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# Alpaca API Configuration
# Get these from your Alpaca paper trading account at https://app.alpaca.markets/paper/dashboard/overview
# Your Alpaca API Key (Paper Trading)
ALPACA_API_KEY=PKXR08ET6CSGV5QFS89E
# Your Alpaca Secret Key (Paper Trading)
ALPACA_SECRET_KEY=5ILlNGVM7WfPwJk8kPRlHhQwDO022H19RWBOkwgd
# Trading Configuration
SYMBOL=ETH/USD
STRATEGY_TYPE=enhanced
MAX_POSITION_SIZE=0.95
RISK_PER_TRADE=0.02
# Risk Management
MAX_DRAWDOWN_LIMIT=0.15
DAILY_LOSS_LIMIT=0.05

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# Environment variables and secrets
.env
*.env
.env.local
.env.production
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# Logs
logs/
*.log
# IDE
.vscode/
.idea/
*.swp
*.swo
*~
# OS
.DS_Store
.DS_Store?
._*
.Spotlight-V100
.Trashes
ehthumbs.db
Thumbs.db
# Backup files
*.bak
*.backup
*.old
# Data files (optional - uncomment if you don't want to track data)
# *.csv
# *.json
# *.parquet
# Trading specific
/trading_data/
/backtest_results/
*.png
*.jpg
*.pdf
# Temporary files
tmp/
temp/
.tmp/

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# Project Setup Complete! 🎉
## What You Now Have
✅ **Professional Trading System Structure**
- Modular, maintainable, and scalable architecture
- Clear separation of concerns (data, strategy, risk, execution)
- Comprehensive logging and monitoring
- Full configuration management
✅ **Safety Features**
- Paper trading by default (no real money at risk)
- Multiple layers of risk management
- Emergency stops and circuit breakers
- Data validation and error handling
✅ **Easy to Use**
- Simple command-line interface
- Quick start script (`run.py`)
- Comprehensive documentation
- System testing capabilities
## Quick Start Guide
### 1. Install Dependencies
```bash
pip install -r requirements.txt
```
### 2. Configure API Keys
Copy `.env.template` to `.env` and add your Alpaca paper trading API keys:
```bash
cp .env.template .env
# Edit .env with your API credentials
```
### 3. Test the System
```bash
python test_system.py
```
### 4. Run Backtesting
```bash
python main.py --mode backtest
```
### 5. Start Live Paper Trading (when ready)
```bash
python main.py --mode live
```
Or use the interactive menu:
```bash
python run.py
```
## Project Structure Overview
```
trading/
├── main.py # 🚀 Main application entry point
├── run.py # 🎯 Interactive quick start menu
├── test_system.py # 🧪 System verification tests
├── requirements.txt # 📦 Python dependencies
├── .env.template # 🔐 Environment variables template
├── README.md # 📖 Comprehensive documentation
├──
├── config/ # ⚙️ Configuration Management
│ ├── __init__.py
│ └── trading_config.py
├──
├── src/ # 🏗️ Core System Components
│ ├── __init__.py
│ ├── data_handler.py # 📊 Market data operations
│ ├── strategy.py # 🎯 Trading strategy logic
│ ├── risk_manager.py # 🛡️ Risk management system
│ ├── trading_engine.py # ⚡ Core trading engine
│ └── logger_setup.py # 📝 Logging configuration
├──
└── logs/ # 📋 Log files (created automatically)
```
## Key Improvements from Original Code
### 🏗️ **Architecture**
- **Before**: Single monolithic files
- **After**: Modular, object-oriented design with clear responsibilities
### 🛡️ **Safety**
- **Before**: Hardcoded credentials, no risk management
- **After**: Environment variables, comprehensive risk controls, paper trading default
### 📊 **Maintainability**
- **Before**: Mixed concerns, hard to modify
- **After**: Clean interfaces, easy to customize and extend
### 🔧 **Configuration**
- **Before**: Hardcoded values throughout code
- **After**: Centralized configuration, environment-based settings
### 📝 **Monitoring**
- **Before**: Basic print statements
- **After**: Professional logging system with multiple log files
### 🚀 **Usability**
- **Before**: Required code modification to run
- **After**: Command-line interface, interactive menu, easy deployment
## Next Steps
1. **Review Configuration**: Check `config/trading_config.py` for strategy parameters
2. **Test Thoroughly**: Run extensive backtests before live trading
3. **Monitor Closely**: Watch logs and performance metrics
4. **Start Small**: Use paper trading and small position sizes initially
5. **Iterate**: Adjust parameters based on performance
## Safety Reminders
- ⚠️ **Always use paper trading first**
- ⚠️ **Never disable risk management**
- ⚠️ **Monitor the system regularly**
- ⚠️ **Start with small position sizes**
- ⚠️ **Understand the risks involved**
## Support
- Check `README.md` for detailed documentation
- Review log files in `logs/` for troubleshooting
- Test with `test_system.py` to verify setup
- Use `--help` flag for command-line options
---
**Congratulations!** You now have a professional-grade automated trading system that's:
- ✅ Safe (paper trading default)
- ✅ Modular (easy to modify)
- ✅ Monitored (comprehensive logging)
- ✅ Tested (backtesting capabilities)
- ✅ Production-ready (proper architecture)
Happy trading! 📈

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# Trading Algorithm Project
A production-ready modular trading system that supports multiple strategies for paper and live trading on Alpaca Markets.
