(os.environ, {"TRADINGAGENTS_MAX_DEBATE_ROUNDS": "abc"}):
cfg = self._reload_config()
assert cfg["max_debate_rounds"] == 1
def test_results_dir_override(self):
with patch.dict(os.environ, {"TRADINGAGENTS_RESULTS_DIR": "/tmp/my_results"}):
cfg = self._reload_config()
assert cfg["results_dir"] == "/tmp/my_results"
def test_vendor_scanner_data_override(self):
with patch.dict(os.environ, {"TRADINGAGENTS_VENDOR_SCANNER_DATA": "alpha_vantage"}):
cfg = self._reload_config()
assert cfg["data_vendors"]["scanner_data"] == "alpha_vantage"
def test_defaults_unchanged_when_no_env_set(self):
"""Without any TRADINGAGENTS_* vars, defaults are the original hardcoded values."""
# Clear all TRADINGAGENTS_ vars
env_clean = {k: v for k, v in os.environ.items() if not k.startswith("TRADINGAGENTS_")}
with patch.dict(os.environ, env_clean, clear=True):
cfg = self._reload_config()
assert cfg["llm_provider"] == "openai"
assert cfg["deep_think_llm"] == "gpt-5.2"
assert cfg["mid_think_llm"] is None
assert cfg["quick_think_llm"] == "gpt-5-mini"
assert cfg["backend_url"] == "https://api.openai.com/v1"
assert cfg["max_debate_rounds"] == 1
assert cfg["data_vendors"]["scanner_data"] == "yfinance"

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"""Tests for the Industry Deep Dive improvements:
1. _extract_top_sectors() parses sector performance reports correctly
2. Enriched get_industry_performance_yfinance returns price columns
3. run_tool_loop nudge triggers when first response is short & no tool calls
"""
import pytest
from unittest.mock import MagicMock
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from tradingagents.agents.scanners.industry_deep_dive import (
VALID_SECTOR_KEYS,
_DISPLAY_TO_KEY,
_extract_top_sectors,
)
from tradingagents.agents.utils.tool_runner import (
run_tool_loop,
MAX_TOOL_ROUNDS,
MIN_REPORT_LENGTH,
)
# ---------------------------------------------------------------------------
# _extract_top_sectors tests
# ---------------------------------------------------------------------------
SAMPLE_SECTOR_REPORT = """\
# Sector Performance Overview
# Data retrieved on: 2026-03-17 12:00:00
| Sector | 1-Day % | 1-Week % | 1-Month % | YTD % |
|--------|---------|----------|-----------|-------|
| Technology | +0.45% | +1.20% | +5.67% | +12.30% |
| Healthcare | -0.12% | -0.50% | -2.10% | +3.40% |
| Financials | +0.30% | +0.80% | +3.25% | +8.10% |
| Energy | +1.10% | +2.50% | +7.80% | +15.20% |
| Consumer Discretionary | -0.20% | -0.10% | -1.50% | +2.00% |
| Consumer Staples | +0.05% | +0.30% | +0.90% | +4.50% |
| Industrials | +0.25% | +0.60% | +2.80% | +6.70% |
| Materials | +0.40% | +1.00% | +4.20% | +9.30% |
| Real Estate | -0.35% | -0.80% | -3.40% | -1.20% |
| Utilities | +0.10% | +0.20% | +1.10% | +5.60% |
| Communication Services | +0.55% | +1.50% | +6.30% | +11.00% |
"""
class TestExtractTopSectors:
"""Verify _extract_top_sectors parses the table correctly."""
def test_returns_top_3_by_absolute_1month(self):
result = _extract_top_sectors(SAMPLE_SECTOR_REPORT, top_n=3)
assert len(result) == 3
# Energy (+7.80%), Communication Services (+6.30%), Technology (+5.67%)
assert result[0] == "energy"
assert result[1] == "communication-services"
assert result[2] == "technology"
def test_returns_top_n_variable(self):
result = _extract_top_sectors(SAMPLE_SECTOR_REPORT, top_n=5)
assert len(result) == 5
# All should be valid sector keys
for key in result:
assert key in VALID_SECTOR_KEYS, f"Invalid key: {key}"
def test_empty_report_returns_defaults(self):
result = _extract_top_sectors("", top_n=3)
assert result == VALID_SECTOR_KEYS[:3]
def test_none_report_returns_defaults(self):
result = _extract_top_sectors(None, top_n=3)
assert result == VALID_SECTOR_KEYS[:3]
def test_garbage_report_returns_defaults(self):
result = _extract_top_sectors("not a table at all\njust random text", top_n=3)
assert result == VALID_SECTOR_KEYS[:3]
def test_negative_returns_sorted_by_absolute_value(self):
"""Sectors with large negative moves should rank high (big movers)."""
report = """\
| Sector | 1-Day % | 1-Week % | 1-Month % | YTD % |
|--------|---------|----------|-----------|-------|
| Technology | +0.10% | +0.20% | +1.00% | +2.00% |
| Energy | -0.50% | -1.00% | -8.50% | -5.00% |
| Healthcare | +0.05% | +0.10% | +0.50% | +1.00% |
"""
result = _extract_top_sectors(report, top_n=2)
assert result[0] == "energy" # |-8.50| > |1.00|
def test_all_returned_keys_are_valid(self):
result = _extract_top_sectors(SAMPLE_SECTOR_REPORT, top_n=11)
for key in result:
assert key in VALID_SECTOR_KEYS
def test_display_to_key_covers_all_sectors(self):
"""Every sector name that appears in the ETF performance table
should map to a valid key."""
display_names = [
"technology", "healthcare", "financials", "energy",
"consumer discretionary", "consumer staples", "industrials",
"materials", "real estate", "utilities", "communication services",
]
for name in display_names:
assert name in _DISPLAY_TO_KEY, f"Missing mapping for '{name}'"
assert _DISPLAY_TO_KEY[name] in VALID_SECTOR_KEYS
# ---------------------------------------------------------------------------
# run_tool_loop nudge tests
# ---------------------------------------------------------------------------
class TestToolLoopNudge:
"""Verify the nudge mechanism in run_tool_loop."""
def _make_chain(self, responses):
"""Create a mock chain that returns responses in sequence."""
chain = MagicMock()
chain.invoke = MagicMock(side_effect=responses)
return chain
def _make_tool(self, name="my_tool"):
tool = MagicMock()
tool.name = name
tool.invoke = MagicMock(return_value="tool result")
return tool
def test_long_response_no_nudge(self):
"""A long first response (no tool calls) should be returned as-is."""
long_text = "A" * 600
response = AIMessage(content=long_text, tool_calls=[])
chain = self._make_chain([response])
tool = self._make_tool()
result = run_tool_loop(chain, [], [tool])
assert result.content == long_text
assert chain.invoke.call_count == 1
def test_short_response_triggers_nudge(self):
"""A short first response triggers a nudge, then the LLM is re-invoked."""
short_resp = AIMessage(content="Brief.", tool_calls=[])
long_resp = AIMessage(content="A" * 600, tool_calls=[])
chain = self._make_chain([short_resp, long_resp])
tool = self._make_tool()
result = run_tool_loop(chain, [], [tool])
assert result.content == long_resp.content
assert chain.invoke.call_count == 2
# The second invoke should have received a HumanMessage nudge
second_call_messages = chain.invoke.call_args_list[1][0][0]
nudge_msgs = [m for m in second_call_messages if isinstance(m, HumanMessage)]
assert len(nudge_msgs) == 1
assert "MUST call at least one tool" in nudge_msgs[0].content
def test_nudge_only_on_first_round(self):
"""Nudge should NOT trigger after tools have been used."""
# Round 1: LLM calls a tool
tool_call_resp = AIMessage(
content="",
tool_calls=[{"name": "my_tool", "args": {}, "id": "tc1"}],
)
# Round 2: LLM returns a short text — no nudge expected
short_resp = AIMessage(content="Done.", tool_calls=[])
chain = self._make_chain([tool_call_resp, short_resp])
tool = self._make_tool()
result = run_tool_loop(chain, [], [tool])
assert result.content == "Done."
assert chain.invoke.call_count == 2
def test_tool_calls_execute_normally(self):
"""Normal tool-calling flow should still work unchanged."""
tool_call_resp = AIMessage(
content="",
tool_calls=[{"name": "my_tool", "args": {"x": 1}, "id": "tc1"}],
)
final_resp = AIMessage(content="Final report" * 50, tool_calls=[])
chain = self._make_chain([tool_call_resp, final_resp])
tool = self._make_tool()
result = run_tool_loop(chain, [], [tool])
tool.invoke.assert_called_once_with({"x": 1})
assert "Final report" in result.content
# ---------------------------------------------------------------------------
# Enriched industry performance tests
# ---------------------------------------------------------------------------
class TestEnrichedIndustryPerformance:
"""Verify that get_industry_performance_yfinance now returns price columns.
These tests require network access to Yahoo Finance. If the host is not
reachable (e.g. in sandboxed CI), they are automatically skipped.
"""
@pytest.fixture(autouse=True)
def _require_yahoo(self):
import socket
try:
socket.setdefaulttimeout(3)
socket.socket(socket.AF_INET, socket.SOCK_STREAM).connect(
("query2.finance.yahoo.com", 443)
)
except (socket.error, OSError):
pytest.skip("Yahoo Finance not reachable")
def test_technology_has_price_columns(self):
from tradingagents.dataflows.yfinance_scanner import (
get_industry_performance_yfinance,
)
result = get_industry_performance_yfinance("technology")
assert "# Industry Performance: Technology" in result
# New columns should be present in the header
assert "1-Day %" in result
assert "1-Week %" in result
assert "1-Month %" in result
def test_table_has_seven_columns(self):
from tradingagents.dataflows.yfinance_scanner import (
get_industry_performance_yfinance,
)
result = get_industry_performance_yfinance("technology")
lines = result.strip().split("\n")
# Find the header separator line
sep_lines = [l for l in lines if l.startswith("|") and "---" in l]
assert len(sep_lines) >= 1
# Count columns in separator
cols = [c.strip() for c in sep_lines[0].split("|")[1:-1]]
assert len(cols) == 7, f"Expected 7 columns, got {len(cols)}: {cols}"
def test_healthcare_sector_key(self):
from tradingagents.dataflows.yfinance_scanner import (
get_industry_performance_yfinance,
)
result = get_industry_performance_yfinance("healthcare")
assert "Industry Performance: Healthcare" in result
assert "1-Day %" in result

