docs: add data types, sizes, and context window budget to agent_dataflow.md
Add per-agent Prompt Size Budget tables (all 17 agents) with data type, format, and token estimates. Add three new reference sections: - Section 7: Tool Data Formats & Sizes (20 tools with format, size, limits) - Section 8: Context Window Budget (model reference + per-agent risk table) - Section 9: End-to-End Token Estimates (trading ~98K, scanner ~13K tokens) Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
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@ -34,6 +34,9 @@ used by every agent.
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- [4.17 Macro Synthesis](#417-macro-synthesis)
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5. [Tool → Data-Source Mapping](#5-tool--data-source-mapping)
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6. [Memory System](#6-memory-system)
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7. [Tool Data Formats & Sizes](#7-tool-data-formats--sizes)
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8. [Context Window Budget](#8-context-window-budget)
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9. [End-to-End Token Estimates](#9-end-to-end-token-estimates)
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---
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@ -320,6 +323,16 @@ Each subsection follows the same structure:
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└─────────────────────────────────────────────────────┘
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```
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**Prompt Size Budget:**
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| Component | Data Type | Format | Avg Size | Avg Tokens |
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|-----------|-----------|--------|----------|------------|
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| System prompt | Text | Instructions + indicator list | ~2.1 KB | ~525 |
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| `get_macro_regime` result | Markdown | Tables (regime + 6 signals) | ~0.8 KB | ~200 |
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| `get_stock_data` result (30 days) | CSV | Header + OHLCV rows | ~5 KB | ~1,250 |
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| `get_indicators` × 8 calls | Markdown | Daily values + description | ~7.2 KB | ~1,800 |
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| **Total prompt** | | | **~15–20 KB** | **~3,750–5,000** |
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---
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### 4.2 Fundamentals Analyst
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@ -378,6 +391,21 @@ Each subsection follows the same structure:
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Output: fundamentals_report
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```
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**Prompt Size Budget:**
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| Component | Data Type | Format | Avg Size | Avg Tokens |
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|-----------|-----------|--------|----------|------------|
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| System prompt | Text | Sequence instructions + metric list | ~1.4 KB | ~350 |
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| `get_ttm_analysis` result | Markdown | Tables (TTM summary + 8-quarter history) | ~1.6 KB | ~400 |
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| `get_fundamentals` result | Markdown | Key ratios table (~15 metrics) | ~1.5 KB | ~375 |
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| `get_peer_comparison` result | Markdown | Ranked table (~10 peers × 6 horizons) | ~1.2 KB | ~300 |
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| `get_sector_relative` result | Markdown | Alpha table (5–6 time periods) | ~0.8 KB | ~200 |
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| `get_balance_sheet` (optional) | CSV | Quarterly rows (up to 8) | ~2.5 KB | ~625 |
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| `get_cashflow` (optional) | CSV | Quarterly rows (up to 8) | ~2.5 KB | ~625 |
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| `get_income_statement` (optional) | CSV | Quarterly rows (up to 8) | ~2.5 KB | ~625 |
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| **Total prompt (core)** | | | **~6.5 KB** | **~1,625** |
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| **Total prompt (with optionals)** | | | **~14 KB** | **~3,500** |
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---
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### 4.3 News Analyst
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@ -422,6 +450,16 @@ Each subsection follows the same structure:
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Output: news_report
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```
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**Prompt Size Budget:**
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| Component | Data Type | Format | Avg Size | Avg Tokens |
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|-----------|-----------|--------|----------|------------|
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| System prompt | Text | Instructions | ~0.75 KB | ~187 |
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| `get_news` result | Markdown | Article list (≤ 20 articles) | ~7 KB | ~1,750 |
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| `get_global_news` result | Markdown | Article list (5 articles) | ~1.75 KB | ~437 |
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| `get_insider_transactions` result | Markdown | Transaction table (10–50 rows) | ~1.5 KB | ~375 |
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| **Total prompt** | | | **~11 KB** | **~2,750** |
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---
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### 4.4 Social Media Analyst
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@ -454,6 +492,14 @@ Each subsection follows the same structure:
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Output: sentiment_report
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```
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**Prompt Size Budget:**
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| Component | Data Type | Format | Avg Size | Avg Tokens |
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|-----------|-----------|--------|----------|------------|
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| System prompt | Text | Instructions | ~0.85 KB | ~212 |
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| `get_news` result | Markdown | Article list (≤ 20 articles) | ~7 KB | ~1,750 |
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| **Total prompt** | | | **~8 KB** | **~2,000** |
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---
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### 4.5 Bull Researcher
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@ -513,6 +559,19 @@ Each subsection follows the same structure:
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└─────────────────────────────────────────────────────┘
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```
