From 0017d5affe15140225fd62a4ea8c18e53ee9b196 Mon Sep 17 00:00:00 2001 From: Manav Chaudhary <120240843+manav4499@users.noreply.github.com> Date: Mon, 16 Feb 2026 19:17:02 -0500 Subject: [PATCH] updated the markdown --- README.md | 3 ++- docs/architecture.md | 28 ++++++++++++++++++++++++++++ docs/tradingAgents.md | 20 ++++++++++---------- 3 files changed, 40 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 136d0638..178fd377 100644 --- a/README.md +++ b/README.md @@ -1 +1,2 @@ - \ No newline at end of file +### TradeDog = LangGraph + Specialized Agents + Real-Time Loop + Paper Execution + diff --git a/docs/architecture.md b/docs/architecture.md index e69de29b..a05a9f86 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -0,0 +1,28 @@ +### High-Level Overview (The Big Picture) + +TradeDog = LangGraph + Specialized Agents + Real-Time Loop + Paper Execution + +Orchestration Engine: LangGraph (stateful graph of nodes/edges) — this is the "brain" that connects everything. +Core Loop: Runs every 15–60 minutes during market hours (or on-demand). +Key Innovation (Your Addition): Profit Guardian + Executor for autonomous profit-taking. +Modes: +Analysis Mode: Deep research for new entries. +Monitoring Mode: Real-time profit/exit checks (runs lighter, cheaper). +Backtest Mode: Historical simulation for tuning. + + + +Text Diagram (the mental model): + +[Market Data Feed (Alpaca + Polygon + Alpha Vantage)] + ↓ (ingest every cycle) +[Shared State (Portfolio, Positions, History, Signals)] + ↓ +[Agent Graph (LangGraph)] + ├── Analyst Team (parallel) + ├── Researcher Team (debate) + ├── Decision Maker + ├── Executor (Alpaca orders) + └── Profit Guardian (exit logic) + ↓ +[Output: Trade Actions + Dashboard Update] \ No newline at end of file diff --git a/docs/tradingAgents.md b/docs/tradingAgents.md index 34310010..fd191738 100644 --- a/docs/tradingAgents.md +++ b/docs/tradingAgents.md @@ -1,11 +1,11 @@

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arXiv Discord - WeChat + WeChat X Follow
Community @@ -56,7 +56,7 @@ TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and technical analysts, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions. Moreover, these agents engage in dynamic discussions to pinpoint the optimal strategy.

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> TradingAgents framework is designed for research purposes. Trading performance may vary based on many factors, including the chosen backbone language models, model temperature, trading periods, the quality of data, and other non-deterministic factors. [It is not intended as financial, investment, or trading advice.](https://tauric.ai/disclaimer/) @@ -70,21 +70,21 @@ Our framework decomposes complex trading tasks into specialized roles. This ensu - Technical Analyst: Utilizes technical indicators (like MACD and RSI) to detect trading patterns and forecast price movements.

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### Researcher Team - Comprises both bullish and bearish researchers who critically assess the insights provided by the Analyst Team. Through structured debates, they balance potential gains against inherent risks.

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### Trader Agent - Composes reports from the analysts and researchers to make informed trading decisions. It determines the timing and magnitude of trades based on comprehensive market insights.

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### Risk Management and Portfolio Manager @@ -92,7 +92,7 @@ Our framework decomposes complex trading tasks into specialized roles. This ensu - The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed.

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## Installation and CLI @@ -145,17 +145,17 @@ python -m cli.main You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc.

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An interface will appear showing results as they load, letting you track the agent's progress as it runs.

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## TradingAgents Package