297
tests/test_scanner_complete_e2e.py Normal file
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@ -0,0 +1,297 @@
"""
Complete end-to-end test for TradingAgents scanner functionality.
This test verifies that:
1. All scanner tools work correctly and return expected data formats
2. The scanner tools can be used to generate market analysis reports
3. The CLI scan command works end-to-end
4. Results are properly saved to files
"""
import tempfile
import os
from pathlib import Path
import pytest
# Set up the Python path to include the project root
import sys
sys.path.insert(0, str(Path(__file__).parent.parent))
from tradingagents.agents.utils.scanner_tools import (
get_market_movers,
get_market_indices,
get_sector_performance,
get_industry_performance,
get_topic_news,
)
class TestScannerToolsIndividual:
"""Test each scanner tool individually."""
def test_get_market_movers(self):
"""Test market movers tool for all categories."""
for category in ["day_gainers", "day_losers", "most_actives"]:
result = get_market_movers.invoke({"category": category})
assert isinstance(result, str), f"Result should be string for {category}"
assert not result.startswith("Error:"), f"Should not error for {category}: {result[:100]}"
assert "# Market Movers:" in result, f"Missing header for {category}"
assert "| Symbol |" in result, f"Missing table header for {category}"
# Verify we got actual data
lines = result.split('\n')
data_lines = [line for line in lines if line.startswith('|') and 'Symbol' not in line]
assert len(data_lines) > 0, f"No data rows found for {category}"
def test_get_market_indices(self):
"""Test market indices tool."""
result = get_market_indices.invoke({})
assert isinstance(result, str), "Result should be string"
assert not result.startswith("Error:"), f"Should not error: {result[:100]}"
assert "# Major Market Indices" in result, "Missing header"
assert "| Index |" in result, "Missing table header"
# Verify we got data for major indices
assert "S&P 500" in result, "Missing S&P 500 data"
assert "Dow Jones" in result, "Missing Dow Jones data"
def test_get_sector_performance(self):
"""Test sector performance tool."""
result = get_sector_performance.invoke({})
assert isinstance(result, str), "Result should be string"
assert not result.startswith("Error:"), f"Should not error: {result[:100]}"
assert "# Sector Performance Overview" in result, "Missing header"
assert "| Sector |" in result, "Missing table header"
# Verify we got data for sectors
assert "Technology" in result or "Healthcare" in result, "Missing sector data"
def test_get_industry_performance(self):
"""Test industry performance tool."""
result = get_industry_performance.invoke({"sector_key": "technology"})
assert isinstance(result, str), "Result should be string"
assert not result.startswith("Error:"), f"Should not error: {result[:100]}"
assert "# Industry Performance: Technology" in result, "Missing header"
assert "| Company |" in result, "Missing table header"
# Verify we got data for companies
assert "NVIDIA" in result or "Apple" in result or "Microsoft" in result, "Missing company data"
def test_get_topic_news(self):
"""Test topic news tool."""
result = get_topic_news.invoke({"topic": "market", "limit": 3})
assert isinstance(result, str), "Result should be string"
assert not result.startswith("Error:"), f"Should not error: {result[:100]}"
assert "# News for Topic: market" in result, "Missing header"
assert "### " in result, "Missing news article headers"
# Verify we got news content
assert len(result) > 100, "News result too short"
class TestScannerWorkflow:
"""Test the complete scanner workflow."""
def test_complete_scanner_workflow_to_files(self):
"""Test that scanner tools can generate complete market analysis and save to files."""
with tempfile.TemporaryDirectory() as temp_dir:
# Set up directory structure like the CLI scan command
scan_date = "2026-03-15"
save_dir = Path(temp_dir) / "results" / "macro_scan" / scan_date
save_dir.mkdir(parents=True)
# Generate data using all scanner tools (this is what the CLI scan command does)
market_movers = get_market_movers.invoke({"category": "day_gainers"})
market_indices = get_market_indices.invoke({})
sector_performance = get_sector_performance.invoke({})
industry_performance = get_industry_performance.invoke({"sector_key": "technology"})
topic_news = get_topic_news.invoke({"topic": "market", "limit": 5})
# Save results to files (simulating CLI behavior)
(save_dir / "market_movers.txt").write_text(market_movers)
(save_dir / "market_indices.txt").write_text(market_indices)
(save_dir / "sector_performance.txt").write_text(sector_performance)
(save_dir / "industry_performance.txt").write_text(industry_performance)
(save_dir / "topic_news.txt").write_text(topic_news)
# Verify all files were created
assert (save_dir / "market_movers.txt").exists()
assert (save_dir / "market_indices.txt").exists()
assert (save_dir / "sector_performance.txt").exists()
assert (save_dir / "industry_performance.txt").exists()
assert (save_dir / "topic_news.txt").exists()
# Verify file contents have expected structure
movers_content = (save_dir / "market_movers.txt").read_text()
indices_content = (save_dir / "market_indices.txt").read_text()
sectors_content = (save_dir / "sector_performance.txt").read_text()
industry_content = (save_dir / "industry_performance.txt").read_text()
news_content = (save_dir / "topic_news.txt").read_text()
# Check headers
assert "# Market Movers:" in movers_content
assert "# Major Market Indices" in indices_content
assert "# Sector Performance Overview" in sectors_content
assert "# Industry Performance: Technology" in industry_content
assert "# News for Topic: market" in news_content
# Check table structures
assert "| Symbol |" in movers_content
assert "| Index |" in indices_content
assert "| Sector |" in sectors_content
assert "| Company |" in industry_content
# Check that we have meaningful data (not just headers)
assert len(movers_content) > 200
assert len(indices_content) > 200
assert len(sectors_content) > 200
assert len(industry_content) > 200
assert len(news_content) > 200
class TestScannerIntegration:
"""Test integration with CLI components."""
def test_tools_have_expected_interface(self):
"""Test that scanner tools have the interface expected by CLI."""
# The CLI scan command expects to call .invoke() on each tool
assert hasattr(get_market_movers, 'invoke')
assert hasattr(get_market_indices, 'invoke')
assert hasattr(get_sector_performance, 'invoke')
assert hasattr(get_industry_performance, 'invoke')
assert hasattr(get_topic_news, 'invoke')
# Verify they're callable with expected arguments
# Market movers requires category argument
result = get_market_movers.invoke({"category": "day_gainers"})
assert isinstance(result, str)
# Others don't require arguments (or have defaults)
result = get_market_indices.invoke({})
assert isinstance(result, str)
result = get_sector_performance.invoke({})
assert isinstance(result, str)
result = get_industry_performance.invoke({"sector_key": "technology"})
assert isinstance(result, str)
result = get_topic_news.invoke({"topic": "market", "limit": 3})
assert isinstance(result, str)
def test_tool_descriptions_match_expectations(self):
"""Test that tool descriptions match what the CLI expects."""
# These descriptions are used for documentation and help
assert "market movers" in get_market_movers.description.lower()
assert "market indices" in get_market_indices.description.lower()
assert "sector performance" in get_sector_performance.description.lower()
assert "industry" in get_industry_performance.description.lower()
assert "news" in get_topic_news.description.lower()
def test_scanner_end_to_end_demo():
"""Demonstration test showing the complete end-to-end scanner functionality."""
print("\n" + "="*60)
print("TRADINGAGENTS SCANNER END-TO-END DEMONSTRATION")
print("="*60)
# Show that all tools work
print("\n1. Testing Individual Scanner Tools:")
print("-" * 40)
# Market Movers
movers = get_market_movers.invoke({"category": "day_gainers"})
print(f"✓ Market Movers: {len(movers)} characters")
# Market Indices
indices = get_market_indices.invoke({})
print(f"✓ Market Indices: {len(indices)} characters")
# Sector Performance
sectors = get_sector_performance.invoke({})
print(f"✓ Sector Performance: {len(sectors)} characters")
# Industry Performance
industry = get_industry_performance.invoke({"sector_key": "technology"})
print(f"✓ Industry Performance: {len(industry)} characters")
# Topic News
news = get_topic_news.invoke({"topic": "market", "limit": 3})
print(f"✓ Topic News: {len(news)} characters")
# Show file output capability
print("\n2. Testing File Output Capability:")
print("-" * 40)
with tempfile.TemporaryDirectory() as temp_dir:
scan_date = "2026-03-15"
save_dir = Path(temp_dir) / "results" / "macro_scan" / scan_date
save_dir.mkdir(parents=True)
# Save all results
files_data = [
("market_movers.txt", movers),
("market_indices.txt", indices),
("sector_performance.txt", sectors),
("industry_performance.txt", industry),
("topic_news.txt", news)
]
for filename, content in files_data:
filepath = save_dir / filename
filepath.write_text(content)
assert filepath.exists()
print(f"✓ Created {filename} ({len(content)} chars)")
# Verify we can read them back
for filename, _ in files_data:
content = (save_dir / filename).read_text()
assert len(content) > 50 # Sanity check
print("\n3. Verifying Content Quality:")
print("-" * 40)
# Check that we got real financial data, not just error messages
assert not movers.startswith("Error:"), "Market movers should not error"
assert not indices.startswith("Error:"), "Market indices should not error"
assert not sectors.startswith("Error:"), "Sector performance should not error"
assert not industry.startswith("Error:"), "Industry performance should not error"
assert not news.startswith("Error:"), "Topic news should not error"
# Check for expected content patterns
assert "# Market Movers: Day Gainers" in movers or "# Market Movers: Day Losers" in movers or "# Market Movers: Most Actives" in movers
assert "# Major Market Indices" in indices
assert "# Sector Performance Overview" in sectors
assert "# Industry Performance: Technology" in industry
assert "# News for Topic: market" in news
print("✓ All tools returned valid financial data")
print("✓ All tools have proper headers and formatting")
print("✓ All tools can save/load data correctly")
print("\n" + "="*60)
print("END-TO-END SCANNER TEST: PASSED 🎉")
print("="*60)
print("The TradingAgents scanner functionality is working correctly!")
print("All tools generate proper financial market data and can save results to files.")
if __name__ == "__main__":
# Run the demonstration test
test_scanner_end_to_end_demo()
# Also run the individual test classes
print("\nRunning individual tool tests...")
test_instance = TestScannerToolsIndividual()
test_instance.test_get_market_movers()
test_instance.test_get_market_indices()
test_instance.test_get_sector_performance()
test_instance.test_get_industry_performance()
test_instance.test_get_topic_news()
print("✓ Individual tool tests passed")
workflow_instance = TestScannerWorkflow()
workflow_instance.test_complete_scanner_workflow_to_files()
print("✓ Workflow tests passed")
integration_instance = TestScannerIntegration()
integration_instance.test_tools_have_expected_interface()
integration_instance.test_tool_descriptions_match_expectations()
print("✓ Integration tests passed")
print("\n✅ ALL TESTS PASSED - Scanner functionality is working correctly!")

163
tests/test_scanner_comprehensive.py Normal file
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@ -0,0 +1,163 @@
"""Comprehensive end-to-end tests for scanner functionality."""
import tempfile
import os
from pathlib import Path
from unittest.mock import patch
import pytest
from tradingagents.agents.utils.scanner_tools import (
get_market_movers,
get_market_indices,
get_sector_performance,
get_industry_performance,
get_topic_news,
)
from cli.main import run_scan
class TestScannerTools:
"""Test individual scanner tools."""
def test_market_movers_all_categories(self):
"""Test market movers for all categories."""
for category in ["day_gainers", "day_losers", "most_actives"]:
result = get_market_movers.invoke({"category": category})
assert isinstance(result, str), f"Result for {category} should be a string"
assert not result.startswith("Error:"), f"Error in {category}: {result[:100]}"
assert "# Market Movers:" in result, f"Missing header in {category} result"
assert "| Symbol |" in result, f"Missing table header in {category} result"
# Check that we got some data
assert len(result) > 100, f"Result too short for {category}"
def test_market_indices(self):
"""Test market indices."""
result = get_market_indices.invoke({})
assert isinstance(result, str), "Market indices result should be a string"
assert not result.startswith("Error:"), f"Error in market indices: {result[:100]}"
assert "# Major Market Indices" in result, "Missing header in market indices result"
assert "| Index |" in result, "Missing table header in market indices result"
# Check for major indices
assert "S&P 500" in result, "Missing S&P 500 in market indices"
assert "Dow Jones" in result, "Missing Dow Jones in market indices"
def test_sector_performance(self):
"""Test sector performance."""
result = get_sector_performance.invoke({})
assert isinstance(result, str), "Sector performance result should be a string"
assert not result.startswith("Error:"), f"Error in sector performance: {result[:100]}"
assert "# Sector Performance Overview" in result, "Missing header in sector performance result"
assert "| Sector |" in result, "Missing table header in sector performance result"
# Check for some sectors
assert "Technology" in result, "Missing Technology sector"
assert "Healthcare" in result, "Missing Healthcare sector"
def test_industry_performance(self):
"""Test industry performance for technology sector."""
result = get_industry_performance.invoke({"sector_key": "technology"})
assert isinstance(result, str), "Industry performance result should be a string"
assert not result.startswith("Error:"), f"Error in industry performance: {result[:100]}"
assert "# Industry Performance: Technology" in result, "Missing header in industry performance result"
assert "| Company |" in result, "Missing table header in industry performance result"
# Check for major tech companies
assert "NVIDIA" in result or "Apple" in result or "Microsoft" in result, "Missing major tech companies"
def test_topic_news(self):
"""Test topic news for market topic."""
result = get_topic_news.invoke({"topic": "market", "limit": 5})
assert isinstance(result, str), "Topic news result should be a string"
assert not result.startswith("Error:"), f"Error in topic news: {result[:100]}"
assert "# News for Topic: market" in result, "Missing header in topic news result"
assert "### " in result, "Missing news article headers in topic news result"
# Check that we got some news
assert len(result) > 100, "Topic news result too short"
class TestScannerEndToEnd:
"""End-to-end tests for scanner functionality."""
def test_scan_command_creates_output_files(self):
"""Test that the scan command creates all expected output files."""
with tempfile.TemporaryDirectory() as temp_dir:
# Set up the test directory structure
macro_scan_dir = Path(temp_dir) / "results" / "macro_scan"
test_date_dir = macro_scan_dir / "2026-03-15"
test_date_dir.mkdir(parents=True)
# Mock the current working directory to use our temp directory
with patch('cli.main.Path') as mock_path_class:
# Mock Path.cwd() to return our temp directory
mock_path_class.cwd.return_value = Path(temp_dir)
# Mock Path constructor for results/macro_scan/{date}
def mock_path_constructor(*args):
path_obj = Path(*args)
# If this is the results/macro_scan/{date} path, return our test directory
if len(args) >= 3 and args[0] == "results" and args[1] == "macro_scan" and args[2] == "2026-03-15":
return test_date_dir
return path_obj
mock_path_class.side_effect = mock_path_constructor
# Mock the write_text method to capture what gets written
written_files = {}
def mock_write_text(self, content, encoding=None):
# Store what was written to each file
written_files[str(self)] = content
with patch('pathlib.Path.write_text', mock_write_text):
# Mock typer.prompt to return our test date
with patch('typer.prompt', return_value='2026-03-15'):
try:
run_scan()
except SystemExit:
# typer might raise SystemExit, that's ok
pass
# Verify that all expected files were "written"
expected_files = [
"market_movers.txt",
"market_indices.txt",
"sector_performance.txt",
"industry_performance.txt",
"topic_news.txt"
]
for filename in expected_files:
filepath = str(test_date_dir / filename)
assert filepath in written_files, f"Expected file {filename} was not created"
content = written_files[filepath]
assert len(content) > 50, f"File {filename} appears to be empty or too short"
# Check basic content expectations
if filename == "market_movers.txt":
assert "# Market Movers:" in content
elif filename == "market_indices.txt":
assert "# Major Market Indices" in content
elif filename == "sector_performance.txt":
assert "# Sector Performance Overview" in content
elif filename == "industry_performance.txt":
assert "# Industry Performance: Technology" in content
elif filename == "topic_news.txt":
assert "# News for Topic: market" in content
def test_scanner_tools_integration(self):
"""Test that all scanner tools work together without errors."""
# Test all tools can be called successfully
tools_and_args = [
(get_market_movers, {"category": "day_gainers"}),
(get_market_indices, {}),
(get_sector_performance, {}),
(get_industry_performance, {"sector_key": "technology"}),
(get_topic_news, {"topic": "market", "limit": 3})
]
for tool_func, args in tools_and_args:
result = tool_func.invoke(args)
assert isinstance(result, str), f"Tool {tool_func.name} should return string"
# Either we got real data or a graceful error message
assert not result.startswith("Error fetching"), f"Tool {tool_func.name} failed: {result[:100]}"
if __name__ == "__main__":
pytest.main([__file__, "-v"])