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**Prompt Size Budget:**
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| Component | Data Type | Format | Avg Size (Rd 1) | Avg Size (Rd 2) | Avg Tokens (Rd 2) |
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|-----------|-----------|--------|-----------------|-----------------|-------------------|
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| Prompt template | Text | f-string with instructions | ~1.2 KB | ~1.2 KB | ~300 |
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| 4 analyst reports | Text | Concatenated Markdown | ~13 KB | ~13 KB | ~3,250 |
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| Debate history | Text | Accumulated transcript | ~0 KB | ~6 KB | ~1,500 |
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| Last Bear argument | Text | Debate response | ~0 KB | ~2 KB | ~500 |
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| Memory (2 matches) | Text | Past situations + advice | ~4 KB | ~4 KB | ~1,000 |
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| **Total prompt** | | | **~18 KB** | **~26 KB** | **~6,550** |
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> Prompt grows ~8 KB per debate round as history accumulates.
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---
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### 4.6 Bear Researcher
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@ -553,6 +612,10 @@ Each subsection follows the same structure:
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• investment_debate_state.count (incremented)
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```
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**Prompt Size Budget:** Same structure as Bull Researcher (see 4.5).
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Round 1 ≈ 18 KB (~4,500 tokens), Round 2 ≈ 26 KB (~6,550 tokens).
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Grows ~8 KB per round.
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---
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### 4.7 Research Manager
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@ -605,6 +668,20 @@ Each subsection follows the same structure:
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└─────────────────────────────────────────────────────┘
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```
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**Prompt Size Budget (after 2 debate rounds):**
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| Component | Data Type | Format | Avg Size | Avg Tokens |
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|-----------|-----------|--------|----------|------------|
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| System prompt | Text | Instructions | ~1.2 KB | ~300 |
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| 4 analyst reports | Text | Concatenated Markdown | ~13 KB | ~3,250 |
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| Full debate transcript | Text | Bull + Bear history (2 rounds) | ~20 KB | ~5,000 |
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| Macro regime report | Markdown | Regime + signals table | ~0.8 KB | ~200 |
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| Memory (2 matches) | Text | Past decisions + outcomes | ~4 KB | ~1,000 |
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| **Total prompt** | | | **~39 KB** | **~9,750** |
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> This is the **largest single-prompt agent** in the trading pipeline.
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> With 3 debate rounds, prompt can reach ~50 KB (~12,500 tokens).
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---
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### 4.8 Trader
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@ -644,6 +721,16 @@ Each subsection follows the same structure:
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• sender = "Trader"
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```
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**Prompt Size Budget:**
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| Component | Data Type | Format | Avg Size | Avg Tokens |
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|-----------|-----------|--------|----------|------------|
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| System message | Text | Instructions + memory | ~0.6 KB | ~150 |
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| Investment plan | Text | Research Manager output | ~3 KB | ~750 |
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| 4 analyst reports | Text | Concatenated Markdown | ~13 KB | ~3,250 |
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| Memory (2 matches) | Text | Past decisions + outcomes | ~4 KB | ~1,000 |
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| **Total prompt** | | | **~21 KB** | **~5,150** |
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---
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### 4.9 Aggressive Debator
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@ -681,6 +768,17 @@ Each subsection follows the same structure:
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• risk_debate_state.count (incremented)
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```
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**Prompt Size Budget:**
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| Component | Data Type | Format | Avg Size (Rd 1) | Avg Size (Rd 2) | Avg Tokens (Rd 2) |
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|-----------|-----------|--------|-----------------|-----------------|-------------------|
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| Prompt template | Text | f-string with instructions | ~1.2 KB | ~1.2 KB | ~300 |
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| 4 analyst reports | Text | Concatenated Markdown | ~13 KB | ~13 KB | ~3,250 |
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| Trader investment plan | Text | Decision + reasoning | ~3 KB | ~3 KB | ~750 |
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| Risk debate history | Text | Accumulated transcript | ~0 KB | ~10 KB | ~2,500 |
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| Conservative/Neutral args | Text | Debate responses | ~0 KB | ~4 KB | ~1,000 |
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| **Total prompt** | | | **~17 KB** | **~31 KB** | **~7,800** |
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---
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### 4.10 Conservative Debator
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@ -705,7 +803,7 @@ Each subsection follows the same structure:
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LLM Prompt (quick_think):
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"Protect assets, minimize volatility. Critically
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examine high-risk elements. Counter aggressive and
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neutral points. Emphasise downsides. Debate to
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neutral points. Emphasize downsides. Debate to
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demonstrate strength of low-risk strategy."
