🤖 SEMANTIC SIGNAL LLM (LLM Trader)

An autonomous AI trading agent that reads charts, remembers outcomes, and sharpens its strategy in real time.

📊 Live Dashboard — Watch the neural trading brain in action
📖 Read the Full Story (Medium)
💬 Join the Discord

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💡 Paper trading by default. A real exchange execution service (llm_trader_executor) is currently in testing — it consumes this bot's decisions and places live CCXT orders. Coming soon. Stay tuned.

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Quick Start

git clone https://github.com/qrak/LLM_trader.git && cd LLM_trader
python -m venv .venv && source .venv/bin/activate  # or .venv\Scripts\Activate.ps1 on Windows
pip install -r requirements.txt
cp keys.env.example keys.env  # add your API keys (Google Gemini free tier works)
python start.py               # dashboard at http://localhost:8000

Detailed setup for Windows, Linux, macOS →

Platform-specific scripts live in scripts/ — they handle venv creation, dependency install, and startup:

Script	Purpose
scripts/start_script_main.ps1	Start the bot (Windows)
scripts/start_script_main_linux.sh	Start the bot (Linux)
scripts/start_script_main_macos.sh	Start the bot (macOS)
scripts/run_all_tests.sh	Run full test suite in .venv
scripts/query_trade_history.py	CLI utility to inspect SQLite trade history

Runtime Controls

Key	Action
a	Force analysis — run immediate market check
d	Toggle dashboard on/off
h	Help — show available commands
q	Quit — graceful shutdown with state preservation

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📸 Screenshots

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Features

• 🧠 Brain with Memory — ChromaDB vector store retains trade experiences, semantic rules, and system rejections. Past outcomes are retrieved by similarity to current market conditions and injected into every LLM prompt.
• 📈 Vision AI Chart Analysis — Generates 4K PNG candlestick charts with indicators, sends them to a multimodal LLM (Gemini 3.5 Flash) for visual pattern recognition. Chart-pattern code was dropped because the AI reads charts better than hardcoded rules.
• 🔄 Reflection Engine — After every N closed trades, the system synthesizes best-practice rules, anti-patterns, and AI-mistake rules. These persist in vector memory and influence future decisions — the bot learns from its own outcomes.
• ✅ Claim Validation — Every LLM response is cross-checked against computed indicators. Reported trend strength is compared against actual ADX; pattern quality is replaced by a deterministic scorer. No blind trust in AI numeric claims.
• 📰 RAG News Engine — Aggregates crypto news from free RSS feeds (CoinDesk, CoinTelegraph, Decrypt, CryptoSlate) with optional Crawl4AI enrichment, plus fundamentals from DeFiLlama.
• 📊 Live Dashboard — FastAPI + WebSocket real-time UI at 0.0.0.0:8000 (or semanticsignal.qrak.org). Shows brain activity, last prompt/response, position state, performance stats, news, market data, and memory bank.
• 🛡️ Risk Pipeline — Pre-execution guard chain (symbol whitelist, max position size, cooldown) + dynamic SL/TP scaling with minimum 1.5 R:R enforced. Soft exits at candle close, hard exits at configurable intervals against live ticker price.
• 🔄 Multi-Provider AI Routing — Primary: Google Gemini 3.5 Flash (free tier). Fallback chain through OpenRouter and LM Studio. Chart vision support on every provider that allows it.
• 🧪 900+ Tests — Fully mocked test suite covering LLM output corruption, async races, rate-limit backoff, vector-DB boundaries, friction-reporting, and closed-loop feedback.