## Features
- **Multiple Strategy Support**: Conservative and Enhanced trend-following strategies
- **Paper & Live Trading**: Safe testing with paper trading, production-ready for live trading
- **Modular Architecture**: Clean separation of concerns for easy maintenance
- **Risk Management**: Position sizing, drawdown limits, and trailing stops
- **Professional Logging**: Comprehensive trade and performance logging
- **Configuration Management**: Environment-based and file-based configuration
- **Strategy Comparison**: Built-in tools to compare strategy performance
## Project Structure
```
trading/
├── src/
│ ├── data_handler.py # Market data retrieval and processing
│ ├── strategy_conservative.py # Conservative trend-following strategy
│ ├── strategy_enhanced.py # Enhanced trend-following with tighter risk controls
│ ├── strategy_factory.py # Strategy selection and instantiation
│ ├── risk_manager.py # Position sizing and risk controls
│ ├── trading_engine.py # Main trading execution engine
│ └── logger_setup.py # Logging configuration
├── config/
│ └── trading_config.py # Configuration settings
├── logs/ # Generated log files
├── main.py # Application entry point
├── compare_strategies.py # Strategy comparison tool
└── README.md
```
## Strategy Types
### Conservative Strategy
- **Philosophy**: Let big winners run, relaxed exit conditions
- **Trailing Stops**: 18-40% (wider range for bigger moves)
- **Emergency Exits**: 28-day maximum hold time
- **Best For**: Trending markets, patient traders
### Enhanced Strategy
- **Philosophy**: Tighter risk control, early loss protection
- **Trailing Stops**: 12-40% (tighter range for quicker exits)
- **Emergency Exits**: 7-14 day maximum hold time
- **Entry Conditions**: More selective (2% above trend line)
- **Best For**: Volatile markets, risk-conscious traders
## Quick Start
### 1. Setup Environment
```bash
# Clone and navigate to project
cd /path/to/trading
# Install dependencies
pip install alpaca-trade-api pandas numpy python-dotenv
# Setup environment variables
echo "ALPACA_API_KEY=your_api_key" > .env
echo "ALPACA_SECRET_KEY=your_secret_key" >> .env
echo "ALPACA_BASE_URL=https://paper-api.alpaca.markets" >> .env
echo "STRATEGY_TYPE=enhanced" >> .env
```
### 2. Run Backtesting
```bash
# Test conservative strategy
python3 main.py --mode backtest --strategy conservative
# Test enhanced strategy
python3 main.py --mode backtest --strategy enhanced
# Compare both strategies
python3 compare_strategies.py
```
### 3. Paper Trading
```bash
# Start paper trading with enhanced strategy
python3 main.py --mode paper --strategy enhanced
# Start paper trading with conservative strategy
python3 main.py --mode paper --strategy conservative
```
## Configuration
### Environment Variables
- `ALPACA_API_KEY`: Your Alpaca API key
- `ALPACA_SECRET_KEY`: Your Alpaca secret key
- `ALPACA_BASE_URL`: API endpoint (paper or live)
- `STRATEGY_TYPE`: Default strategy ("conservative" or "enhanced")
### Strategy Selection
You can select strategies in multiple ways:
1. **Command Line**: `--strategy enhanced`
2. **Environment Variable**: `STRATEGY_TYPE=conservative`
3. **Config File**: Modify `trading_config.py`
Priority: Command line > Environment variable > Config file
## Usage Examples
```bash
# Backtest enhanced strategy for 90 days
python3 main.py --mode backtest --strategy enhanced --days 90
# Paper trade with conservative strategy
python3 main.py --mode paper --strategy conservative
# Compare strategies side-by-side
python3 compare_strategies.py
# Check strategy configuration
python3 -c "from config.trading_config import TradingConfig; print(TradingConfig())"
```
## Strategy Comparison
The `compare_strategies.py` script provides detailed performance comparison:
- **Returns**: Total return vs market benchmark
- **Risk Metrics**: Max drawdown, Sharpe ratio
- **Trade Analytics**: Win rate, profit factor, average wins/losses
- **Timing**: Time in market, trade frequency