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│
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▼
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@ -715,6 +813,9 @@ Each subsection follows the same structure:
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• risk_debate_state.count (incremented)
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```
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**Prompt Size Budget:** Same structure as Aggressive Debator (see 4.9).
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Round 1 ≈ 17 KB (~4,250 tokens), Round 2 ≈ 31 KB (~7,800 tokens).
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---
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### 4.11 Neutral Debator
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@ -749,6 +850,9 @@ Each subsection follows the same structure:
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• risk_debate_state.count (incremented)
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```
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**Prompt Size Budget:** Same structure as Aggressive Debator (see 4.9).
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Round 1 ≈ 17 KB (~4,250 tokens), Round 2 ≈ 31 KB (~7,800 tokens).
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---
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### 4.12 Risk Manager
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@ -794,6 +898,21 @@ Each subsection follows the same structure:
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• final_trade_decision (the system's final answer)
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```
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**Prompt Size Budget (after 2 risk-debate rounds):**
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| Component | Data Type | Format | Avg Size | Avg Tokens |
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|-----------|-----------|--------|----------|------------|
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| System prompt | Text | Instructions | ~1.3 KB | ~325 |
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| 4 analyst reports | Text | Concatenated Markdown | ~13 KB | ~3,250 |
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| Trader investment plan | Text | Decision + reasoning | ~3 KB | ~750 |
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| Full risk debate transcript | Text | Aggressive + Conservative + Neutral (2 rds) | ~30 KB | ~7,500 |
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| Macro regime report | Markdown | Regime + signals table | ~0.8 KB | ~200 |
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| Memory (2 matches) | Text | Past risk decisions + outcomes | ~4 KB | ~1,000 |
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| **Total prompt** | | | **~52 KB** | **~13,025** |
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> **Largest prompt in the entire framework.** With 3 risk-debate rounds,
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> this can reach ~70 KB (~17,500 tokens).
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---
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### 4.13 Geopolitical Scanner
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@ -841,6 +960,14 @@ Each subsection follows the same structure:
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Output: geopolitical_report
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```
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**Prompt Size Budget:**
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| Component | Data Type | Format | Avg Size | Avg Tokens |
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|-----------|-----------|--------|----------|------------|
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| System prompt | Text | Instructions | ~0.6 KB | ~150 |
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| `get_topic_news` × 3–4 calls | Markdown | Article lists (10 articles each) | ~8 KB | ~2,000 |
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| **Total prompt** | | | **~9 KB** | **~2,150** |
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---
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### 4.14 Market Movers Scanner
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@ -888,6 +1015,15 @@ Each subsection follows the same structure:
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Output: market_movers_report
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```
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**Prompt Size Budget:**
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| Component | Data Type | Format | Avg Size | Avg Tokens |
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|-----------|-----------|--------|----------|------------|
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| System prompt | Text | Instructions | ~0.6 KB | ~150 |
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| `get_market_movers` × 3 calls | Markdown | Tables (15 stocks each) | ~4.5 KB | ~1,125 |
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| `get_market_indices` result | Markdown | Table (5 indices) | ~1 KB | ~250 |
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| **Total prompt** | | | **~6 KB** | **~1,525** |
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---
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### 4.15 Sector Scanner
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@ -924,6 +1060,16 @@ Each subsection follows the same structure:
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Output: sector_performance_report
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```
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**Prompt Size Budget:**
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| Component | Data Type | Format | Avg Size | Avg Tokens |
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|-----------|-----------|--------|----------|------------|
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| System prompt | Text | Instructions | ~0.5 KB | ~125 |
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| `get_sector_performance` result | Markdown | Table (11 sectors × 4 horizons) | ~0.9 KB | ~220 |
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| **Total prompt** | | | **~1.4 KB** | **~345** |
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> Smallest prompt of any scanner agent.
