🤖 DayTraderAI

Institutional-Grade Algorithmic Day Trading System

A sophisticated, fully autonomous trading platform combining real-time technical analysis, AI-powered validation, and professional risk management. Built with 20+ specialized modules working in harmony to execute high-probability trades with surgical precision.

📊 Live Performance (December 2025)

Metric	Current	Target
Win Rate	65-70%	70%+
Avg Winner	+2.2R	+2.0R
Avg Loser	-0.8R	-1.0R
Profit Factor	~2.8	2.5+
Sharpe Ratio	2.8+	2.5+
Max Drawdown	<2%	<5%

🏗️ System Architecture

Core Components

┌─────────────────────────────────────────────────────────────────────┐
│                         DAYTRADERAI ENGINE                          │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐             │
│  │   Market    │───▶│   Feature   │───▶│  Strategy   │             │
│  │    Data     │    │   Engine    │    │   Engine    │             │
│  │  (Alpaca)   │    │ (50+ Indic) │    │ (EMA/RSI)   │             │
│  └─────────────┘    └─────────────┘    └──────┬──────┘             │
│                                               │                     │
│  ┌─────────────┐    ┌─────────────┐    ┌──────▼──────┐             │
│  │   Regime    │───▶│    Risk     │───▶│   Order     │             │
│  │  Manager    │    │   Manager   │    │  Manager    │             │
│  │ (Fear/Greed)│    │ (Sizing)    │    │ (Execution) │             │
│  └─────────────┘    └─────────────┘    └──────┬──────┘             │
│                                               │                     │
│  ┌─────────────┐    ┌─────────────┐    ┌──────▼──────┐             │
│  │   Profit    │◀───│  Position   │◀───│   Smart     │             │
│  │ Protection  │    │   Manager   │    │  Executor   │             │
│  │ (R-Multiple)│    │ (Tracking)  │    │ (Fill Det.) │             │
│  └─────────────┘    └─────────────┘    └─────────────┘             │
│                                                                     │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐             │
│  │  Stop Loss  │    │  Momentum   │    │     ML      │             │
│  │ Protection  │    │   Scanner   │    │ Shadow Mode │             │
│  │ (5s checks) │    │ (Wave Rider)│    │ (Learning)  │             │
│  └─────────────┘    └─────────────┘    └─────────────┘             │
│                                                                     │
└─────────────────────────────────────────────────────────────────────┘

Module Breakdown

Layer	Module	Purpose
Data	`market_data.py`	Real-time bars from Alpaca WebSocket
Data	`features.py`	50+ technical indicators (EMA, RSI, MACD, VWAP, etc.)
Data	`daily_cache.py`	Twelve Data API for daily charts
Indicators	`fear_greed_scraper.py`	CNN Fear & Greed Index (0-100)
Indicators	`vix_fetcher.py`	VIX volatility monitoring
Indicators	`market_regime.py`	Market breadth analysis
Trading	`strategy.py`	EMA crossover signals with multi-factor confirmation
Trading	`risk_manager.py`	Dynamic position sizing, circuit breakers
Trading	`order_manager.py`	Order routing and bracket management
Trading	`position_manager.py`	Position tracking, trailing stops, partial profits
Trading	`regime_manager.py`	Fear/Greed regime adaptation
Trading	`momentum_confirmed_regime.py`	Triple-layer regime intelligence
Trading	`stop_loss_protection.py`	5-second stop verification
Trading	`profit_protection/`	R-multiple tracking, 2R/3R/4R profit taking
Orders	`smart_order_executor.py`	Slippage protection, fill detection
Orders	`fill_detection_engine.py`	Multi-method fill verification
Scanner	`momentum_scanner.py`	Momentum Wave Rider system
Scanner	`opportunity_scanner.py`	Technical opportunity detection
ML	`shadow_mode.py`	Zero-impact learning system
Advisory	`perplexity.py`	AI market research
Advisory	`openrouter.py`	Multi-model AI (DeepSeek, Grok)