## Risk Management
Both strategies include comprehensive risk controls:
- **Position Sizing**: Configurable risk per trade
- **Trailing Stops**: Dynamic stop-loss adjustment
- **Emergency Exits**: Maximum hold time limits
- **Drawdown Limits**: Account protection thresholds
- **Market Hours**: Trading only during market hours
## Logging
The system generates detailed logs in the `logs/` directory:
- `trading.log`: General trading activity
- `trades.log`: Trade execution details
- `risk.log`: Risk management events
- `performance.log`: Performance metrics
## Safety Features
- **Paper Trading First**: Always test with paper money
- **Fail-Safe Defaults**: Conservative settings by default
- **Error Handling**: Graceful error recovery
- **Position Limits**: Maximum position size controls
- **Market Data Validation**: Data quality checks
## Development
To add a new strategy:
1. Create new strategy file in `src/strategy_your_name.py`
2. Inherit from base strategy pattern (see existing strategies)
3. Register in `src/strategy_factory.py`
4. Update configuration options
5. Test with backtesting before live usage
## Support
For issues or questions:
1. Check logs in `logs/` directory
2. Verify API keys and permissions
3. Test with paper trading first
4. Review strategy configuration
## Disclaimer
This software is for educational and testing purposes. Always test thoroughly with paper trading before using real money. Past performance does not guarantee future results.

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import alpaca_trade_api as tradeapi
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
# Use past dates for backtesting
end_date = datetime.now().strftime('%Y-%m-%d')
start_date = (datetime.now() - timedelta(days=180)).strftime('%Y-%m-%d')
api = tradeapi.REST('PKXR08ET6CSGV5QFS89E', '5ILlNGVM7WfPwJk8kPRlHhQwDO022H19RWBOkwgd', base_url='https://data.alpaca.markets')
# Get historical data
bars = api.get_crypto_bars('ETH/USD', tradeapi.TimeFrame.Hour, start_date, end_date).df
bars.index = pd.to_datetime(bars.index)
# SIMPLIFIED TREND-FOLLOWING INDICATORS - Focus on what works
bars['ema_fast'] = bars['close'].ewm(span=12).mean() # 12 hours - responsive but not noisy
bars['ema_slow'] = bars['close'].ewm(span=30).mean() # 30 hours - trend filter
bars['ema_trend'] = bars['close'].ewm(span=80).mean() # 80 hours - major trend
# RSI for momentum filtering
def calculate_rsi(prices, window=14):
delta = prices.diff()
gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
rs = gain / loss
rsi = 100 - (100 / (1 + rs))
return rsi
bars['rsi'] = calculate_rsi(bars['close'], 14)
# MACD for momentum confirmation
bars['ema_12'] = bars['close'].ewm(span=12).mean()
bars['ema_26'] = bars['close'].ewm(span=26).mean()
bars['macd'] = bars['ema_12'] - bars['ema_26']
bars['macd_signal'] = bars['macd'].ewm(span=9).mean()
# Price momentum
bars['price_momentum_6h'] = bars['close'].pct_change(periods=6)
# Volume confirmation (relaxed)
bars['volume_ma'] = bars['volume'].rolling(window=20).mean()
bars['volume_ok'] = bars['volume'] > bars['volume_ma'] * 0.7 # Very relaxed
# CONSERVATIVE BUY CONDITIONS - Back to what worked!
buy_conditions = (
# BASIC TREND REQUIREMENT - More strict
(bars['close'] > bars['ema_trend']) & # Must be above major trend (no tolerance)
# ENTRY TRIGGERS - More selective
(
# EMA bullish alignment with momentum
((bars['ema_fast'] > bars['ema_slow']) &
(bars['close'] > bars['ema_fast']) &
(bars['rsi'] > 45) & (bars['rsi'] < 75)) | # Balanced RSI
# MACD bullish with strong momentum
((bars['macd'] > bars['macd_signal']) &
(bars['price_momentum_6h'] > 0.02) & # 2% momentum required
(bars['rsi'] > 40) & (bars['rsi'] < 70)) | # Not overbought
# Strong breakout with volume
((bars['close'] > bars['ema_fast']) &
(bars['close'] > bars['ema_slow']) &
(bars['close'] > bars['ema_trend']) &
(bars['volume_ok']) &
(bars['price_momentum_6h'] > 0.015)) # Strong momentum
)
)