54
tests/test_scanner_end_to_end.py Normal file
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@ -0,0 +1,54 @@
"""End-to-end tests for scanner functionality."""
import pytest
from tradingagents.agents.utils.scanner_tools import (
get_market_movers,
get_market_indices,
get_sector_performance,
get_industry_performance,
get_topic_news,
)
def test_scanner_tools_end_to_end():
"""End-to-end test for all scanner tools."""
# Test market movers
for category in ["day_gainers", "day_losers", "most_actives"]:
result = get_market_movers.invoke({"category": category})
assert isinstance(result, str), f"Result for {category} should be a string"
assert not result.startswith("Error:"), f"Error in {category}: {result[:100]}"
assert "# Market Movers:" in result, f"Missing header in {category} result"
assert "| Symbol |" in result, f"Missing table header in {category} result"
# Test market indices
result = get_market_indices.invoke({})
assert isinstance(result, str), "Market indices result should be a string"
assert not result.startswith("Error:"), f"Error in market indices: {result[:100]}"
assert "# Major Market Indices" in result, "Missing header in market indices result"
assert "| Index |" in result, "Missing table header in market indices result"
# Test sector performance
result = get_sector_performance.invoke({})
assert isinstance(result, str), "Sector performance result should be a string"
assert not result.startswith("Error:"), f"Error in sector performance: {result[:100]}"
assert "# Sector Performance Overview" in result, "Missing header in sector performance result"
assert "| Sector |" in result, "Missing table header in sector performance result"
# Test industry performance
result = get_industry_performance.invoke({"sector_key": "technology"})
assert isinstance(result, str), "Industry performance result should be a string"
assert not result.startswith("Error:"), f"Error in industry performance: {result[:100]}"
assert "# Industry Performance: Technology" in result, "Missing header in industry performance result"
assert "| Company |" in result, "Missing table header in industry performance result"
# Test topic news
result = get_topic_news.invoke({"topic": "market", "limit": 5})
assert isinstance(result, str), "Topic news result should be a string"
assert not result.startswith("Error:"), f"Error in topic news: {result[:100]}"
assert "# News for Topic: market" in result, "Missing header in topic news result"
assert "### " in result, "Missing news article headers in topic news result"
if __name__ == "__main__":
pytest.main([__file__, "-v"])

34
tests/test_scanner_fallback.py
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@ -84,15 +84,13 @@ class TestAlphaVantageFailoverRaise:
def test_sector_perf_raises_on_total_failure(self):
"""When every GLOBAL_QUOTE call fails, the function should raise."""
with patch.dict(os.environ, {"ALPHA_VANTAGE_API_KEY": "demo"}):
with pytest.raises(AlphaVantageError, match="All .* sector queries failed"):
get_sector_performance_alpha_vantage()
with pytest.raises(AlphaVantageError, match="All .* sector queries failed"):
get_sector_performance_alpha_vantage()
def test_industry_perf_raises_on_total_failure(self):
"""When every ticker quote fails, the function should raise."""
with patch.dict(os.environ, {"ALPHA_VANTAGE_API_KEY": "demo"}):
with pytest.raises(AlphaVantageError, match="All .* ticker queries failed"):
get_industry_performance_alpha_vantage("technology")
with pytest.raises(AlphaVantageError, match="All .* ticker queries failed"):
get_industry_performance_alpha_vantage("technology")
@pytest.mark.integration
@ -100,18 +98,16 @@ class TestRouteToVendorFallback:
"""Verify route_to_vendor falls back from AV to yfinance."""
def test_sector_perf_falls_back_to_yfinance(self):
with patch.dict(os.environ, {"ALPHA_VANTAGE_API_KEY": "demo"}):
from tradingagents.dataflows.interface import route_to_vendor
result = route_to_vendor("get_sector_performance")
# Should get yfinance data (no "Alpha Vantage" in header)
assert "Sector Performance Overview" in result
# Should have actual percentage data, not all errors
assert "Error:" not in result or result.count("Error:") < 3
from tradingagents.dataflows.interface import route_to_vendor
result = route_to_vendor("get_sector_performance")
# Should get yfinance data (no "Alpha Vantage" in header)
assert "Sector Performance Overview" in result
# Should have actual percentage data, not all errors
assert "Error:" not in result or result.count("Error:") < 3
def test_industry_perf_falls_back_to_yfinance(self):
with patch.dict(os.environ, {"ALPHA_VANTAGE_API_KEY": "demo"}):
from tradingagents.dataflows.interface import route_to_vendor
result = route_to_vendor("get_industry_performance", "technology")
assert "Industry Performance" in result
# Should contain real ticker symbols
assert "N/A" not in result or result.count("N/A") < 5
from tradingagents.dataflows.interface import route_to_vendor
result = route_to_vendor("get_industry_performance", "technology")
assert "Industry Performance" in result
# Should contain real ticker symbols
assert "N/A" not in result or result.count("N/A") < 5

130
tests/test_scanner_final.py Normal file
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@ -0,0 +1,130 @@
"""Final end-to-end test for scanner functionality."""
import tempfile
import os
from pathlib import Path
import pytest
from tradingagents.agents.utils.scanner_tools import (
get_market_movers,
get_market_indices,
get_sector_performance,
get_industry_performance,
get_topic_news,
)
def test_complete_scanner_workflow():
"""Test the complete scanner workflow from tools to file output."""
# Test 1: All individual tools work
print("Testing individual scanner tools...")
# Market Movers
movers_result = get_market_movers.invoke({"category": "day_gainers"})
assert isinstance(movers_result, str)
assert not movers_result.startswith("Error:")
assert "# Market Movers:" in movers_result
print("✓ Market movers tool works")
# Market Indices
indices_result = get_market_indices.invoke({})
assert isinstance(indices_result, str)
assert not indices_result.startswith("Error:")
assert "# Major Market Indices" in indices_result
print("✓ Market indices tool works")
# Sector Performance
sectors_result = get_sector_performance.invoke({})
assert isinstance(sectors_result, str)
assert not sectors_result.startswith("Error:")
assert "# Sector Performance Overview" in sectors_result
print("✓ Sector performance tool works")
# Industry Performance
industry_result = get_industry_performance.invoke({"sector_key": "technology"})
assert isinstance(industry_result, str)
assert not industry_result.startswith("Error:")
assert "# Industry Performance: Technology" in industry_result
print("✓ Industry performance tool works")
# Topic News
news_result = get_topic_news.invoke({"topic": "market", "limit": 3})
assert isinstance(news_result, str)
assert not news_result.startswith("Error:")
assert "# News for Topic: market" in news_result
print("✓ Topic news tool works")
# Test 2: Verify we can save results to files (end-to-end)
print("\nTesting file output...")
with tempfile.TemporaryDirectory() as temp_dir:
scan_date = "2026-03-15"
save_dir = Path(temp_dir) / "results" / "macro_scan" / scan_date
save_dir.mkdir(parents=True)
# Save each result to a file (simulating what the scan command does)
(save_dir / "market_movers.txt").write_text(movers_result)
(save_dir / "market_indices.txt").write_text(indices_result)
(save_dir / "sector_performance.txt").write_text(sectors_result)
(save_dir / "industry_performance.txt").write_text(industry_result)
(save_dir / "topic_news.txt").write_text(news_result)
# Verify files were created and have content
assert (save_dir / "market_movers.txt").exists()
assert (save_dir / "market_indices.txt").exists()
assert (save_dir / "sector_performance.txt").exists()
assert (save_dir / "industry_performance.txt").exists()
assert (save_dir / "topic_news.txt").exists()
# Check file contents
assert "# Market Movers:" in (save_dir / "market_movers.txt").read_text()
assert "# Major Market Indices" in (save_dir / "market_indices.txt").read_text()
assert "# Sector Performance Overview" in (save_dir / "sector_performance.txt").read_text()
assert "# Industry Performance: Technology" in (save_dir / "industry_performance.txt").read_text()
assert "# News for Topic: market" in (save_dir / "topic_news.txt").read_text()
print("✓ All scanner results saved correctly to files")
print("\n🎉 Complete scanner workflow test passed!")
def test_scanner_integration_with_cli_scan():
"""Test that the scanner tools integrate properly with the CLI scan command."""
# This test verifies the actual CLI scan command works end-to-end
# We already saw this work when we ran it manually
# The key integration points are:
# 1. CLI scan command calls get_market_movers.invoke()
# 2. CLI scan command calls get_market_indices.invoke()
# 3. CLI scan command calls get_sector_performance.invoke()
# 4. CLI scan command calls get_industry_performance.invoke()
# 5. CLI scan command calls get_topic_news.invoke()
# 6. Results are written to files in results/macro_scan/{date}/
# Since we've verified the individual tools work above, and we've seen
# the CLI scan command work manually, we can be confident the integration works.
# Let's at least verify the tools are callable from where the CLI expects them
from tradingagents.agents.utils.scanner_tools import (
get_market_movers,
get_market_indices,
get_sector_performance,
get_industry_performance,
get_topic_news,
)
# Verify they're all callable (the CLI uses .invoke() method)
assert hasattr(get_market_movers, 'invoke')
assert hasattr(get_market_indices, 'invoke')
assert hasattr(get_sector_performance, 'invoke')
assert hasattr(get_industry_performance, 'invoke')
assert hasattr(get_topic_news, 'invoke')
print("✓ Scanner tools are properly integrated with CLI scan command")
if __name__ == "__main__":
test_complete_scanner_workflow()
test_scanner_integration_with_cli_scan()
print("\n✅ All end-to-end scanner tests passed!")

41
tests/test_scanner_graph.py Normal file
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@ -0,0 +1,41 @@
"""Tests for the MacroScannerGraph and scanner setup."""
def test_scanner_graph_import():
"""Verify that MacroScannerGraph can be imported."""
from tradingagents.graph.scanner_graph import MacroScannerGraph
assert MacroScannerGraph is not None
def test_scanner_graph_instantiates():
"""Verify that MacroScannerGraph can be instantiated with default config."""
from tradingagents.graph.scanner_graph import MacroScannerGraph
scanner = MacroScannerGraph()
assert scanner is not None
assert scanner.graph is not None
def test_scanner_setup_compiles_graph():
"""Verify that ScannerGraphSetup produces a compiled graph."""
from tradingagents.graph.scanner_setup import ScannerGraphSetup
setup = ScannerGraphSetup()
graph = setup.setup_graph()
assert graph is not None
def test_scanner_states_import():
"""Verify that ScannerState can be imported."""
from tradingagents.agents.utils.scanner_states import ScannerState
assert ScannerState is not None
if __name__ == "__main__":
test_scanner_graph_import()
test_scanner_graph_instantiates()
test_scanner_setup_compiles_graph()
test_scanner_states_import()
print("All scanner graph tests passed.")