⚡ How It Works

1. Signal Generation (Real-Time)

# backend/data/features.py generates signals every minute
Signal: SELL NVDA
├── EMA9: $183.32 crosses below EMA21: $183.60
├── RSI: 32.7 (bearish)
├── MACD: Bearish crossover
├── Volume: 1.50x average (confirmed)
├── VWAP: Price below VWAP (aligned)
└── Confidence: 74%

2. Regime-Adaptive Filtering

# backend/trading/strategy.py validates signals
Fear & Greed Index: 27/100 (FEAR regime)
├── Short in fear? Requires 3+ confirmations ✓
├── RSI > 25? Checking for oversold bounce risk
├── Confidence > 70%? Required in fear environment
└── Result: Signal APPROVED or REJECTED

3. Risk Management

# backend/trading/risk_manager.py calculates position size
Risk Multipliers:
├── Confidence (74%): 1.0x
├── Safety Score: 1.00x
├── Sentiment (27): 0.80x (reduced in fear)
├── Trend: 1.00x
├── Combined: 0.80x
└── Final Risk: 0.80% of equity

4. Smart Order Execution

# backend/orders/smart_order_executor.py
Order: SELL 55 NVDA @ $182.25
├── Slippage buffer: +0.3%
├── Stop Loss: $184.43 (1.5% from entry)
├── Take Profit: $176.25 (2:1 R/R)
├── Fill detection: 30-second timeout
└── Bracket orders: Created automatically

5. Profit Protection (Continuous)

# backend/trading/position_manager.py monitors positions
Position: NVDA @ +2.22R
├── State: PARTIAL_PROFIT_TAKEN
├── Action: Take 50% profits (27 shares)
├── Remaining: 28 shares with trailing stop
├── Stop updated: $184.96 → $181.90
└── R-multiple logged to Supabase

6. Stop Loss Protection (Every 5 Seconds)

# backend/trading/stop_loss_protection.py
Position Check: NVDA
├── Has active stop loss? NO
├── Action: Creating emergency bracket
├── Stop: $184.96 (1.5% from entry)
├── Take Profit: $177.67 (2:1 R/R)
└── Result: Position PROTECTED ✓

🎯 Key Features

Momentum-Confirmed Regime System

Triple Intelligence: Fear & Greed + Momentum Strength + VIX
Dynamic Sizing: 0.8x in fear, 1.0x neutral, 1.2x in greed
VIX Caps: Automatic risk reduction when VIX > 30

Intelligent Profit Protection

R-Multiple Tracking: Every position measured in risk units
Partial Profits: 50% at 2R, scale out at 3R/4R
Breakeven Protection: Stop moved to entry at 1R
Trailing Stops: 1% trailing distance locks profits

Smart Signal Filtering

Oversold Bounce Rejection: RSI < 25 in fear = no shorts
Confidence Requirements: 70%+ needed in fear environment
Multi-Factor Confirmation: EMA + RSI + MACD + Volume + VWAP

Self-Healing Architecture

5-Second Stop Verification: No position left unprotected
Orphan Order Cleanup: Stale orders removed automatically
Emergency Stops: Created for any unprotected position
Database Sync: Crash-resistant state management

🛠️ Tech Stack

Backend

Python 3.10+ - Core trading logic
FastAPI - High-performance async API
Alpaca Markets - Commission-free trading API
Supabase - Real-time database (PostgreSQL)
WebSockets - Live market data streaming

Frontend

React 18 - Modern UI framework
TypeScript - Type-safe development
Tailwind CSS - Utility-first styling
Vite - Fast build tooling

AI/ML

DeepSeek V3 - Trade validation
Perplexity Sonar - Market research
Grok 4 - Copilot chat
ML Shadow Mode - Learning system (0% weight)