# ULTRA-CONSERVATIVE SELL CONDITIONS - Let winners run much longer!
sell_conditions = (
# ONLY MAJOR TREND BREAKS - Much more restrictive
(
# Confirmed EMA death cross AND major breakdown
((bars['ema_fast'] < bars['ema_slow']) &
(bars['close'] < bars['ema_trend'] * 0.90) & # 10% below trend (not 8%)
(bars['rsi'] < 35)) | # Very weak momentum (not 40)
# Sustained breakdown below major trend - more severe
((bars['close'] < bars['ema_trend'] * 0.88) & # 12% below major trend (not 8%)
(bars['price_momentum_6h'] < -0.04)) | # Stronger decline (not -0.03)
# Extreme overbought with strong reversal signs
((bars['rsi'] > 85) & # Higher threshold (not 80)
(bars['rsi'].shift(1) > bars['rsi']) & # RSI declining
(bars['rsi'].shift(2) > bars['rsi'].shift(1)) & # For 2 periods
(bars['rsi'].shift(3) > bars['rsi'].shift(2)) & # For 3 periods
(bars['macd'] < bars['macd_signal']) & # MACD bearish
(bars['price_momentum_6h'] < -0.02)) # Plus price weakness
)
)
# Apply signals with LONGER cooldown - Back to original
bars['buy_signal_raw'] = buy_conditions
bars['sell_signal_raw'] = sell_conditions
bars['buy_signal'] = False
bars['sell_signal'] = False
last_signal_idx = -50
COOLDOWN_HOURS = 24 # Back to 24-hour cooldown for quality trades
for i in range(len(bars)):
if i - last_signal_idx >= COOLDOWN_HOURS:
if bars['buy_signal_raw'].iloc[i]:
bars.iloc[i, bars.columns.get_loc('buy_signal')] = True
last_signal_idx = i
elif bars['sell_signal_raw'].iloc[i]:
bars.iloc[i, bars.columns.get_loc('sell_signal')] = True
last_signal_idx = i
# POSITION MANAGEMENT - Conservative entry + WIDER trailing stops
bars['position'] = 0
position = 0
entry_price = None
highest_price_since_entry = None
bars_since_entry = 0
for i in range(1, len(bars)):
current_price = bars['close'].iloc[i]
if bars['buy_signal'].iloc[i] and position == 0:
# CONSERVATIVE ENTRY - Back to original
if bars['close'].iloc[i] > bars['ema_trend'].iloc[i]: # Must be above trend
position = 1
entry_price = current_price
highest_price_since_entry = current_price
bars_since_entry = 0
elif position == 1:
bars_since_entry += 1
# Update highest price
if current_price > highest_price_since_entry:
highest_price_since_entry = current_price
profit_pct = (current_price / entry_price - 1)
# CRYPTO-SCALE TRAILING STOPS - Let big winners truly run!
if profit_pct > 2.00: # 200%+ profit: 40% trailing stop
trailing_stop_pct = 0.60
elif profit_pct > 1.50: # 150%+ profit: 35% trailing stop
trailing_stop_pct = 0.65
elif profit_pct > 1.00: # 100%+ profit: 30% trailing stop
trailing_stop_pct = 0.70
elif profit_pct > 0.75: # 75%+ profit: 25% trailing stop
trailing_stop_pct = 0.75
elif profit_pct > 0.50: # 50%+ profit: 22% trailing stop
trailing_stop_pct = 0.78
elif profit_pct > 0.25: # 25%+ profit: 20% trailing stop
trailing_stop_pct = 0.80
else: # < 25% profit: 18% trailing stop
trailing_stop_pct = 0.82
trailing_stop_price = highest_price_since_entry * trailing_stop_pct
# MINIMAL EXIT CONDITIONS - Only major trend breaks
exit_conditions = (
bars['sell_signal'].iloc[i] or
current_price <= trailing_stop_price or
# Emergency stops - very relaxed
(bars_since_entry >= 672 and profit_pct < -0.30) or # 28 days & -30%
(bars['close'].iloc[i] < bars['ema_trend'].iloc[i] * 0.75) # 25% below major trend
)
if exit_conditions:
position = 0
entry_price = None
highest_price_since_entry = None
bars_since_entry = 0
bars.iloc[i, bars.columns.get_loc('position')] = position
# ENHANCED POSITION MANAGEMENT - Better risk control for longer periods
bars['position'] = 0
position = 0
entry_price = None
highest_price_since_entry = None
bars_since_entry = 0
for i in range(1, len(bars)):
current_price = bars['close'].iloc[i]