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---
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### 4.16 Industry Deep Dive
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@ -988,6 +1134,16 @@ Each subsection follows the same structure:
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Output: industry_deep_dive_report
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```
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**Prompt Size Budget:**
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| Component | Data Type | Format | Avg Size | Avg Tokens |
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|-----------|-----------|--------|----------|------------|
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| System prompt | Text | Instructions + sector list | ~1 KB | ~250 |
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| Phase 1 context (3 reports) | Text | Concatenated Markdown | ~6 KB | ~1,500 |
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| `get_industry_performance` × 3 | Markdown | Tables (10–15 companies each) | ~7.5 KB | ~1,875 |
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| `get_topic_news` × 2 | Markdown | Article lists (10 articles each) | ~5 KB | ~1,250 |
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| **Total prompt** | | | **~20 KB** | **~4,875** |
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---
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### 4.17 Macro Synthesis
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@ -1034,6 +1190,19 @@ Each subsection follows the same structure:
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Output: macro_scan_summary (JSON string)
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```
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**Prompt Size Budget:**
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| Component | Data Type | Format | Avg Size | Avg Tokens |
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|-----------|-----------|--------|----------|------------|
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| System prompt | Text | Instructions + JSON schema | ~1.3 KB | ~325 |
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| Geopolitical report (Phase 1) | Text | Markdown report | ~3 KB | ~750 |
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| Market movers report (Phase 1) | Text | Markdown report | ~3 KB | ~750 |
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| Sector performance report (Phase 1) | Text | Markdown report | ~2 KB | ~500 |
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| Industry deep dive report (Phase 2) | Text | Markdown report | ~8 KB | ~2,000 |
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| **Total prompt** | | | **~17 KB** | **~4,325** |
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**Output:** Valid JSON (~3–5 KB, ~750–1,250 tokens).
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---
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## 5. Tool → Data-Source Mapping
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@ -1118,3 +1287,220 @@ to retrieve relevant past trading situations.
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→ Appends to document store
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→ Rebuilds BM25 index for future retrieval
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```
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---
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## 7. Tool Data Formats & Sizes
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All tools return **strings** to the LLM. The table below shows the format,
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typical size, and any truncation limits for each tool.
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> **Token estimate rule of thumb:** 1 token ≈ 4 characters for English text.
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### Trading Tools
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| Tool | Return Format | Typical Size | Tokens | Items | Truncation / Limits |
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|------|---------------|-------------|--------|-------|---------------------|
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| `get_stock_data` | CSV (header + OHLCV rows) | 5–20 KB | 1,250–5,000 | 30–250 rows | None; all requested days returned |