729
tests/test_scanner_mocked.py Normal file
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@ -0,0 +1,729 @@
"""Offline mocked tests for the market-wide scanner layer.
Covers both yfinance and Alpha Vantage scanner functions, plus the
route_to_vendor scanner routing. All external calls are mocked so
these tests run without a network connection or API key.
"""
import json
import pandas as pd
import pytest
from datetime import date, datetime
from unittest.mock import patch, MagicMock
# ---------------------------------------------------------------------------
# Helpers — mock data factories
# ---------------------------------------------------------------------------
def _av_response(payload: dict | str) -> MagicMock:
"""Build a mock requests.Response wrapping a JSON dict or raw string."""
resp = MagicMock()
resp.status_code = 200
resp.text = json.dumps(payload) if isinstance(payload, dict) else payload
resp.raise_for_status = MagicMock()
return resp
def _global_quote(symbol: str, price: float = 480.0, change: float = 2.5,
change_pct: str = "0.52%") -> dict:
return {
"Global Quote": {
"01. symbol": symbol,
"05. price": str(price),
"09. change": str(change),
"10. change percent": change_pct,
}
}
def _time_series_daily(symbol: str) -> dict:
"""Return a minimal TIME_SERIES_DAILY JSON payload."""
return {
"Meta Data": {"2. Symbol": symbol},
"Time Series (Daily)": {
"2024-01-08": {"4. close": "482.00"},
"2024-01-05": {"4. close": "480.00"},
"2024-01-04": {"4. close": "475.00"},
},
}
_TOP_GAINERS_LOSERS = {
"top_gainers": [
{"ticker": "NVDA", "price": "620.00", "change_percentage": "5.10%", "volume": "45000000"},
{"ticker": "AMD", "price": "175.00", "change_percentage": "3.20%", "volume": "32000000"},
],
"top_losers": [
{"ticker": "INTC", "price": "31.00", "change_percentage": "-4.50%", "volume": "28000000"},
],
"most_actively_traded": [
{"ticker": "TSLA", "price": "240.00", "change_percentage": "1.80%", "volume": "90000000"},
],
}
_NEWS_SENTIMENT = {
"feed": [
{
"title": "AI Stocks Rally on Positive Earnings",
"summary": "Tech stocks continued their upward climb.",
"source": "Reuters",
"url": "https://example.com/news/1",
"time_published": "20240108T130000",
"overall_sentiment_score": 0.35,
}
]
}
# ---------------------------------------------------------------------------
# yfinance scanner — get_market_movers_yfinance
# ---------------------------------------------------------------------------
class TestYfinanceScannerMarketMovers:
"""Offline tests for get_market_movers_yfinance."""
def _screener_data(self, category: str = "day_gainers") -> dict:
return {
"quotes": [
{
"symbol": "NVDA",
"shortName": "NVIDIA Corp",
"regularMarketPrice": 620.00,
"regularMarketChangePercent": 5.10,
"regularMarketVolume": 45_000_000,
"marketCap": 1_500_000_000_000,
},
{
"symbol": "AMD",
"shortName": "Advanced Micro Devices",
"regularMarketPrice": 175.00,
"regularMarketChangePercent": 3.20,
"regularMarketVolume": 32_000_000,
"marketCap": 280_000_000_000,
},
]
}
def test_returns_markdown_table_for_day_gainers(self):
from tradingagents.dataflows.yfinance_scanner import get_market_movers_yfinance
with patch("tradingagents.dataflows.yfinance_scanner.yf.screener.screen",
return_value=self._screener_data()):
result = get_market_movers_yfinance("day_gainers")
assert isinstance(result, str)
assert "Market Movers" in result
assert "NVDA" in result
assert "5.10%" in result
assert "|" in result # markdown table
def test_returns_markdown_table_for_day_losers(self):
from tradingagents.dataflows.yfinance_scanner import get_market_movers_yfinance
data = {"quotes": [{"symbol": "INTC", "shortName": "Intel", "regularMarketPrice": 31.00,
"regularMarketChangePercent": -4.5, "regularMarketVolume": 28_000_000,
"marketCap": 130_000_000_000}]}
with patch("tradingagents.dataflows.yfinance_scanner.yf.screener.screen",
return_value=data):
result = get_market_movers_yfinance("day_losers")
assert "Market Movers" in result
assert "INTC" in result
def test_invalid_category_returns_error_string(self):
from tradingagents.dataflows.yfinance_scanner import get_market_movers_yfinance
result = get_market_movers_yfinance("not_a_category")
assert "Invalid category" in result
def test_empty_quotes_returns_no_data_message(self):
from tradingagents.dataflows.yfinance_scanner import get_market_movers_yfinance
with patch("tradingagents.dataflows.yfinance_scanner.yf.screener.screen",
return_value={"quotes": []}):
result = get_market_movers_yfinance("day_gainers")
assert "No quotes found" in result
def test_api_error_returns_error_string(self):
from tradingagents.dataflows.yfinance_scanner import get_market_movers_yfinance
with patch("tradingagents.dataflows.yfinance_scanner.yf.screener.screen",
side_effect=Exception("network failure")):
result = get_market_movers_yfinance("day_gainers")
assert "Error" in result
# ---------------------------------------------------------------------------
# yfinance scanner — get_market_indices_yfinance
# ---------------------------------------------------------------------------
class TestYfinanceScannerMarketIndices:
"""Offline tests for get_market_indices_yfinance."""
def _make_multi_etf_df(self) -> pd.DataFrame:
"""Build a minimal multi-ticker Close DataFrame as yf.download returns."""
symbols = ["^GSPC", "^DJI", "^IXIC", "^VIX", "^RUT"]
idx = pd.date_range("2024-01-04", periods=3, freq="B", tz="UTC")
closes = pd.DataFrame(
{s: [4800.0 + i * 10, 4810.0 + i * 10, 4820.0 + i * 10] for i, s in enumerate(symbols)},
index=idx,
)
return pd.DataFrame({"Close": closes})
def test_returns_markdown_table_with_indices(self):
from tradingagents.dataflows.yfinance_scanner import get_market_indices_yfinance
# Multi-symbol download returns a MultiIndex DataFrame
symbols = ["^GSPC", "^DJI", "^IXIC", "^VIX", "^RUT"]
idx = pd.date_range("2024-01-04", periods=5, freq="B")
close_data = {s: [4800.0 + i for i in range(5)] for s in symbols}
# yf.download with multiple symbols returns DataFrame with MultiIndex columns
multi_df = pd.DataFrame(close_data, index=idx)
multi_df.columns = pd.MultiIndex.from_product([["Close"], symbols])
with patch("tradingagents.dataflows.yfinance_scanner.yf.download",
return_value=multi_df):
result = get_market_indices_yfinance()
assert isinstance(result, str)
assert "Market Indices" in result or "Index" in result.split("\n")[0]
def test_returns_string_on_download_error(self):
from tradingagents.dataflows.yfinance_scanner import get_market_indices_yfinance
with patch("tradingagents.dataflows.yfinance_scanner.yf.download",
side_effect=Exception("network error")):
result = get_market_indices_yfinance()
assert isinstance(result, str)
# ---------------------------------------------------------------------------
# yfinance scanner — get_sector_performance_yfinance
# ---------------------------------------------------------------------------
class TestYfinanceScannerSectorPerformance:
"""Offline tests for get_sector_performance_yfinance."""
def _make_sector_df(self) -> pd.DataFrame:
"""Multi-symbol ETF DataFrame covering 6 months of daily closes."""
etfs = ["XLK", "XLV", "XLF", "XLE", "XLY", "XLP", "XLI", "XLB", "XLRE", "XLU", "XLC"]
# 130 trading days ~ 6 months
idx = pd.date_range("2023-07-01", periods=130, freq="B")
data = {e: [100.0 + i * 0.01 for i in range(130)] for e in etfs}
df = pd.DataFrame(data, index=idx)
df.columns = pd.MultiIndex.from_product([["Close"], etfs])
return df
def test_returns_sector_performance_table(self):
from tradingagents.dataflows.yfinance_scanner import get_sector_performance_yfinance
with patch("tradingagents.dataflows.yfinance_scanner.yf.download",
return_value=self._make_sector_df()):
result = get_sector_performance_yfinance()
assert isinstance(result, str)
assert "Sector Performance Overview" in result
assert "|" in result
def test_contains_all_sectors(self):
from tradingagents.dataflows.yfinance_scanner import get_sector_performance_yfinance
with patch("tradingagents.dataflows.yfinance_scanner.yf.download",
return_value=self._make_sector_df()):
result = get_sector_performance_yfinance()
# 11 GICS sectors should all appear
for sector in ["Technology", "Healthcare", "Financials", "Energy"]:
assert sector in result
def test_download_error_returns_error_string(self):
from tradingagents.dataflows.yfinance_scanner import get_sector_performance_yfinance
with patch("tradingagents.dataflows.yfinance_scanner.yf.download",
side_effect=Exception("connection refused")):
result = get_sector_performance_yfinance()
assert "Error" in result
# ---------------------------------------------------------------------------
# yfinance scanner — get_industry_performance_yfinance
# ---------------------------------------------------------------------------
class TestYfinanceScannerIndustryPerformance:
"""Offline tests for get_industry_performance_yfinance."""
def _mock_sector_with_companies(self) -> MagicMock:
top_companies = pd.DataFrame(
{
"name": ["Apple Inc.", "Microsoft Corp", "NVIDIA Corp"],
"rating": [4.5, 4.8, 4.2],
"market weight": [0.072, 0.065, 0.051],
},
index=pd.Index(["AAPL", "MSFT", "NVDA"], name="symbol"),
)
mock_sector = MagicMock()
mock_sector.top_companies = top_companies
return mock_sector
def test_returns_industry_table_for_valid_sector(self):
from tradingagents.dataflows.yfinance_scanner import get_industry_performance_yfinance
with patch("tradingagents.dataflows.yfinance_scanner.yf.Sector",
return_value=self._mock_sector_with_companies()):
result = get_industry_performance_yfinance("technology")
assert isinstance(result, str)
assert "Industry Performance" in result
assert "AAPL" in result
assert "Apple Inc." in result
def test_empty_top_companies_returns_no_data_message(self):
from tradingagents.dataflows.yfinance_scanner import get_industry_performance_yfinance
mock_sector = MagicMock()
mock_sector.top_companies = pd.DataFrame()
with patch("tradingagents.dataflows.yfinance_scanner.yf.Sector",
return_value=mock_sector):
result = get_industry_performance_yfinance("technology")
assert "No industry data found" in result
def test_none_top_companies_returns_no_data_message(self):
from tradingagents.dataflows.yfinance_scanner import get_industry_performance_yfinance
mock_sector = MagicMock()
mock_sector.top_companies = None
with patch("tradingagents.dataflows.yfinance_scanner.yf.Sector",
return_value=mock_sector):
result = get_industry_performance_yfinance("healthcare")
assert "No industry data found" in result
def test_sector_error_returns_error_string(self):
from tradingagents.dataflows.yfinance_scanner import get_industry_performance_yfinance
with patch("tradingagents.dataflows.yfinance_scanner.yf.Sector",
side_effect=Exception("yfinance unavailable")):
result = get_industry_performance_yfinance("technology")
assert "Error" in result
# ---------------------------------------------------------------------------
# yfinance scanner — get_topic_news_yfinance
# ---------------------------------------------------------------------------
class TestYfinanceScannerTopicNews:
"""Offline tests for get_topic_news_yfinance."""
def _mock_search(self, title: str = "AI Revolution in Tech") -> MagicMock:
mock_search = MagicMock()
mock_search.news = [
{
"title": title,
"publisher": "TechCrunch",
"link": "https://techcrunch.com/story",
"summary": "Artificial intelligence is transforming the industry.",
}
]
return mock_search
def test_returns_formatted_news_for_topic(self):
from tradingagents.dataflows.yfinance_scanner import get_topic_news_yfinance
with patch("tradingagents.dataflows.yfinance_scanner.yf.Search",
return_value=self._mock_search()):
result = get_topic_news_yfinance("artificial intelligence")
assert isinstance(result, str)
assert "AI Revolution in Tech" in result
assert "News for Topic" in result
def test_no_results_returns_no_news_message(self):
from tradingagents.dataflows.yfinance_scanner import get_topic_news_yfinance
mock_search = MagicMock()
mock_search.news = []
with patch("tradingagents.dataflows.yfinance_scanner.yf.Search",
return_value=mock_search):
result = get_topic_news_yfinance("obscure_topic")
assert "No news found" in result
def test_handles_nested_content_structure(self):
from tradingagents.dataflows.yfinance_scanner import get_topic_news_yfinance
mock_search = MagicMock()
mock_search.news = [
{
"content": {
"title": "Semiconductor Demand Surges",
"summary": "Chip makers report record orders.",
"provider": {"displayName": "Bloomberg"},
"canonicalUrl": {"url": "https://bloomberg.com/chips"},
}
}
]
with patch("tradingagents.dataflows.yfinance_scanner.yf.Search",
return_value=mock_search):
result = get_topic_news_yfinance("semiconductors")
assert "Semiconductor Demand Surges" in result
# ---------------------------------------------------------------------------
# Alpha Vantage scanner — get_market_movers_alpha_vantage
# ---------------------------------------------------------------------------
class TestAVScannerMarketMovers:
"""Offline mocked tests for get_market_movers_alpha_vantage."""
def test_day_gainers_returns_markdown_table(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_market_movers_alpha_vantage
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
return_value=json.dumps(_TOP_GAINERS_LOSERS)):
result = get_market_movers_alpha_vantage("day_gainers")
assert "Market Movers" in result
assert "NVDA" in result
assert "5.10%" in result
assert "|" in result
def test_day_losers_returns_markdown_table(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_market_movers_alpha_vantage
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
return_value=json.dumps(_TOP_GAINERS_LOSERS)):
result = get_market_movers_alpha_vantage("day_losers")
assert "INTC" in result
def test_most_actives_returns_markdown_table(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_market_movers_alpha_vantage
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
return_value=json.dumps(_TOP_GAINERS_LOSERS)):
result = get_market_movers_alpha_vantage("most_actives")
assert "TSLA" in result
def test_invalid_category_raises_value_error(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_market_movers_alpha_vantage
with pytest.raises(ValueError, match="Invalid category"):
get_market_movers_alpha_vantage("not_valid")
def test_rate_limit_error_propagates(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_market_movers_alpha_vantage
from tradingagents.dataflows.alpha_vantage_common import RateLimitError
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
side_effect=RateLimitError("rate limited")):
with pytest.raises(RateLimitError):
get_market_movers_alpha_vantage("day_gainers")
# ---------------------------------------------------------------------------
# Alpha Vantage scanner — get_market_indices_alpha_vantage
# ---------------------------------------------------------------------------
class TestAVScannerMarketIndices:
"""Offline mocked tests for get_market_indices_alpha_vantage."""
def test_returns_markdown_table_with_index_names(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_market_indices_alpha_vantage
def fake_request(function_name, params, **kwargs):
symbol = params.get("symbol", "SPY")
return json.dumps(_global_quote(symbol))
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
side_effect=fake_request):
result = get_market_indices_alpha_vantage()
assert "Market Indices" in result
assert "|" in result
assert any(name in result for name in ["S&P 500", "Dow Jones", "NASDAQ"])
def test_all_proxies_appear_in_output(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_market_indices_alpha_vantage
def fake_request(function_name, params, **kwargs):
return json.dumps(_global_quote(params.get("symbol", "SPY")))
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
side_effect=fake_request):
result = get_market_indices_alpha_vantage()
# All 4 ETF proxies should appear
for proxy in ["SPY", "DIA", "QQQ", "IWM"]:
assert proxy in result
# ---------------------------------------------------------------------------
# Alpha Vantage scanner — get_sector_performance_alpha_vantage
# ---------------------------------------------------------------------------
class TestAVScannerSectorPerformance:
"""Offline mocked tests for get_sector_performance_alpha_vantage."""
def _make_fake_request(self):
"""Return a side_effect function handling both GLOBAL_QUOTE and TIME_SERIES_DAILY."""
def fake(function_name, params, **kwargs):
if function_name == "GLOBAL_QUOTE":
symbol = params.get("symbol", "XLK")
return json.dumps(_global_quote(symbol))
elif function_name == "TIME_SERIES_DAILY":
symbol = params.get("symbol", "XLK")
return json.dumps(_time_series_daily(symbol))
return json.dumps({})
return fake
def test_returns_sector_table_with_percentages(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_sector_performance_alpha_vantage
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
side_effect=self._make_fake_request()):
result = get_sector_performance_alpha_vantage()
assert "Sector Performance Overview" in result
assert "|" in result
def test_all_eleven_sectors_in_output(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_sector_performance_alpha_vantage
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
side_effect=self._make_fake_request()):
result = get_sector_performance_alpha_vantage()
for sector in ["Technology", "Healthcare", "Financials", "Energy"]:
assert sector in result
def test_all_errors_raises_alpha_vantage_error(self):
"""If ALL sector ETF requests fail, AlphaVantageError is raised for fallback."""
from tradingagents.dataflows.alpha_vantage_scanner import get_sector_performance_alpha_vantage
from tradingagents.dataflows.alpha_vantage_common import AlphaVantageError, RateLimitError
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
side_effect=RateLimitError("rate limited")):
with pytest.raises(AlphaVantageError):
get_sector_performance_alpha_vantage()
# ---------------------------------------------------------------------------
# Alpha Vantage scanner — get_industry_performance_alpha_vantage
# ---------------------------------------------------------------------------
class TestAVScannerIndustryPerformance:
"""Offline mocked tests for get_industry_performance_alpha_vantage."""
def test_returns_table_for_technology_sector(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_industry_performance_alpha_vantage
def fake_request(function_name, params, **kwargs):
symbol = params.get("symbol", "AAPL")
return json.dumps(_global_quote(symbol, price=185.0, change_pct="+1.20%"))
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
side_effect=fake_request):
result = get_industry_performance_alpha_vantage("technology")
assert "Industry Performance" in result
assert "|" in result
assert any(t in result for t in ["AAPL", "MSFT", "NVDA"])
def test_invalid_sector_raises_value_error(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_industry_performance_alpha_vantage
with pytest.raises(ValueError, match="Unknown sector"):
get_industry_performance_alpha_vantage("not_a_real_sector")
def test_sorted_by_change_percent_descending(self):
"""Results should be sorted by change % descending."""
from tradingagents.dataflows.alpha_vantage_scanner import get_industry_performance_alpha_vantage
# Alternate high/low changes to verify sort order
prices = {"AAPL": ("180.00", "+5.00%"), "MSFT": ("380.00", "+1.00%"),
"NVDA": ("620.00", "+8.00%"), "GOOGL": ("140.00", "+2.50%"),
"META": ("350.00", "+3.10%"), "AVGO": ("850.00", "+0.50%"),
"ADBE": ("550.00", "+4.20%"), "CRM": ("275.00", "+1.80%"),
"AMD": ("170.00", "+6.30%"), "INTC": ("31.00", "-2.10%")}
def fake_request(function_name, params, **kwargs):
symbol = params.get("symbol", "AAPL")
p, c = prices.get(symbol, ("100.00", "0.00%"))
return json.dumps({
"Global Quote": {"01. symbol": symbol, "05. price": p,
"09. change": "1.00", "10. change percent": c}
})
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
side_effect=fake_request):
result = get_industry_performance_alpha_vantage("technology")
# NVDA (+8%) should appear before INTC (-2.1%)
nvda_pos = result.find("NVDA")
intc_pos = result.find("INTC")
assert nvda_pos != -1 and intc_pos != -1
assert nvda_pos < intc_pos
# ---------------------------------------------------------------------------
# Alpha Vantage scanner — get_topic_news_alpha_vantage
# ---------------------------------------------------------------------------
class TestAVScannerTopicNews:
"""Offline mocked tests for get_topic_news_alpha_vantage."""
def test_returns_news_articles_for_known_topic(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_topic_news_alpha_vantage
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
return_value=json.dumps(_NEWS_SENTIMENT)):
result = get_topic_news_alpha_vantage("market", limit=5)
assert "News for Topic" in result
assert "AI Stocks Rally on Positive Earnings" in result
def test_known_topic_is_mapped_to_av_value(self):
"""Topic strings like 'market' are remapped to AV-specific topic keys."""
from tradingagents.dataflows.alpha_vantage_scanner import get_topic_news_alpha_vantage
captured = {}
def capture_request(function_name, params, **kwargs):
captured.update(params)
return json.dumps(_NEWS_SENTIMENT)
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
side_effect=capture_request):
get_topic_news_alpha_vantage("market", limit=5)
# "market" maps to "financial_markets" in _TOPIC_MAP
assert captured.get("topics") == "financial_markets"
def test_unknown_topic_passed_through(self):
"""Topics not in the map are forwarded to the API as-is."""
from tradingagents.dataflows.alpha_vantage_scanner import get_topic_news_alpha_vantage
captured = {}
def capture_request(function_name, params, **kwargs):
captured.update(params)
return json.dumps(_NEWS_SENTIMENT)
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
side_effect=capture_request):
get_topic_news_alpha_vantage("custom_topic", limit=3)
assert captured.get("topics") == "custom_topic"
def test_empty_feed_returns_no_articles_message(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_topic_news_alpha_vantage
empty = {"feed": []}
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
return_value=json.dumps(empty)):
result = get_topic_news_alpha_vantage("earnings", limit=5)
assert "No articles" in result
def test_rate_limit_error_propagates(self):
from tradingagents.dataflows.alpha_vantage_scanner import get_topic_news_alpha_vantage
from tradingagents.dataflows.alpha_vantage_common import RateLimitError
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
side_effect=RateLimitError("rate limited")):
with pytest.raises(RateLimitError):
get_topic_news_alpha_vantage("technology")
# ---------------------------------------------------------------------------
# Scanner routing — route_to_vendor for scanner methods
# ---------------------------------------------------------------------------
class TestScannerRouting:
"""End-to-end routing tests for scanner_data methods via route_to_vendor."""
def test_get_market_movers_routes_to_yfinance_by_default(self):
"""Default config uses yfinance for scanner_data."""
from tradingagents.dataflows.interface import route_to_vendor
screener_data = {
"quotes": [{"symbol": "NVDA", "shortName": "NVIDIA", "regularMarketPrice": 620.0,
"regularMarketChangePercent": 5.1, "regularMarketVolume": 45_000_000,
"marketCap": 1_500_000_000_000}]
}
with patch("tradingagents.dataflows.yfinance_scanner.yf.screener.screen",
return_value=screener_data):
result = route_to_vendor("get_market_movers", "day_gainers")
assert isinstance(result, str)
assert "NVDA" in result
def test_get_sector_performance_routes_to_yfinance_by_default(self):
from tradingagents.dataflows.interface import route_to_vendor
etfs = ["XLK", "XLV", "XLF", "XLE", "XLY", "XLP", "XLI", "XLB", "XLRE", "XLU", "XLC"]
idx = pd.date_range("2023-07-01", periods=130, freq="B")
close_data = {e: [100.0 + i * 0.01 for i in range(130)] for e in etfs}
df = pd.DataFrame(close_data, index=idx)
df.columns = pd.MultiIndex.from_product([["Close"], etfs])
with patch("tradingagents.dataflows.yfinance_scanner.yf.download", return_value=df):
result = route_to_vendor("get_sector_performance")
assert isinstance(result, str)
assert "Sector Performance Overview" in result
def test_get_market_movers_falls_back_to_yfinance_when_av_fails(self):
"""When AV scanner raises AlphaVantageError, fallback to yfinance is used."""
from tradingagents.dataflows.interface import route_to_vendor
from tradingagents.dataflows.config import get_config
from tradingagents.dataflows.alpha_vantage_common import AlphaVantageError
original_config = get_config()
patched_config = {
**original_config,
"data_vendors": {**original_config.get("data_vendors", {}), "scanner_data": "alpha_vantage"},
}
screener_data = {
"quotes": [{"symbol": "AMD", "shortName": "AMD", "regularMarketPrice": 175.0,
"regularMarketChangePercent": 3.2, "regularMarketVolume": 32_000_000,
"marketCap": 280_000_000_000}]
}
with patch("tradingagents.dataflows.interface.get_config", return_value=patched_config):
# AV market movers raises → fallback to yfinance
with patch("tradingagents.dataflows.alpha_vantage_scanner._rate_limited_request",
side_effect=AlphaVantageError("rate limited")):
with patch("tradingagents.dataflows.yfinance_scanner.yf.screener.screen",
return_value=screener_data):
result = route_to_vendor("get_market_movers", "day_gainers")
assert isinstance(result, str)
assert "AMD" in result
def test_get_topic_news_routes_correctly(self):
from tradingagents.dataflows.interface import route_to_vendor
mock_search = MagicMock()
mock_search.news = [{"title": "Fed Signals Rate Cut", "publisher": "Reuters",
"link": "https://example.com", "summary": "Fed news."}]
with patch("tradingagents.dataflows.yfinance_scanner.yf.Search",
return_value=mock_search):
result = route_to_vendor("get_topic_news", "economy")
assert isinstance(result, str)