if bars['buy_signal'].iloc[i] and position == 0:
# MORE STRICT ENTRY - Only in very strong trends
if (bars['close'].iloc[i] > bars['ema_trend'].iloc[i] * 1.02 and # 2% above major trend
bars['ema_fast'].iloc[i] > bars['ema_slow'].iloc[i] * 1.01): # Fast EMA clearly above slow
position = 1
entry_price = current_price
highest_price_since_entry = current_price
bars_since_entry = 0
elif position == 1:
bars_since_entry += 1
# Update highest price
if current_price > highest_price_since_entry:
highest_price_since_entry = current_price
profit_pct = (current_price / entry_price - 1)
# CRYPTO-SCALE TRAILING STOPS with better risk management
if profit_pct > 2.00: # 200%+ profit: 40% trailing stop
trailing_stop_pct = 0.60
elif profit_pct > 1.50: # 150%+ profit: 35% trailing stop
trailing_stop_pct = 0.65
elif profit_pct > 1.00: # 100%+ profit: 30% trailing stop
trailing_stop_pct = 0.70
elif profit_pct > 0.75: # 75%+ profit: 25% trailing stop
trailing_stop_pct = 0.75
elif profit_pct > 0.50: # 50%+ profit: 22% trailing stop
trailing_stop_pct = 0.78
elif profit_pct > 0.25: # 25%+ profit: 20% trailing stop
trailing_stop_pct = 0.80
elif profit_pct > 0.10: # 10%+ profit: 15% trailing stop
trailing_stop_pct = 0.85
else: # < 10% profit: 12% trailing stop (tighter for early losses)
trailing_stop_pct = 0.88
trailing_stop_price = highest_price_since_entry * trailing_stop_pct
# IMPROVED EXIT CONDITIONS - Better loss control
exit_conditions = (
bars['sell_signal'].iloc[i] or
current_price <= trailing_stop_price or
# Tighter time-based stops to prevent long drawdowns
(bars_since_entry >= 168 and profit_pct < -0.10) or # 7 days & -10% (not 28 days & -30%)
(bars_since_entry >= 336 and profit_pct < -0.05) or # 14 days & -5%
(bars['close'].iloc[i] < bars['ema_trend'].iloc[i] * 0.85) # 15% below major trend (not 25%)
)
if exit_conditions:
position = 0
entry_price = None
highest_price_since_entry = None
bars_since_entry = 0
bars.iloc[i, bars.columns.get_loc('position')] = position
# Calculate returns with lower costs (fewer trades)
bars['market_return'] = bars['close'].pct_change()
bars['strategy_return'] = bars['position'].shift(1) * bars['market_return']
trade_cost = 0.0005 # Slightly lower for fewer, larger trades
bars['position_change'] = bars['position'].diff().abs()
bars['costs'] = bars['position_change'] * trade_cost
bars['strategy_return'] = bars['strategy_return'] - bars['costs']
# Cumulative returns
bars['cum_market'] = (1 + bars['market_return']).cumprod() - 1
bars['cum_strategy'] = (1 + bars['strategy_return']).cumprod() - 1
# Performance metrics
total_return = bars['cum_strategy'].iloc[-1] * 100
market_return_pct = bars['cum_market'].iloc[-1] * 100
# Count trades
position_changes = bars['position'].diff()
buys = (position_changes == 1).sum()
sells = (position_changes == -1).sum()
# Calculate trade returns
trade_returns = []
entry_price = None
for i in range(len(bars)):
if position_changes.iloc[i] == 1:
entry_price = bars['close'].iloc[i]
elif position_changes.iloc[i] == -1 and entry_price is not None:
exit_price = bars['close'].iloc[i]
trade_return = (exit_price / entry_price - 1) - (2 * trade_cost)
trade_returns.append(trade_return)
entry_price = None
if len(trade_returns) > 0:
trade_returns = np.array(trade_returns)
win_rate = (trade_returns > 0).mean() * 100
avg_win = trade_returns[trade_returns > 0].mean() * 100 if (trade_returns > 0).any() else 0
avg_loss = trade_returns[trade_returns < 0].mean() * 100 if (trade_returns < 0).any() else 0
max_win = trade_returns.max() * 100
max_loss = trade_returns.min() * 100
total_wins = trade_returns[trade_returns > 0].sum()
total_losses = abs(trade_returns[trade_returns < 0].sum())