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| `get_indicators` | Markdown (daily values + description) | ~0.9 KB per indicator | ~225 | 30 daily values | 30-day lookback (configurable) |
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| `get_macro_regime` | Markdown (regime table + 6 signal rows) | ~0.8 KB | ~200 | 1 regime + 6 signals | Fixed signal set |
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| `get_fundamentals` | Markdown (key ratios table) | ~1.5 KB | ~375 | ~15 metrics | None |
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| `get_ttm_analysis` | Markdown (TTM summary + 8-quarter table) | ~1.6 KB | ~400 | 15 metrics + 8 quarters | Last 8 quarters |
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| `get_balance_sheet` | CSV (quarterly columns) | ~2.5 KB | ~625 | Up to 8 quarters | Last 8 quarters |
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| `get_income_statement` | CSV (quarterly columns) | ~2.5 KB | ~625 | Up to 8 quarters | Last 8 quarters |
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| `get_cashflow` | CSV (quarterly columns) | ~2.5 KB | ~625 | Up to 8 quarters | Last 8 quarters |
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| `get_peer_comparison` | Markdown (ranked table) | ~1.2 KB | ~300 | ~10 peers | Top 10 sector peers |
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| `get_sector_relative` | Markdown (alpha table) | ~0.8 KB | ~200 | 5–6 time periods | Fixed periods |
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| `get_news` | Markdown (article list) | ~7 KB | ~1,750 | ≤ 20 articles | First 20 from API, filtered by date |
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| `get_global_news` | Markdown (article list) | ~1.75 KB | ~437 | 5 articles (default) | Configurable limit; deduplicated |
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| `get_insider_transactions` | Markdown (transaction table) | ~1.5 KB | ~375 | 10–50 transactions | API-dependent |
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### Scanner Tools
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| Tool | Return Format | Typical Size | Tokens | Items | Truncation / Limits |
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|------|---------------|-------------|--------|-------|---------------------|
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| `get_market_movers` | Markdown (table) | ~1.5 KB per category | ~375 | 15 stocks | Hard limit: top 15 |
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| `get_market_indices` | Markdown (table) | ~1 KB | ~250 | 5 indices | Fixed set (SPY, DJI, NASDAQ, VIX, RUT) |
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| `get_sector_performance` | Markdown (table) | ~0.9 KB | ~220 | 11 sectors × 4 horizons | Fixed 11 GICS sectors |
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| `get_industry_performance` | Markdown (table) | ~2.5 KB | ~625 | 10–15 companies | Top companies by market weight |
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| `get_topic_news` | Markdown (article list) | ~2.5 KB | ~625 | 10 articles (default) | Configurable limit |
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| `get_earnings_calendar` | Markdown (table) | ~3 KB | ~750 | 20–50+ events | All events in date range |
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| `get_economic_calendar` | Markdown (table) | ~2.5 KB | ~625 | 5–15 events | All events in date range |
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### Non-Tool Data Injected into Prompts
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| Data | Format | Avg Size | Tokens | Notes |
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|------|--------|----------|--------|-------|
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| Memory match (× 2) | Text (situation + recommendation) | ~2 KB each | ~500 each | BM25 retrieval; injected as "Past Reflections" |
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| Debate history (per round) | Text (accumulated transcript) | ~3–4 KB per turn | ~750–1,000 | Grows linearly with debate rounds |
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| Analyst report (each) | Text (Markdown) | ~3 KB | ~750 | Output from analyst agents |
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| Macro regime report | Markdown (tables) | ~0.8 KB | ~200 | Shared across multiple agents |