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Architecture

flowchart TB
    subgraph Data["Data Sources"]
        EX["Exchanges (CCXT) → OHLCV + Order Book + Trade Flow"]
        NEWS["RSS Feeds + Crawl4AI"]
        FUND["CoinGecko + DeFiLlama + Alternative.me"]
    end
    subgraph Analysis["Analysis Engine"]
        TC["Technical Calculator<br/>40+ indicators"]
        PE["Pattern Engine<br/>Deterministic indicator patterns"]
        CG["Chart Generator<br/>4K PNG with SMA/RSI/Volume"]
        RAG["RAG Engine<br/>News relevance scoring"]
    end
    subgraph Brain["🧠 Brain Layer"]
        VM["Vector Memory<br/>ChromaDB (3 collections)"]
        REFL["Reflection Engine<br/>Rules from closed trades"]
        CTX["Context Builder<br/>Similarity retrieval + confidence calibration"]
    end
    subgraph Execution["Paper Execution"]
        RP["Risk Manager<br/>SL/TP, sizing, R:R"]
        GP["Guard Pipeline<br/>Symbol → Size → Cooldown"]
        STRAT["Trading Strategy<br/>ExitMonitor + PositionStatusMonitor"]
    end
    Data --> Analysis
    Analysis --> Brain
    Brain --> AI["AI Provider<br/>(Gemini / OpenRouter / LM Studio)"]
    AI --> RP --> GP --> STRAT
    STRAT -.->|Closed trade feedback| Brain

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Key Files

Path	Role
start.py	Entry point — 8-stage dependency injection, ChromaDB + CoinGecko cache setup
src/app.py	CryptoTradingBot — main async loop, ticker fetch, analysis orchestration
src/trading/brain.py	TradingBrainService — context assembly, experience recording, reflection triggers
src/trading/vector_memory.py	ChromaDB interface — trade experiences, semantic rules, blocked trades
src/analyzer/analysis_engine.py	Market analysis orchestration — indicators, chart, RAG, LLM call
src/managers/provider_orchestrator.py	AI provider fallback chain with retry logic
src/managers/risk_manager.py	Dynamic SL/TP, position sizing, friction tracking
src/trading/trading_strategy.py	Position lifecycle, guard enforcement, exit monitoring
src/analyzer/prompts/template_manager.py	System prompt construction with falsification-based invalidation step
src/analyzer/trend_validator.py	Cross-checks LLM-reported trend strength against computed ADX
src/analyzer/pattern_quality_scorer.py	Deterministic pattern quality scoring replacing LLM's self-reported score
src/notifiers/notifier.py	Discord notifications with message expiration tracking

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Testing

# Full suite (900+ tests)
pytest tests/ -q

# Focused
pytest tests/test_ticker_retry.py tests/test_brain_integration.py -q

# Linting
ruff check src tests start.py

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Configuration

Key settings in config/config.ini:

Setting	Default	Description
crypto_pair	BTC/USDC	Trading pair
timeframe	4h	Analysis candle timeframe
provider	googleai	AI provider (googleai, openrouter, lmstudio)
demo_quote_capital	10000	Simulated capital
max_position_size	0.10	Max position as fraction of capital
stop_loss_type	hard	hard (interval check) or soft (candle close)

Required API keys in keys.env:

Variable	Required	For
GOOGLE_STUDIO_API_KEY	Yes	Primary AI provider (free tier)
GOOGLE_STUDIO_PAID_API_KEY	If used	Paid tier Google AI
OPENROUTER_API_KEY	If used	Secondary AI provider
BOT_TOKEN_DISCORD	If used	Discord notifications
MAIN_CHANNEL_ID	If used	Discord notification channel
COINGECKO_API_KEY	No	Market metrics (rate limit boost)
HF_TOKEN	No	HuggingFace model access

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Roadmap

• Multiple Trading Agent Personalities — Conservative, aggressive, contrarian, trend-following strategists (aspirational)
• Multi-Model Consensus — "Council of Models" architecture for collective decision-making (aspirational)
• Live Trading — Real exchange order execution via llm_trader_executor — currently in testing, coming soon (plan at .ai/plans/real_trading_implementation_plan.md)
• Admin Dashboard — Web GUI for bot configuration (replaces manual config.ini editing) (plan at .ai/plans/admin-dashboard-plan.md)

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Disclaimer

NOT FINANCIAL ADVICE. This software is experimental and in BETA. A real exchange execution service is in testing — use with caution. No warranty provided. Use at your own risk.

License

MIT .