profit_factor = total_wins / total_losses if total_losses > 0 else float('inf')
else:
win_rate = avg_win = avg_loss = max_win = max_loss = profit_factor = 0
# Sharpe ratio
if bars['strategy_return'].std() > 0:
sharpe = bars['strategy_return'].mean() / bars['strategy_return'].std() * np.sqrt(365 * 24)
else:
sharpe = 0
# Max drawdown
running_max = bars['cum_strategy'].cummax()
drawdown = (bars['cum_strategy'] - running_max)
max_drawdown = drawdown.min() * 100
print("===== TREND-FOLLOWING STRATEGY =====")
print(f"Strategy Return: {total_return:.2f}%")
print(f"Market Return: {market_return_pct:.2f}%")
print(f"Outperformance: {total_return - market_return_pct:.2f}%")
print(f"Sharpe Ratio: {sharpe:.2f}")
print(f"Max Drawdown: {max_drawdown:.2f}%")
print(f"Total Trades: {len(trade_returns)}")
print(f"Buy Signals: {buys}")
print(f"Sell Signals: {sells}")
print(f"Win Rate: {win_rate:.2f}%")
print(f"Profit Factor: {profit_factor:.2f}")
print(f"Avg Win: {avg_win:.2f}%")
print(f"Avg Loss: {avg_loss:.2f}%")
print(f"Max Win: {max_win:.2f}%")
print(f"Max Loss: {max_loss:.2f}%")
# Calculate time in market
time_in_market = (bars['position'] > 0).mean() * 100
print(f"Time in Market: {time_in_market:.1f}%")

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import alpaca_trade_api as tradeapi
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
# Use past dates for backtesting
end_date = datetime.now().strftime('%Y-%m-%d')
start_date = (datetime.now() - timedelta(days=90)).strftime('%Y-%m-%d')
api = tradeapi.REST('PKXR08ET6CSGV5QFS89E', '5ILlNGVM7WfPwJk8kPRlHhQwDO022H19RWBOkwgd', base_url='https://data.alpaca.markets')
# Get historical data
bars = api.get_crypto_bars('ETH/USD', tradeapi.TimeFrame.Hour, start_date, end_date).df
bars.index = pd.to_datetime(bars.index)
# SIMPLIFIED TREND-FOLLOWING INDICATORS - Focus on what works
bars['ema_fast'] = bars['close'].ewm(span=12).mean() # 12 hours - responsive but not noisy
bars['ema_slow'] = bars['close'].ewm(span=30).mean() # 30 hours - trend filter
bars['ema_trend'] = bars['close'].ewm(span=80).mean() # 80 hours - major trend
# RSI for momentum filtering
def calculate_rsi(prices, window=14):
delta = prices.diff()
gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
rs = gain / loss
rsi = 100 - (100 / (1 + rs))
return rsi
bars['rsi'] = calculate_rsi(bars['close'], 14)
# MACD for momentum confirmation
bars['ema_12'] = bars['close'].ewm(span=12).mean()
bars['ema_26'] = bars['close'].ewm(span=26).mean()
bars['macd'] = bars['ema_12'] - bars['ema_26']
bars['macd_signal'] = bars['macd'].ewm(span=9).mean()
# Price momentum
bars['price_momentum_6h'] = bars['close'].pct_change(periods=6)
# Volume confirmation (relaxed)
bars['volume_ma'] = bars['volume'].rolling(window=20).mean()
bars['volume_ok'] = bars['volume'] > bars['volume_ma'] * 0.7 # Very relaxed
# CONSERVATIVE BUY CONDITIONS - Back to what worked!
buy_conditions = (
# BASIC TREND REQUIREMENT - More strict
(bars['close'] > bars['ema_trend']) & # Must be above major trend (no tolerance)
# ENTRY TRIGGERS - More selective
(
# EMA bullish alignment with momentum
((bars['ema_fast'] > bars['ema_slow']) &
(bars['close'] > bars['ema_fast']) &
(bars['rsi'] > 45) & (bars['rsi'] < 75)) | # Balanced RSI
# MACD bullish with strong momentum
((bars['macd'] > bars['macd_signal']) &
(bars['price_momentum_6h'] > 0.02) & # 2% momentum required
(bars['rsi'] > 40) & (bars['rsi'] < 70)) | # Not overbought
# Strong breakout with volume
((bars['close'] > bars['ema_fast']) &
(bars['close'] > bars['ema_slow']) &
(bars['close'] > bars['ema_trend']) &
(bars['volume_ok']) &
(bars['price_momentum_6h'] > 0.015)) # Strong momentum
)
)