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---
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## 8. Context Window Budget
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This section compares each agent's **estimated prompt size** against
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the context windows of popular models to identify potential overflow risks.
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### Model Context Windows (Reference)
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| Model | Context Window | Input Limit (approx) | Notes |
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|-------|---------------|---------------------|-------|
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| gpt-4o-mini | 128K tokens | ~100K usable | Default quick-think candidate |
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| gpt-4o | 128K tokens | ~100K usable | Alternative quick/mid |
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| gpt-5-mini | 128K tokens | ~100K usable | Default `quick_think_llm` |
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| gpt-5.2 | 128K tokens | ~100K usable | Default `deep_think_llm` |
|
||||
| claude-3.5-sonnet | 200K tokens | ~180K usable | Anthropic option |
|
||||
| claude-4-sonnet | 200K tokens | ~180K usable | Anthropic option |
|
||||
| gemini-2.5-pro | 1M tokens | ~900K usable | Google option |
|
||||
| deepseek-r1 | 128K tokens | ~100K usable | OpenRouter / Ollama option |
|
||||
| llama-3.1-70b | 128K tokens | ~100K usable | Ollama local option |
|
||||
| mistral-large | 128K tokens | ~100K usable | OpenRouter option |
|
||||
|
||||
### Per-Agent Prompt Size vs Context Budget
|
||||
|
||||
| # | Agent | Tier | Avg Prompt | Peak Prompt† | % of 128K | Risk |
|
||||
|---|-------|------|-----------|-------------|-----------|------|
|
||||
| 1 | Market Analyst | Quick | ~5,000 tok | ~6,000 tok | 4–5% | ✅ Safe |
|
||||
| 2 | Fundamentals Analyst | Quick | ~1,600 tok | ~3,500 tok | 1–3% | ✅ Safe |
|
||||
| 3 | News Analyst | Quick | ~2,750 tok | ~3,200 tok | 2–3% | ✅ Safe |
|
||||
| 4 | Social Media Analyst | Quick | ~2,000 tok | ~2,500 tok | 1–2% | ✅ Safe |
|
||||
| 5 | Bull Researcher (Rd 2) | Mid | ~6,550 tok | ~10,000 tok | 5–8% | ✅ Safe |
|
||||
| 6 | Bear Researcher (Rd 2) | Mid | ~6,550 tok | ~10,000 tok | 5–8% | ✅ Safe |
|
||||
| 7 | **Research Manager** | **Deep** | **~9,750 tok** | **~15,000 tok** | **8–12%** | ✅ Safe |
|
||||
| 8 | Trader | Mid | ~5,150 tok | ~6,500 tok | 4–5% | ✅ Safe |
|
||||
| 9 | Aggressive Debator (Rd 2) | Quick | ~7,800 tok | ~14,000 tok | 6–11% | ✅ Safe |
|
||||
| 10 | Conservative Debator (Rd 2) | Quick | ~7,800 tok | ~14,000 tok | 6–11% | ✅ Safe |
|
||||
| 11 | Neutral Debator (Rd 2) | Quick | ~7,800 tok | ~14,000 tok | 6–11% | ✅ Safe |
|
||||
| 12 | **Risk Manager** | **Deep** | **~13,000 tok** | **~17,500 tok** | **10–14%** | ✅ Safe |
|
||||
| 13 | Geopolitical Scanner | Quick | ~2,150 tok | ~3,000 tok | 2% | ✅ Safe |
|
||||
| 14 | Market Movers Scanner | Quick | ~1,525 tok | ~2,000 tok | 1–2% | ✅ Safe |
|
||||
| 15 | Sector Scanner | Quick | ~345 tok | ~500 tok | <1% | ✅ Safe |
|
||||
| 16 | Industry Deep Dive | Mid | ~4,875 tok | ~7,000 tok | 4–5% | ✅ Safe |
|
||||
| 17 | Macro Synthesis | Deep | ~4,325 tok | ~6,500 tok | 3–5% | ✅ Safe |
|
||||
|
||||
> **†Peak Prompt** = estimate with `max_debate_rounds=3` or maximum optional
|
||||
> tool calls. All agents are well within the 128K context window.
|
||||
|
||||
### When to Watch Context Limits
|
||||
|
||||
Even though individual agents fit comfortably, be aware of these scenarios:
|
||||
|
||||
| Scenario | Estimated Total | Risk |
|
||||
|----------|----------------|------|
|
||||
| Default config (2 debate rounds) | Max single prompt ≈ 17.5K tokens | ✅ No risk |
|
||||
| `max_debate_rounds=5` | Risk Manager prompt ≈ 30K tokens | ✅ Low risk |
|
||||
| `max_debate_rounds=10` | Risk Manager prompt ≈ 55K tokens | ⚠️ Monitor |
|
||||
| Small context model (8K window) | Risk Manager default already 13K | ❌ **Will overflow** |
|
||||
| Ollama local (small model, 4K ctx) | Most debate agents exceed 4K | ❌ **Will overflow** |
|
||||
|
||||
> **Recommendation:** For local Ollama models with small context windows
|
||||
> (e.g., 4K–8K), set `max_debate_rounds=1` and `max_risk_discuss_rounds=1`.