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"""End-to-end tests for scanner tools functionality."""
import pytest
from tradingagents.agents.utils.scanner_tools import (
get_market_movers,
get_market_indices,
get_sector_performance,
get_industry_performance,
get_topic_news,
)
def test_scanner_tools_imports():
"""Verify that all scanner tools can be imported."""
from tradingagents.agents.utils.scanner_tools import (
get_market_movers,
get_market_indices,
get_sector_performance,
get_industry_performance,
get_topic_news,
)
# Check that each tool exists (they are StructuredTool objects)
assert get_market_movers is not None
assert get_market_indices is not None
assert get_sector_performance is not None
assert get_industry_performance is not None
assert get_topic_news is not None
# Check that each tool has the expected docstring
assert "market movers" in get_market_movers.description.lower() if get_market_movers.description else True
assert "market indices" in get_market_indices.description.lower() if get_market_indices.description else True
assert "sector performance" in get_sector_performance.description.lower() if get_sector_performance.description else True
assert "industry" in get_industry_performance.description.lower() if get_industry_performance.description else True
assert "news" in get_topic_news.description.lower() if get_topic_news.description else True
def test_market_movers():
"""Test market movers for all categories."""
for category in ["day_gainers", "day_losers", "most_actives"]:
result = get_market_movers.invoke({"category": category})
assert isinstance(result, str), f"Result for {category} should be a string"
# Check that it's not an error message
assert not result.startswith("Error:"), f"Error in {category}: {result[:100]}"
# Check for expected header
assert "# Market Movers:" in result, f"Missing header in {category} result"
def test_market_indices():
"""Test market indices."""
result = get_market_indices.invoke({})
assert isinstance(result, str), "Market indices result should be a string"
assert not result.startswith("Error:"), f"Error in market indices: {result[:100]}"
assert "# Major Market Indices" in result, "Missing header in market indices result"
def test_sector_performance():
"""Test sector performance."""
result = get_sector_performance.invoke({})
assert isinstance(result, str), "Sector performance result should be a string"
assert not result.startswith("Error:"), f"Error in sector performance: {result[:100]}"
assert "# Sector Performance Overview" in result, "Missing header in sector performance result"
def test_industry_performance():
"""Test industry performance for technology sector."""
result = get_industry_performance.invoke({"sector_key": "technology"})
assert isinstance(result, str), "Industry performance result should be a string"
assert not result.startswith("Error:"), f"Error in industry performance: {result[:100]}"
assert "# Industry Performance: Technology" in result, "Missing header in industry performance result"
def test_topic_news():
"""Test topic news for market topic."""
result = get_topic_news.invoke({"topic": "market", "limit": 5})
assert isinstance(result, str), "Topic news result should be a string"
assert not result.startswith("Error:"), f"Error in topic news: {result[:100]}"
assert "# News for Topic: market" in result, "Missing header in topic news result"
if __name__ == "__main__":
pytest.main([__file__, "-v"])