# ULTRA-CONSERVATIVE SELL CONDITIONS - Let winners run much longer!
sell_conditions = (
# ONLY MAJOR TREND BREAKS - Much more restrictive
(
# Confirmed EMA death cross AND major breakdown
((bars['ema_fast'] < bars['ema_slow']) &
(bars['close'] < bars['ema_trend'] * 0.90) & # 10% below trend (not 8%)
(bars['rsi'] < 35)) | # Very weak momentum (not 40)
# Sustained breakdown below major trend - more severe
((bars['close'] < bars['ema_trend'] * 0.88) & # 12% below major trend (not 8%)
(bars['price_momentum_6h'] < -0.04)) | # Stronger decline (not -0.03)
# Extreme overbought with strong reversal signs
((bars['rsi'] > 85) & # Higher threshold (not 80)
(bars['rsi'].shift(1) > bars['rsi']) & # RSI declining
(bars['rsi'].shift(2) > bars['rsi'].shift(1)) & # For 2 periods
(bars['rsi'].shift(3) > bars['rsi'].shift(2)) & # For 3 periods
(bars['macd'] < bars['macd_signal']) & # MACD bearish
(bars['price_momentum_6h'] < -0.02)) # Plus price weakness
)
)
# Apply signals with LONGER cooldown - Back to original
bars['buy_signal_raw'] = buy_conditions
bars['sell_signal_raw'] = sell_conditions
bars['buy_signal'] = False
bars['sell_signal'] = False
last_signal_idx = -50
COOLDOWN_HOURS = 24 # Back to 24-hour cooldown for quality trades
for i in range(len(bars)):
if i - last_signal_idx >= COOLDOWN_HOURS:
if bars['buy_signal_raw'].iloc[i]:
bars.iloc[i, bars.columns.get_loc('buy_signal')] = True
last_signal_idx = i
elif bars['sell_signal_raw'].iloc[i]:
bars.iloc[i, bars.columns.get_loc('sell_signal')] = True
last_signal_idx = i
# POSITION MANAGEMENT - Conservative entry + WIDER trailing stops
bars['position'] = 0
position = 0
entry_price = None
highest_price_since_entry = None
bars_since_entry = 0
for i in range(1, len(bars)):
current_price = bars['close'].iloc[i]
if bars['buy_signal'].iloc[i] and position == 0:
# CONSERVATIVE ENTRY - Back to original
if bars['close'].iloc[i] > bars['ema_trend'].iloc[i]: # Must be above trend
position = 1
entry_price = current_price
highest_price_since_entry = current_price
bars_since_entry = 0
elif position == 1:
bars_since_entry += 1
# Update highest price
if current_price > highest_price_since_entry:
highest_price_since_entry = current_price
profit_pct = (current_price / entry_price - 1)
# CRYPTO-SCALE TRAILING STOPS - Let big winners truly run!
if profit_pct > 2.00: # 200%+ profit: 40% trailing stop
trailing_stop_pct = 0.60
elif profit_pct > 1.50: # 150%+ profit: 35% trailing stop
trailing_stop_pct = 0.65
elif profit_pct > 1.00: # 100%+ profit: 30% trailing stop
trailing_stop_pct = 0.70
elif profit_pct > 0.75: # 75%+ profit: 25% trailing stop
trailing_stop_pct = 0.75
elif profit_pct > 0.50: # 50%+ profit: 22% trailing stop
trailing_stop_pct = 0.78
elif profit_pct > 0.25: # 25%+ profit: 20% trailing stop
trailing_stop_pct = 0.80
else: # < 25% profit: 18% trailing stop
trailing_stop_pct = 0.82
trailing_stop_price = highest_price_since_entry * trailing_stop_pct
# MINIMAL EXIT CONDITIONS - Only major trend breaks
exit_conditions = (
bars['sell_signal'].iloc[i] or
current_price <= trailing_stop_price or
# Emergency stops - very relaxed
(bars_since_entry >= 672 and profit_pct < -0.30) or # 28 days & -30%
(bars['close'].iloc[i] < bars['ema_trend'].iloc[i] * 0.75) # 25% below major trend
)
if exit_conditions:
position = 0
entry_price = None
highest_price_since_entry = None
bars_since_entry = 0
bars.iloc[i, bars.columns.get_loc('position')] = position
# Calculate returns with lower costs (fewer trades)
bars['market_return'] = bars['close'].pct_change()
bars['strategy_return'] = bars['position'].shift(1) * bars['market_return']
trade_cost = 0.0005 # Slightly lower for fewer, larger trades
bars['position_change'] = bars['position'].diff().abs()
bars['costs'] = bars['position_change'] * trade_cost
bars['strategy_return'] = bars['strategy_return'] - bars['costs']
# Cumulative returns
bars['cum_market'] = (1 + bars['market_return']).cumprod() - 1
bars['cum_strategy'] = (1 + bars['strategy_return']).cumprod() - 1
# Performance metrics
total_return = bars['cum_strategy'].iloc[-1] * 100