|
||||
|
||||
---
|
||||
|
||||
## 9. End-to-End Token Estimates
|
||||
|
||||
### Trading Pipeline (Single Company Analysis)
|
||||
|
||||
```
|
||||
Phase Calls Avg Tokens (per call) Subtotal
|
||||
─────────────────────────────────────────────────────────────────────────
|
||||
1. ANALYST PHASE (parallel)
|
||||
Market Analyst 1 ~5,000 ~5,000
|
||||
Fundamentals Analyst 1 ~1,600–3,500 ~2,500
|
||||
News Analyst 1 ~2,750 ~2,750
|
||||
Social Media Analyst 1 ~2,000 ~2,000
|
||||
Phase 1: ~12,250
|
||||
|
||||
2. INVESTMENT DEBATE (2 rounds)
|
||||
Bull Researcher 2 ~4,500 → ~6,550 ~11,050
|
||||
Bear Researcher 2 ~4,500 → ~6,550 ~11,050
|
||||
Phase 2: ~22,100
|
||||
|
||||
3. RESEARCH MANAGER
|
||||
Research Manager 1 ~9,750 ~9,750
|
||||
Phase 3: ~9,750
|
||||
|
||||
4. TRADER
|
||||
Trader 1 ~5,150 ~5,150
|
||||
Phase 4: ~5,150
|
||||
|
||||
5. RISK DEBATE (2 rounds × 3 agents)
|
||||
Aggressive Debator 2 ~4,250 → ~7,800 ~12,050
|
||||
Conservative Debator 2 ~4,250 → ~7,800 ~12,050
|
||||
Neutral Debator 2 ~4,250 → ~7,800 ~12,050
|
||||
Phase 5: ~36,150
|
||||
|
||||
6. RISK MANAGER
|
||||
Risk Manager 1 ~13,000 ~13,000
|
||||
Phase 6: ~13,000
|
||||
|
||||
═══════════════════════════════════════════════════════════════════════════
|
||||
TOTAL INPUT TOKENS (single company): ~98,400
|
||||
═══════════════════════════════════════════════════════════════════════════
|
||||
```
|
||||
|
||||
> Each agent also produces **output tokens** (~500–3,000 per call).
|
||||
> Total output across all agents ≈ 15,000–25,000 tokens.
|
||||
> **Grand total (input + output) ≈ 115,000–125,000 tokens per company.**
|
||||
|
||||
### Scanner Pipeline (Market-Wide Scan)
|
||||
|
||||
```
|
||||
Phase Calls Avg Tokens (per call) Subtotal
|
||||
─────────────────────────────────────────────────────────────────────────
|
||||
1. PHASE 1 SCANNERS (parallel)
|
||||
Geopolitical Scanner 1 ~2,150 ~2,150
|
||||
Market Movers Scanner 1 ~1,525 ~1,525
|
||||
Sector Scanner 1 ~345 ~345
|
||||
Phase 1: ~4,020
|
||||
|
||||
2. PHASE 2
|
||||
Industry Deep Dive 1 ~4,875 ~4,875
|
||||
Phase 2: ~4,875
|
||||
|
||||
3. PHASE 3
|
||||
Macro Synthesis 1 ~4,325 ~4,325
|
||||
Phase 3: ~4,325
|
||||
|
||||
═══════════════════════════════════════════════════════════════════════════
|
||||
TOTAL INPUT TOKENS (market scan): ~13,220
|
||||
═══════════════════════════════════════════════════════════════════════════
|
||||
```
|
||||
|
||||
> Scanner output tokens ≈ 5,000–8,000 additional.
|
||||
> **Grand total (input + output) ≈ 18,000–21,000 tokens per scan.**
|
||||
|
||||
### Full Pipeline (Scan → Per-Ticker Deep Dives)
|
||||
|
||||
When running the `pipeline` command (scan + per-ticker analysis for top picks):
|
||||
|
||||
```
|
||||
Scanner pipeline: ~13,220 input tokens
|
||||
+ N company analyses (N = 8–10 picks): ~98,400 × N input tokens
|
||||
───────────────────────────────────────────────────────────────────
|
||||
Example (10 companies): ~997,220 input tokens
|
||||
≈ 1.0M total tokens (input + output)
|
||||
```
|
||||
|
||||
### Key Observations
|
||||
|
||||
1. **No automatic truncation**: The framework concatenates all tool output
|
||||
and debate history into prompts without truncation. Context usage grows
|
||||
linearly with debate rounds.
|
||||
|
||||
2. **Debate history is the main driver**: In a 2-round debate, history adds
|
||||
~8 KB per round per debater. The Risk Manager sees all three debaters'
|
||||
accumulated history.
|
||||
|
||||
3. **All prompts fit 128K models**: Even the largest prompt (Risk Manager
|
||||
at peak) uses only ~14% of a 128K context window.
|
||||
|
||||
4. **Small-context models are at risk**: Models with ≤ 8K context windows
|
||||
cannot accommodate debate agents beyond round 1. Use
|
||||
`max_debate_rounds=1` for such models.
|
||||
|
||||
5. **Cost optimization**: The scanner pipeline uses ~13K tokens total —
|
||||
roughly 7× cheaper than a single company analysis.
|
||||
|
|
|
|||
Loading…
Reference in New Issue