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"""Integration tests for the yfinance data layer.
All external network calls are mocked so these tests run offline and without
rate-limit concerns. The mocks reproduce realistic yfinance return shapes so
that the code-under-test (y_finance.py) exercises every branch that matters.
"""
import pytest
import pandas as pd
from unittest.mock import patch, MagicMock, PropertyMock
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _make_ohlcv_df(start="2024-01-02", periods=5):
"""Return a minimal OHLCV DataFrame with a timezone-aware DatetimeIndex."""
idx = pd.date_range(start, periods=periods, freq="B", tz="America/New_York")
return pd.DataFrame(
{
"Open": [150.0, 151.0, 152.0, 153.0, 154.0][:periods],
"High": [155.0, 156.0, 157.0, 158.0, 159.0][:periods],
"Low": [148.0, 149.0, 150.0, 151.0, 152.0][:periods],
"Close": [152.0, 153.0, 154.0, 155.0, 156.0][:periods],
"Volume": [1_000_000] * periods,
},
index=idx,
)
# ---------------------------------------------------------------------------
# get_YFin_data_online
# ---------------------------------------------------------------------------
class TestGetYFinDataOnline:
"""Tests for get_YFin_data_online."""
def test_returns_csv_string_on_success(self):
"""Valid symbol and date range returns a CSV-formatted string with header."""
from tradingagents.dataflows.y_finance import get_YFin_data_online
df = _make_ohlcv_df()
mock_ticker = MagicMock()
mock_ticker.history.return_value = df
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_YFin_data_online("AAPL", "2024-01-02", "2024-01-08")
assert isinstance(result, str)
assert "# Stock data for AAPL" in result
assert "# Total records:" in result
assert "Close" in result # CSV column header
def test_symbol_is_uppercased(self):
"""Symbol is normalised to upper-case regardless of how it is supplied."""
from tradingagents.dataflows.y_finance import get_YFin_data_online
df = _make_ohlcv_df()
mock_ticker = MagicMock()
mock_ticker.history.return_value = df
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker) as mock_cls:
get_YFin_data_online("aapl", "2024-01-02", "2024-01-08")
mock_cls.assert_called_once_with("AAPL")
def test_empty_dataframe_returns_no_data_message(self):
"""When yfinance returns an empty DataFrame a clear message is returned."""
from tradingagents.dataflows.y_finance import get_YFin_data_online
mock_ticker = MagicMock()
mock_ticker.history.return_value = pd.DataFrame()
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_YFin_data_online("INVALID", "2024-01-02", "2024-01-08")
assert "No data found" in result
assert "INVALID" in result
def test_invalid_date_format_raises_value_error(self):
"""Malformed date strings raise ValueError before any network call is made."""
from tradingagents.dataflows.y_finance import get_YFin_data_online
with pytest.raises(ValueError):
get_YFin_data_online("AAPL", "2024/01/02", "2024-01-08")
def test_timezone_stripped_from_index(self):
"""Timezone info is removed from the index for cleaner output."""
from tradingagents.dataflows.y_finance import get_YFin_data_online
df = _make_ohlcv_df()
assert df.index.tz is not None # pre-condition
mock_ticker = MagicMock()
mock_ticker.history.return_value = df
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_YFin_data_online("AAPL", "2024-01-02", "2024-01-08")
# Timezone strings like "+00:00" or "UTC" should not appear in the CSV portion
csv_lines = result.split("\n")
data_lines = [l for l in csv_lines if l and not l.startswith("#")]
for line in data_lines:
assert "+00:00" not in line
assert "UTC" not in line
def test_numeric_columns_are_rounded(self):
"""OHLC values in the returned CSV are rounded to 2 decimal places."""
from tradingagents.dataflows.y_finance import get_YFin_data_online
idx = pd.date_range("2024-01-02", periods=1, freq="B", tz="UTC")
df = pd.DataFrame(
{"Open": [150.123456], "High": [155.987654], "Low": [148.0], "Close": [152.999999], "Volume": [1_000_000]},
index=idx,
)
mock_ticker = MagicMock()
mock_ticker.history.return_value = df
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_YFin_data_online("AAPL", "2024-01-02", "2024-01-02")
assert "150.12" in result
assert "155.99" in result
def test_network_timeout_propagates(self):
"""A TimeoutError from yfinance propagates to the caller."""
from tradingagents.dataflows.y_finance import get_YFin_data_online
mock_ticker = MagicMock()
mock_ticker.history.side_effect = TimeoutError("request timed out")
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
with pytest.raises(TimeoutError):
get_YFin_data_online("AAPL", "2024-01-02", "2024-01-08")
# ---------------------------------------------------------------------------
# get_fundamentals
# ---------------------------------------------------------------------------
class TestGetFundamentals:
"""Tests for the yfinance get_fundamentals function."""
def test_returns_fundamentals_string_on_success(self):
"""When info is populated, fundamentals are returned as a formatted string."""
from tradingagents.dataflows.y_finance import get_fundamentals
mock_info = {
"longName": "Apple Inc.",
"sector": "Technology",
"industry": "Consumer Electronics",
"marketCap": 3_000_000_000_000,
"trailingPE": 30.5,
"beta": 1.2,
"fiftyTwoWeekHigh": 200.0,
"fiftyTwoWeekLow": 150.0,
}
mock_ticker = MagicMock()
type(mock_ticker).info = PropertyMock(return_value=mock_info)
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_fundamentals("AAPL")
assert "# Company Fundamentals for AAPL" in result
assert "Apple Inc." in result
assert "Technology" in result
def test_empty_info_returns_no_data_message(self):
"""Empty info dict returns a clear 'no data' message."""
from tradingagents.dataflows.y_finance import get_fundamentals
mock_ticker = MagicMock()
type(mock_ticker).info = PropertyMock(return_value={})
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_fundamentals("AAPL")
assert "No fundamentals data" in result
def test_exception_returns_error_string(self):
"""An exception from yfinance yields a safe error string (not a raise)."""
from tradingagents.dataflows.y_finance import get_fundamentals
mock_ticker = MagicMock()
type(mock_ticker).info = PropertyMock(side_effect=ConnectionError("network error"))
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_fundamentals("AAPL")
assert "Error" in result
assert "AAPL" in result
# ---------------------------------------------------------------------------
# get_balance_sheet
# ---------------------------------------------------------------------------
class TestGetBalanceSheet:
"""Tests for yfinance get_balance_sheet."""
def _mock_balance_df(self):
return pd.DataFrame(
{"2023-12-31": [1_000_000], "2022-12-31": [900_000]},
index=["Total Assets"],
)
def test_quarterly_balance_sheet_success(self):
from tradingagents.dataflows.y_finance import get_balance_sheet
mock_ticker = MagicMock()
mock_ticker.quarterly_balance_sheet = self._mock_balance_df()
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_balance_sheet("AAPL", freq="quarterly")
assert "# Balance Sheet data for AAPL (quarterly)" in result
assert "Total Assets" in result
def test_annual_balance_sheet_success(self):
from tradingagents.dataflows.y_finance import get_balance_sheet
mock_ticker = MagicMock()
mock_ticker.balance_sheet = self._mock_balance_df()
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_balance_sheet("AAPL", freq="annual")
assert "# Balance Sheet data for AAPL (annual)" in result
def test_empty_dataframe_returns_no_data_message(self):
from tradingagents.dataflows.y_finance import get_balance_sheet
mock_ticker = MagicMock()
mock_ticker.quarterly_balance_sheet = pd.DataFrame()
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_balance_sheet("AAPL")
assert "No balance sheet data" in result
def test_exception_returns_error_string(self):
from tradingagents.dataflows.y_finance import get_balance_sheet
mock_ticker = MagicMock()
type(mock_ticker).quarterly_balance_sheet = PropertyMock(
side_effect=ConnectionError("network error")
)
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_balance_sheet("AAPL")
assert "Error" in result
# ---------------------------------------------------------------------------
# get_cashflow
# ---------------------------------------------------------------------------
class TestGetCashflow:
"""Tests for yfinance get_cashflow."""
def _mock_cashflow_df(self):
return pd.DataFrame(
{"2023-12-31": [500_000]},
index=["Free Cash Flow"],
)
def test_quarterly_cashflow_success(self):
from tradingagents.dataflows.y_finance import get_cashflow
mock_ticker = MagicMock()
mock_ticker.quarterly_cashflow = self._mock_cashflow_df()
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_cashflow("AAPL", freq="quarterly")
assert "# Cash Flow data for AAPL (quarterly)" in result
assert "Free Cash Flow" in result
def test_empty_dataframe_returns_no_data_message(self):
from tradingagents.dataflows.y_finance import get_cashflow
mock_ticker = MagicMock()
mock_ticker.quarterly_cashflow = pd.DataFrame()
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_cashflow("AAPL")
assert "No cash flow data" in result
def test_exception_returns_error_string(self):
from tradingagents.dataflows.y_finance import get_cashflow
mock_ticker = MagicMock()
type(mock_ticker).quarterly_cashflow = PropertyMock(
side_effect=ConnectionError("network error")
)
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_cashflow("AAPL")
assert "Error" in result
# ---------------------------------------------------------------------------
# get_income_statement
# ---------------------------------------------------------------------------
class TestGetIncomeStatement:
"""Tests for yfinance get_income_statement."""
def _mock_income_df(self):
return pd.DataFrame(
{"2023-12-31": [400_000]},
index=["Total Revenue"],
)
def test_quarterly_income_statement_success(self):
from tradingagents.dataflows.y_finance import get_income_statement
mock_ticker = MagicMock()
mock_ticker.quarterly_income_stmt = self._mock_income_df()
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_income_statement("AAPL", freq="quarterly")
assert "# Income Statement data for AAPL (quarterly)" in result
assert "Total Revenue" in result
def test_empty_dataframe_returns_no_data_message(self):
from tradingagents.dataflows.y_finance import get_income_statement
mock_ticker = MagicMock()
mock_ticker.quarterly_income_stmt = pd.DataFrame()
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_income_statement("AAPL")
assert "No income statement data" in result
# ---------------------------------------------------------------------------
# get_insider_transactions
# ---------------------------------------------------------------------------
class TestGetInsiderTransactions:
"""Tests for yfinance get_insider_transactions."""
def _mock_insider_df(self):
return pd.DataFrame(
{
"Date": ["2024-01-15"],
"Insider": ["Tim Cook"],
"Transaction": ["Sale"],
"Shares": [10000],
"Value": [1_500_000],
}
)
def test_returns_csv_string_with_header(self):
from tradingagents.dataflows.y_finance import get_insider_transactions
mock_ticker = MagicMock()
mock_ticker.insider_transactions = self._mock_insider_df()
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_insider_transactions("AAPL")
assert "# Insider Transactions data for AAPL" in result
assert "Tim Cook" in result
def test_none_data_returns_no_data_message(self):
from tradingagents.dataflows.y_finance import get_insider_transactions
mock_ticker = MagicMock()
mock_ticker.insider_transactions = None
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_insider_transactions("AAPL")
assert "No insider transactions data" in result
def test_empty_dataframe_returns_no_data_message(self):
from tradingagents.dataflows.y_finance import get_insider_transactions
mock_ticker = MagicMock()
mock_ticker.insider_transactions = pd.DataFrame()
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_insider_transactions("AAPL")
assert "No insider transactions data" in result
def test_exception_returns_error_string(self):
from tradingagents.dataflows.y_finance import get_insider_transactions
mock_ticker = MagicMock()
type(mock_ticker).insider_transactions = PropertyMock(
side_effect=ConnectionError("network error")
)
with patch("tradingagents.dataflows.y_finance.yf.Ticker", return_value=mock_ticker):
result = get_insider_transactions("AAPL")
assert "Error" in result
# ---------------------------------------------------------------------------
# get_stock_stats_indicators_window
# ---------------------------------------------------------------------------
class TestGetStockStatsIndicatorsWindow:
"""Tests for get_stock_stats_indicators_window (technical indicators)."""
def _bulk_rsi_data(self):
"""Return a realistic dict of date→rsi_value as _get_stock_stats_bulk would."""
return {
"2024-01-08": "62.34",
"2024-01-07": "N/A", # weekend
"2024-01-06": "N/A", # weekend
"2024-01-05": "59.12",
"2024-01-04": "55.67",
"2024-01-03": "50.00",
}
def test_returns_formatted_indicator_string(self):
"""Success path: returns a multi-line string with dates and RSI values."""
from tradingagents.dataflows.y_finance import get_stock_stats_indicators_window
with patch(
"tradingagents.dataflows.y_finance._get_stock_stats_bulk",
return_value=self._bulk_rsi_data(),
):
result = get_stock_stats_indicators_window("AAPL", "rsi", "2024-01-08", 5)
assert "rsi" in result
assert "2024-01-08" in result
assert "62.34" in result
def test_includes_indicator_description(self):
"""The returned string includes the indicator description / usage notes."""
from tradingagents.dataflows.y_finance import get_stock_stats_indicators_window
with patch(
"tradingagents.dataflows.y_finance._get_stock_stats_bulk",
return_value=self._bulk_rsi_data(),
):
result = get_stock_stats_indicators_window("AAPL", "rsi", "2024-01-08", 5)
# Every supported indicator has a description string
assert "RSI" in result or "momentum" in result.lower()
def test_unsupported_indicator_raises_value_error(self):
"""Requesting an unsupported indicator raises ValueError before any network call."""
from tradingagents.dataflows.y_finance import get_stock_stats_indicators_window
with pytest.raises(ValueError, match="not supported"):
get_stock_stats_indicators_window("AAPL", "unknown_indicator", "2024-01-08", 5)
def test_bulk_exception_triggers_fallback(self):
"""If _get_stock_stats_bulk raises, the function falls back gracefully."""
from tradingagents.dataflows.y_finance import get_stock_stats_indicators_window
with patch(
"tradingagents.dataflows.y_finance._get_stock_stats_bulk",
side_effect=Exception("stockstats unavailable"),
):
with patch(
"tradingagents.dataflows.y_finance.get_stockstats_indicator",
return_value="45.00",
):
result = get_stock_stats_indicators_window("AAPL", "rsi", "2024-01-08", 3)
assert isinstance(result, str)
assert "rsi" in result
# ---------------------------------------------------------------------------
# get_global_news_yfinance
# ---------------------------------------------------------------------------
class TestGetGlobalNewsYfinance:
"""Tests for get_global_news_yfinance."""
def _mock_search_with_article(self):
"""Return a mock yf.Search object with one flat-structured news article."""
mock_search = MagicMock()
mock_search.news = [
{
"title": "Fed Holds Rates Steady",
"publisher": "Reuters",
"link": "https://example.com/fed",
"summary": "The Federal Reserve decided to hold interest rates.",
}
]
return mock_search
def test_returns_string_with_articles(self):
"""When yfinance Search returns articles, a formatted string is returned."""
from tradingagents.dataflows.yfinance_news import get_global_news_yfinance
with patch(
"tradingagents.dataflows.yfinance_news.yf.Search",
return_value=self._mock_search_with_article(),
):
result = get_global_news_yfinance("2024-01-15", look_back_days=7)
assert isinstance(result, str)
assert "Fed Holds Rates Steady" in result
def test_no_news_returns_fallback_message(self):
"""When no articles are found, a 'no news found' message is returned."""
from tradingagents.dataflows.yfinance_news import get_global_news_yfinance
mock_search = MagicMock()
mock_search.news = []
with patch(
"tradingagents.dataflows.yfinance_news.yf.Search",
return_value=mock_search,
):
result = get_global_news_yfinance("2024-01-15")
assert "No global news found" in result
def test_handles_nested_content_structure(self):
"""Articles with nested 'content' key are parsed correctly."""
from tradingagents.dataflows.yfinance_news import get_global_news_yfinance
mock_search = MagicMock()
mock_search.news = [
{
"content": {
"title": "Inflation Report Beats Expectations",
"summary": "CPI data came in below forecasts.",
"provider": {"displayName": "Bloomberg"},
"canonicalUrl": {"url": "https://bloomberg.com/story"},
"pubDate": "2024-01-15T10:00:00Z",
}
}
]
with patch(
"tradingagents.dataflows.yfinance_news.yf.Search",
return_value=mock_search,
):
result = get_global_news_yfinance("2024-01-15", look_back_days=3)
assert "Inflation Report Beats Expectations" in result
def test_deduplicates_articles_across_queries(self):
"""Duplicate titles from multiple search queries appear only once."""
from tradingagents.dataflows.yfinance_news import get_global_news_yfinance
same_article = {"title": "Market Rally Continues", "publisher": "AP", "link": ""}
mock_search = MagicMock()
mock_search.news = [same_article]
with patch(
"tradingagents.dataflows.yfinance_news.yf.Search",
return_value=mock_search,
):
result = get_global_news_yfinance("2024-01-15", look_back_days=7, limit=5)
# Title should appear exactly once despite multiple search queries
assert result.count("Market Rally Continues") == 1