🚀 Quick Start

1. Clone & Install

git clone https://github.com/codebytelabs/DayTraderAI.git
cd DayTraderAI

# Backend
cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Frontend
cd ../frontend
npm install

2. Configure Environment

# backend/.env
ALPACA_API_KEY=your_key
ALPACA_SECRET_KEY=your_secret
ALPACA_BASE_URL=https://paper-api.alpaca.markets

SUPABASE_URL=your_url
SUPABASE_KEY=your_anon_key

PERPLEXITY_API_KEY=your_key  # Optional
OPENROUTER_API_KEY=your_key  # Optional

3. Run

# Terminal 1: Backend
cd backend && ./start_backend.sh

# Terminal 2: Frontend
cd frontend && npm run dev

📈 Configuration

Key Settings (backend/config.py)

# Risk Management
risk_per_trade_pct = 0.015      # 1.5% base risk
min_stop_distance_pct = 0.008   # 0.8% minimum stop
max_stop_distance_pct = 0.020   # 2.0% maximum stop
circuit_breaker_pct = 0.03      # 3% daily max loss

# Stop Loss
stop_loss_atr_mult = 1.5        # 1.5x ATR stops
stop_loss_atr_period = 10       # 10-period ATR

# Profit Taking
partial_profit_r_target = 2.0   # Take 50% at 2R
breakeven_r_trigger = 1.0       # Move stop to entry at 1R
trailing_stop_pct = 0.01        # 1% trailing distance

# Trading Hours
entry_cutoff_time = "15:30"     # No new trades after 3:30 PM
eod_exit_time = "15:57"         # Force close at 3:57 PM

📊 Performance Projections

Scenario	Daily	Monthly	Annual	Max DD
Conservative	0.3%	6-8%	75-100%	<5%
Realistic	0.5%	10-12%	120-150%	<8%
Optimistic	0.7%	14-16%	180-220%	<12%

Compounding Example ($50,000 start):

Year 1: $50K → $125K (150% @ 0.4%/day)
Year 2: $125K → $312K (150%)
Year 3: $312K → $780K (150%)

⚠️ Disclaimer

This software is for educational purposes only. Trading involves substantial risk of loss. Past performance does not guarantee future results. Always test in paper trading before using real capital.

📄 License

MIT License - see LICENSE

Built with precision by the DayTraderAI Team 🚀

Name		Name	Last commit message	Last commit date
Latest commit History 51 Commits
.hypothesis		.hypothesis
.kiro/specs		.kiro/specs
Docs		Docs
backend		backend
components		components
frontend		frontend
hooks		hooks
services		services
simulation		simulation
state		state
types		types
.env.example		.env.example
.gitignore		.gitignore
App.tsx		App.tsx
LICENSE		LICENSE
MOMENTUM_WAVE_RIDER_ACTIVATION.md		MOMENTUM_WAVE_RIDER_ACTIVATION.md
README.md		README.md
index.html		index.html
index.tsx		index.tsx
mcp_servers_config.toml		mcp_servers_config.toml
metadata.json		metadata.json
minimal-app.html		minimal-app.html
package-lock.json		package-lock.json
package.json		package.json
run_integration_tests.sh		run_integration_tests.sh
start_app.sh		start_app.sh
start_backend.sh		start_backend.sh
test_results.json		test_results.json
tsconfig.json		tsconfig.json
types.ts		types.ts
update_mcp_config.sh		update_mcp_config.sh
vite-env.d.ts		vite-env.d.ts
vite.config.ts		vite.config.ts

License

codebytelabs/DayTraderAI

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🤖 DayTraderAI

📊 Live Performance (December 2025)

🏗️ System Architecture

Core Components

Module Breakdown

⚡ How It Works

1. Signal Generation (Real-Time)

2. Regime-Adaptive Filtering

3. Risk Management

4. Smart Order Execution

5. Profit Protection (Continuous)

6. Stop Loss Protection (Every 5 Seconds)

🎯 Key Features

Momentum-Confirmed Regime System

Intelligent Profit Protection

Smart Signal Filtering

Self-Healing Architecture

🛠️ Tech Stack

Backend

Frontend

AI/ML

🚀 Quick Start

1. Clone & Install

2. Configure Environment

3. Run

📈 Configuration

Key Settings (backend/config.py)

📊 Performance Projections

⚠️ Disclaimer

📄 License

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