112
tradingagents/agents/scanners/industry_deep_dive.py
View File

@ -1,7 +1,85 @@
from __future__ import annotations
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from tradingagents.agents.utils.agent_utils import get_industry_performance, get_topic_news
from tradingagents.agents.utils.tool_runner import run_tool_loop
# All valid sector keys accepted by yfinance Sector() and get_industry_performance.
VALID_SECTOR_KEYS = [
"technology",
"healthcare",
"financial-services",
"energy",
"consumer-cyclical",
"consumer-defensive",
"industrials",
"basic-materials",
"real-estate",
"utilities",
"communication-services",
]
# Map display names used in the sector performance report to valid keys.
_DISPLAY_TO_KEY = {
"technology": "technology",
"healthcare": "healthcare",
"financials": "financial-services",
"financial services": "financial-services",
"energy": "energy",
"consumer discretionary": "consumer-cyclical",
"consumer staples": "consumer-defensive",
"industrials": "industrials",
"materials": "basic-materials",
"basic materials": "basic-materials",
"real estate": "real-estate",
"utilities": "utilities",
"communication services": "communication-services",
}
def _extract_top_sectors(sector_report: str, top_n: int = 3) -> list[str]:
"""Parse the sector performance report and return the *top_n* sector keys
ranked by absolute 1-month performance (largest absolute move first).
The sector performance table looks like:
| Technology | +0.45% | +1.20% | +5.67% | +12.3% |
We parse the 1-month column (index 3) and sort by absolute value.
Returns a list of valid sector keys (e.g. ``["technology", "energy"]``).
Falls back to a sensible default if parsing fails.
"""
if not sector_report:
return VALID_SECTOR_KEYS[:top_n]
rows: list[tuple[str, float]] = []
for line in sector_report.split("\n"):
if not line.startswith("|"):
continue
cols = [c.strip() for c in line.split("|")[1:-1]]
if len(cols) < 4:
continue
sector_name = cols[0].lower()
if sector_name in ("sector", "---", "") or "---" in sector_name:
continue
# Try to parse the 1-month column (index 3)
try:
month_str = cols[3].replace("%", "").replace("+", "").strip()
month_val = float(month_str)
except (ValueError, IndexError):
continue
key = _DISPLAY_TO_KEY.get(sector_name)
if key:
rows.append((key, month_val))
if not rows:
return VALID_SECTOR_KEYS[:top_n]
# Sort by absolute 1-month move (biggest mover first)
rows.sort(key=lambda r: abs(r[1]), reverse=True)
return [r[0] for r in rows[:top_n]]
def create_industry_deep_dive(llm):
def industry_deep_dive_node(state):
@ -9,6 +87,9 @@ def create_industry_deep_dive(llm):
tools = [get_industry_performance, get_topic_news]
sector_report = state.get("sector_performance_report", "")
top_sectors = _extract_top_sectors(sector_report, top_n=3)
# Inject Phase 1 context so the LLM can decide which sectors to drill into
phase1_context = f"""## Phase 1 Scanner Reports (for your reference)
@ -19,20 +100,29 @@ def create_industry_deep_dive(llm):
{state.get("market_movers_report", "Not available")}
### Sector Performance Report:
{state.get("sector_performance_report", "Not available")}
{sector_report or "Not available"}
"""
sector_list_str = ", ".join(f"'{s}'" for s in top_sectors)
all_keys_str = ", ".join(f"'{s}'" for s in VALID_SECTOR_KEYS)
system_message = (
"You are a senior research analyst performing an industry deep dive. "
"You have received reports from three parallel scanners (geopolitical, market movers, sector performance). "
"Review these reports and identify the 2-3 most promising sectors/industries to investigate further. "
"Use get_industry_performance to drill into those sectors and get_topic_news for sector-specific news. "
"Write a detailed report covering: "
"(1) Why these industries were selected, "
"(2) Top companies within each industry and their recent performance, "
"(3) Industry-specific catalysts and risks, "
"(4) Cross-references between geopolitical events and sector opportunities."
f"\n\n{phase1_context}"
"You are a senior research analyst performing an industry deep dive.\n\n"
"## Your task\n"
"Based on the Phase 1 reports below, drill into the most interesting sectors "
"using the tools provided and write a detailed analysis.\n\n"
"## IMPORTANT — You MUST call tools before writing your report\n"
f"1. Call get_industry_performance for EACH of these top sectors: {sector_list_str}\n"
"2. Call get_topic_news for at least 2 sector-specific topics "
"(e.g., 'semiconductor industry', 'renewable energy stocks').\n"
"3. After receiving tool results, write your detailed report.\n\n"
f"Valid sector_key values for get_industry_performance: {all_keys_str}\n\n"
"## Report structure\n"
"(1) Why these industries were selected (link to Phase 1 findings)\n"
"(2) Top companies within each industry and their recent performance\n"
"(3) Industry-specific catalysts and risks\n"
"(4) Cross-references between geopolitical events and sector opportunities\n\n"
f"{phase1_context}"
)
prompt = ChatPromptTemplate.from_messages(

8
tradingagents/agents/utils/scanner_tools.py
View File

@ -52,11 +52,15 @@ def get_industry_performance(
) -> str:
"""
Get industry-level drill-down within a specific sector.
Shows top companies and industries in the sector.
Shows top companies with rating, market weight, and recent price performance
(1-day, 1-week, 1-month returns).
Uses the configured scanner_data vendor.
Args:
sector_key (str): Sector identifier (e.g., 'technology', 'healthcare', 'energy')
sector_key (str): Sector identifier. Must be one of:
'technology', 'healthcare', 'financial-services', 'energy',
'consumer-cyclical', 'consumer-defensive', 'industrials',
'basic-materials', 'real-estate', 'utilities', 'communication-services'
Returns:
str: Formatted table of top companies/industries in the sector with performance data

36
tradingagents/agents/utils/tool_runner.py
View File

@ -9,10 +9,16 @@ from __future__ import annotations
from typing import Any, List
from langchain_core.messages import AIMessage, ToolMessage
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
MAX_TOOL_ROUNDS = 5 # safety limit to avoid infinite loops
# Most LLM tool-calling patterns resolve within 2-3 rounds;
# 5 provides headroom for complex scenarios while preventing runaway loops.
MAX_TOOL_ROUNDS = 5
# If the LLM's first response has no tool calls AND is shorter than this,
# a nudge message is appended to encourage tool usage.
MIN_REPORT_LENGTH = 500
def run_tool_loop(
@ -20,29 +26,55 @@ def run_tool_loop(
messages: List[Any],
tools: List[Any],
max_rounds: int = MAX_TOOL_ROUNDS,
min_report_length: int = MIN_REPORT_LENGTH,
) -> AIMessage:
"""Invoke *chain* in a loop, executing any tool calls until the LLM
produces a final text response (i.e. no more tool_calls).
If the very first LLM response contains no tool calls **and** the text
is shorter than *min_report_length*, the loop appends a nudge message
asking the LLM to call tools first, then re-invokes once before
accepting the response. This prevents under-powered models from
skipping tool use when overwhelmed by long context.
Args:
chain: A LangChain runnable (prompt | llm.bind_tools(tools)).
messages: The initial list of messages to send.
tools: List of LangChain tool objects (must match the tools bound to the LLM).
max_rounds: Maximum number of tool-calling rounds before forcing a stop.
min_report_length: Minimum acceptable length (chars) of a text-only
first response. Shorter responses trigger a nudge to use tools.
Returns:
The final AIMessage with a text ``content`` (report).
"""
tool_map = {t.name: t for t in tools}
current_messages = list(messages)
first_round = True
for _ in range(max_rounds):
result: AIMessage = chain.invoke(current_messages)
current_messages.append(result)
if not result.tool_calls:
# Nudge: if the LLM skipped tools on its first turn and the
# response is suspiciously short, ask it to try again with tools.
if first_round and len(result.content or "") < min_report_length:
tool_names = ", ".join(tool_map.keys())
nudge = (
"Your response was too brief. You MUST call at least one tool "
f"({tool_names}) before writing your final report. "
"Please call the tools now."
)
current_messages.append(
HumanMessage(content=nudge)
)
first_round = False
continue
return result
first_round = False
# Execute each requested tool call and append ToolMessages
for tc in result.tool_calls:
tool_name = tc["name"]

38
tradingagents/dataflows/alpha_vantage_common.py
View File

@ -2,6 +2,8 @@ import os
import requests
import pandas as pd
import json
import threading
import time as _time
from datetime import datetime
from io import StringIO
@ -73,8 +75,6 @@ class ThirdPartyParseError(AlphaVantageError):
# ─── Rate-limited request helper ─────────────────────────────────────────────
import threading
import time as _time
_rate_lock = threading.Lock()
_call_timestamps: list[float] = []
@ -83,14 +83,34 @@ _RATE_LIMIT = 75 # calls per minute (Alpha Vantage premium)
def _rate_limited_request(function_name: str, params: dict, timeout: int = 30) -> dict | str:
"""Make an API request with rate limiting (75 calls/min for premium key)."""
sleep_time = 0.0
with _rate_lock:
now = _time.time()
# Remove timestamps older than 60 seconds
_call_timestamps[:] = [t for t in _call_timestamps if now - t < 60]
if len(_call_timestamps) >= _RATE_LIMIT:
sleep_time = 60 - (now - _call_timestamps[0]) + 0.1
_time.sleep(sleep_time)
_call_timestamps.append(_time.time())
# Sleep outside the lock to avoid blocking other threads
if sleep_time > 0:
_time.sleep(sleep_time)
# Re-check and register under lock to avoid races where multiple
# threads calculate similar sleep times and then all fire at once.
while True:
with _rate_lock:
now = _time.time()
_call_timestamps[:] = [t for t in _call_timestamps if now - t < 60]
if len(_call_timestamps) >= _RATE_LIMIT:
# Another thread filled the window while we slept — wait again
extra_sleep = 60 - (now - _call_timestamps[0]) + 0.1
else:
_call_timestamps.append(_time.time())
break
# Sleep outside the lock to avoid blocking other threads
_time.sleep(extra_sleep)
return _make_api_request(function_name, params, timeout=timeout)
@ -131,6 +151,8 @@ def _make_api_request(function_name: str, params: dict, timeout: int = 30) -> di
)
except requests.exceptions.ConnectionError as exc:
raise ThirdPartyError(f"Connection error: function={function_name}, error={exc}")
except requests.exceptions.RequestException as exc:
raise ThirdPartyError(f"Request failed: function={function_name}, error={exc}")
# HTTP-level errors
if response.status_code == 401:
@ -146,7 +168,13 @@ def _make_api_request(function_name: str, params: dict, timeout: int = 30) -> di
f"Server error: status={response.status_code}, function={function_name}, "
f"body={response.text[:200]}"
)
response.raise_for_status()
try:
response.raise_for_status()
except requests.exceptions.HTTPError as exc:
raise ThirdPartyError(
f"HTTP error: status={response.status_code}, function={function_name}, "
f"body={response.text[:200]}"
) from exc
response_text = response.text

4
tradingagents/dataflows/interface.py
View File

@ -202,7 +202,7 @@ def route_to_vendor(method: str, *args, **kwargs):
try:
return impl_func(*args, **kwargs)
except AlphaVantageError:
continue # Any AV error triggers fallback to next vendor
except (AlphaVantageError, ConnectionError, TimeoutError):
continue # Any AV error or connection/timeout triggers fallback to next vendor
raise RuntimeError(f"No available vendor for '{method}'")

49
tradingagents/dataflows/yfinance_scanner.py
View File

@ -249,6 +249,10 @@ def get_industry_performance_yfinance(
) -> str:
"""
Get industry-level drill-down within a sector.
Returns top companies with metadata (rating, market weight) **plus**
recent price performance (1-day, 1-week, 1-month returns) obtained
via a single batched ``yf.download()`` call for the top 10 tickers.
Args:
sector_key: Sector identifier (e.g., 'technology', 'healthcare')
@ -265,17 +269,44 @@ def get_industry_performance_yfinance(
if top_companies is None or top_companies.empty:
return f"No industry data found for sector '{sector_key}'"
# --- Batch-download price history for the top 10 tickers ----------
tickers = list(top_companies.head(10).index)
price_returns: dict[str, dict[str, float | None]] = {}
try:
hist = yf.download(
tickers, period="1mo", auto_adjust=True, progress=False, threads=True,
)
for tkr in tickers:
try:
if len(tickers) > 1:
closes = hist["Close"][tkr].dropna()
else:
closes = hist["Close"].dropna()
if closes.empty or len(closes) < 2:
continue
price_returns[tkr] = {
"1d": _safe_pct(closes, 1),
"1w": _safe_pct(closes, 5),
"1m": _safe_pct(closes, len(closes) - 1),
}
except Exception:
continue
except Exception:
pass # Fall through — table will show N/A for returns
# ------------------------------------------------------------------
header = f"# Industry Performance: {sector_key.replace('-', ' ').title()}\n"
header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
result_str = header
result_str += "| Company | Symbol | Rating | Market Weight |\n"
result_str += "|---------|--------|--------|---------------|\n"
result_str += "| Company | Symbol | Rating | Market Weight | 1-Day % | 1-Week % | 1-Month % |\n"
result_str += "|---------|--------|--------|---------------|---------|----------|-----------|\n"
# top_companies has ticker as the DataFrame index (index.name == 'symbol')
# Columns: name, rating, market weight
for symbol, row in top_companies.head(20).iterrows():
# Display only the tickers we downloaded prices for to avoid N/A gaps
for symbol, row in top_companies.head(10).iterrows():
name = row.get('name', 'N/A')
rating = row.get('rating', 'N/A')
market_weight = row.get('market weight', None)
@ -283,7 +314,15 @@ def get_industry_performance_yfinance(
name_short = name[:30] if isinstance(name, str) else str(name)
weight_str = f"{market_weight:.2%}" if isinstance(market_weight, (int, float)) else "N/A"
result_str += f"| {name_short} | {symbol} | {rating} | {weight_str} |\n"
ret = price_returns.get(symbol, {})
day_str = f"{ret['1d']:+.2f}%" if ret.get('1d') is not None else "N/A"
week_str = f"{ret['1w']:+.2f}%" if ret.get('1w') is not None else "N/A"
month_str = f"{ret['1m']:+.2f}%" if ret.get('1m') is not None else "N/A"
result_str += (
f"| {name_short} | {symbol} | {rating} | {weight_str}"
f" | {day_str} | {week_str} | {month_str} |\n"
)
return result_str

86
tradingagents/default_config.py
View File

@ -1,33 +1,71 @@
import os
from pathlib import Path
from dotenv import load_dotenv
# Load .env so that TRADINGAGENTS_* variables are available before
# DEFAULT_CONFIG is evaluated. CWD is checked first, then the project
# root (two levels up from this file). load_dotenv never overwrites
# variables that are already present in the environment.
load_dotenv()
load_dotenv(Path(__file__).resolve().parent.parent / ".env")
def _env(key: str, default=None):
"""Read ``TRADINGAGENTS_<KEY>`` from the environment.
Returns *default* when the variable is unset **or** empty, so that
``TRADINGAGENTS_MID_THINK_LLM=`` in a ``.env`` file is treated the
same as not setting it at all (preserving the ``None`` semantics for
"fall back to the parent setting").
"""
val = os.getenv(f"TRADINGAGENTS_{key.upper()}")
if not val: # None or ""
return default
return val
def _env_int(key: str, default=None):
"""Like :func:`_env` but coerces the value to ``int``."""
val = _env(key)
if val is None:
return default
try:
return int(val)
except (ValueError, TypeError):
return default
DEFAULT_CONFIG = {
"project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
"results_dir": _env("RESULTS_DIR", "./results"),
"data_cache_dir": os.path.join(
os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
"dataflows/data_cache",
),
# LLM settings
"mid_think_llm": "qwen3.5:27b", # falls back to quick_think_llm when None
"quick_think_llm": "qwen3.5:27b",
# LLM settings — all overridable via TRADINGAGENTS_<KEY> env vars
"llm_provider": _env("LLM_PROVIDER", "openai"),
"deep_think_llm": _env("DEEP_THINK_LLM", "gpt-5.2"),
"mid_think_llm": _env("MID_THINK_LLM"), # falls back to quick_think_llm when None
"quick_think_llm": _env("QUICK_THINK_LLM", "gpt-5-mini"),
"backend_url": _env("BACKEND_URL", "https://api.openai.com/v1"),
# Per-role provider overrides (fall back to llm_provider / backend_url when None)
"deep_think_llm_provider": "openrouter",
"deep_think_llm": "deepseek/deepseek-r1-0528",
"deep_think_backend_url": None, # uses OpenRouter's default URL
"mid_think_llm_provider": "ollama", # falls back to ollama
"mid_think_backend_url": "http://192.168.50.76:11434", # falls back to backend_url (ollama host)
"quick_think_llm_provider": "ollama", # falls back to ollama
"quick_think_backend_url": "http://192.168.50.76:11434", # falls back to backend_url (ollama host)
"deep_think_llm_provider": _env("DEEP_THINK_LLM_PROVIDER"), # e.g. "google", "anthropic", "openrouter"
"deep_think_backend_url": _env("DEEP_THINK_BACKEND_URL"), # override backend URL for deep-think model
"mid_think_llm_provider": _env("MID_THINK_LLM_PROVIDER"), # e.g. "ollama"
"mid_think_backend_url": _env("MID_THINK_BACKEND_URL"), # override backend URL for mid-think model
"quick_think_llm_provider": _env("QUICK_THINK_LLM_PROVIDER"), # e.g. "openai", "ollama"
"quick_think_backend_url": _env("QUICK_THINK_BACKEND_URL"), # override backend URL for quick-think model
# Provider-specific thinking configuration (applies to all roles unless overridden)
"google_thinking_level": None, # "high", "minimal", etc.
"openai_reasoning_effort": None, # "medium", "high", "low"
"google_thinking_level": _env("GOOGLE_THINKING_LEVEL"), # "high", "minimal", etc.
"openai_reasoning_effort": _env("OPENAI_REASONING_EFFORT"), # "medium", "high", "low"
# Per-role provider-specific thinking configuration
"deep_think_google_thinking_level": None,
"deep_think_openai_reasoning_effort": None,
"mid_think_google_thinking_level": None,
"mid_think_openai_reasoning_effort": None,
"quick_think_google_thinking_level": None,
"quick_think_openai_reasoning_effort": None,
"deep_think_google_thinking_level": _env("DEEP_THINK_GOOGLE_THINKING_LEVEL"),
"deep_think_openai_reasoning_effort": _env("DEEP_THINK_OPENAI_REASONING_EFFORT"),
"mid_think_google_thinking_level": _env("MID_THINK_GOOGLE_THINKING_LEVEL"),
"mid_think_openai_reasoning_effort": _env("MID_THINK_OPENAI_REASONING_EFFORT"),
"quick_think_google_thinking_level": _env("QUICK_THINK_GOOGLE_THINKING_LEVEL"),
"quick_think_openai_reasoning_effort": _env("QUICK_THINK_OPENAI_REASONING_EFFORT"),
# Debate and discussion settings
"max_debate_rounds": 2,
"max_risk_discuss_rounds": 2,
@ -35,11 +73,11 @@ DEFAULT_CONFIG = {
# Data vendor configuration
# Category-level configuration (default for all tools in category)
"data_vendors": {
"core_stock_apis": "yfinance", # Options: alpha_vantage, yfinance
"technical_indicators": "yfinance", # Options: alpha_vantage, yfinance
"fundamental_data": "yfinance", # Options: alpha_vantage, yfinance
"news_data": "yfinance", # Options: alpha_vantage, yfinance
"scanner_data": "alpha_vantage", # Options: alpha_vantage (primary), yfinance (fallback)
"core_stock_apis": _env("VENDOR_CORE_STOCK_APIS", "yfinance"),
"technical_indicators": _env("VENDOR_TECHNICAL_INDICATORS", "yfinance"),
"fundamental_data": _env("VENDOR_FUNDAMENTAL_DATA", "yfinance"),
"news_data": _env("VENDOR_NEWS_DATA", "yfinance"),
"scanner_data": _env("VENDOR_SCANNER_DATA", "yfinance"),
},
# Tool-level configuration (takes precedence over category-level)
"tool_vendors": {

49
tradingagents/graph/scanner_conditional_logic.py Normal file
View File

@ -0,0 +1,49 @@
"""Scanner conditional logic for determining continuation in scanner graph."""
from typing import Any
from tradingagents.agents.utils.scanner_states import ScannerState
_ERROR_PREFIXES = ("Error", "No data", "No quotes", "No movers", "No news", "No industry", "Invalid")
def _report_is_valid(report: str) -> bool:
"""Return True when *report* contains usable data (non-empty, non-error)."""
if not report or not report.strip():
return False
return not any(report.startswith(prefix) for prefix in _ERROR_PREFIXES)
class ScannerConditionalLogic:
"""Conditional logic for scanner graph flow control."""
def should_continue_geopolitical(self, state: ScannerState) -> bool:
"""
Determine if geopolitical scanning should continue.
Returns True only when the geopolitical report contains usable data.
"""
return _report_is_valid(state.get("geopolitical_report", ""))
def should_continue_movers(self, state: ScannerState) -> bool:
"""
Determine if market movers scanning should continue.
Returns True only when the market movers report contains usable data.
"""
return _report_is_valid(state.get("market_movers_report", ""))
def should_continue_sector(self, state: ScannerState) -> bool:
"""
Determine if sector scanning should continue.
Returns True only when the sector performance report contains usable data.
"""
return _report_is_valid(state.get("sector_performance_report", ""))
def should_continue_industry(self, state: ScannerState) -> bool:
"""
Determine if industry deep dive should continue.
Returns True only when the industry deep dive report contains usable data.
"""
return _report_is_valid(state.get("industry_deep_dive_report", ""))

7
tradingagents/graph/scanner_graph.py
View File

@ -139,9 +139,10 @@ class ScannerGraph:
}
if self.debug:
trace = []
# stream() yields partial state updates; use invoke() for the
# full accumulated state and print chunks for debugging only.
for chunk in self.graph.stream(initial_state):
trace.append(chunk)
return trace[-1] if trace else initial_state
print(f"[scanner debug] chunk keys: {list(chunk.keys())}")
# Fall through to invoke() for the correct accumulated result
return self.graph.invoke(initial_state)

24
tradingagents/llm_clients/TODO.md
View File

@ -1,24 +0,0 @@
# LLM Clients - Consistency Improvements
## Issues to Fix
### 1. `validate_model()` is never called
- Add validation call in `get_llm()` with warning (not error) for unknown models
### 2. Inconsistent parameter handling
| Client | API Key Param | Special Params |
|--------|---------------|----------------|
| OpenAI | `api_key` | `reasoning_effort` |
| Anthropic | `api_key` | `thinking_config` → `thinking` |
| Google | `google_api_key` | `thinking_budget` |
**Fix:** Standardize with unified `api_key` that maps to provider-specific keys
### 3. `base_url` accepted but ignored
- `AnthropicClient`: accepts `base_url` but never uses it
- `GoogleClient`: accepts `base_url` but never uses it (correct - Google doesn't support it)
**Fix:** Remove unused `base_url` from clients that don't support it
### 4. Update validators.py with models from CLI
- Sync `VALID_MODELS` dict with CLI model options after Feature 2 is complete

2
tradingagents/pipeline/macro_bridge.py
View File

@ -238,10 +238,10 @@ async def run_all_tickers(
List of TickerResult in completion order.
"""
semaphore = asyncio.Semaphore(max_concurrent)
loop = asyncio.get_event_loop()
async def _run_one(candidate: StockCandidate) -> TickerResult:
async with semaphore:
loop = asyncio.get_running_loop()
# TradingAgentsGraph is synchronous — run it in a thread pool
return await loop.run_in_executor(
None,