market_return_pct = bars['cum_market'].iloc[-1] * 100
# Count trades
position_changes = bars['position'].diff()
buys = (position_changes == 1).sum()
sells = (position_changes == -1).sum()
# Calculate trade returns
trade_returns = []
entry_price = None
for i in range(len(bars)):
if position_changes.iloc[i] == 1:
entry_price = bars['close'].iloc[i]
elif position_changes.iloc[i] == -1 and entry_price is not None:
exit_price = bars['close'].iloc[i]
trade_return = (exit_price / entry_price - 1) - (2 * trade_cost)
trade_returns.append(trade_return)
entry_price = None
if len(trade_returns) > 0:
trade_returns = np.array(trade_returns)
win_rate = (trade_returns > 0).mean() * 100
avg_win = trade_returns[trade_returns > 0].mean() * 100 if (trade_returns > 0).any() else 0
avg_loss = trade_returns[trade_returns < 0].mean() * 100 if (trade_returns < 0).any() else 0
max_win = trade_returns.max() * 100
max_loss = trade_returns.min() * 100
total_wins = trade_returns[trade_returns > 0].sum()
total_losses = abs(trade_returns[trade_returns < 0].sum())
profit_factor = total_wins / total_losses if total_losses > 0 else float('inf')
else:
win_rate = avg_win = avg_loss = max_win = max_loss = profit_factor = 0
# Sharpe ratio
if bars['strategy_return'].std() > 0:
sharpe = bars['strategy_return'].mean() / bars['strategy_return'].std() * np.sqrt(365 * 24)
else:
sharpe = 0
# Max drawdown
running_max = bars['cum_strategy'].cummax()
drawdown = (bars['cum_strategy'] - running_max)
max_drawdown = drawdown.min() * 100
print("===== TREND-FOLLOWING STRATEGY =====")
print(f"Strategy Return: {total_return:.2f}%")
print(f"Market Return: {market_return_pct:.2f}%")
print(f"Outperformance: {total_return - market_return_pct:.2f}%")
print(f"Sharpe Ratio: {sharpe:.2f}")
print(f"Max Drawdown: {max_drawdown:.2f}%")
print(f"Total Trades: {len(trade_returns)}")
print(f"Buy Signals: {buys}")
print(f"Sell Signals: {sells}")
print(f"Win Rate: {win_rate:.2f}%")
print(f"Profit Factor: {profit_factor:.2f}")
print(f"Avg Win: {avg_win:.2f}%")
print(f"Avg Loss: {avg_loss:.2f}%")
print(f"Max Win: {max_win:.2f}%")
print(f"Max Loss: {max_loss:.2f}%")
# Calculate time in market
time_in_market = (bars['position'] > 0).mean() * 100
print(f"Time in Market: {time_in_market:.1f}%")

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#!/usr/bin/env python3
"""
Strategy Comparison Script
Runs both strategies and compares their performance.
"""
import sys
# Add src directory to path for imports
sys.path.append('src')
sys.path.append('config')
from src.logger_setup import setup_logging
from src.data_handler import DataHandler
from src.strategy_factory import get_strategy
import logging
def run_strategy_comparison():
"""Compare both strategies side by side"""
setup_logging()
logger = logging.getLogger(__name__)
print("=" * 80)
print("STRATEGY COMPARISON - CONSERVATIVE vs ENHANCED")
print("=" * 80)
try:
# Get data once for both strategies
data_handler = DataHandler()
bars = data_handler.get_historical_data(days=180)
if bars.empty:
print("❌ No historical data available")
return
# Calculate technical indicators
bars = data_handler.calculate_technical_indicators(bars)
strategies = ['conservative', 'enhanced']
results = {}
for strategy_type in strategies:
print(f"\n📊 Running {strategy_type.upper()} strategy...")
try:
# Create strategy
strategy = get_strategy(strategy_type)
# Generate signals
strategy_bars = bars.copy()
strategy_bars = strategy.generate_signals(strategy_bars)
# Run simulation
results[strategy_type] = run_strategy_simulation(strategy_bars, strategy)
print(f"✅ {strategy.name} completed")
except Exception as e:
print(f"❌ Error running {strategy_type} strategy: {e}")
continue
# Display comparison
display_strategy_comparison(results)
except Exception as e:
logger.error(f"Error in strategy comparison: {e}")
print(f"❌ Comparison failed: {e}")
def run_strategy_simulation(bars, strategy):
"""Run simulation for a strategy"""
# Position tracking
position = 0
entry_price = None
highest_price_since_entry = None
bars_since_entry = 0
trades = []
bars['position'] = 0
for i in range(1, len(bars)):
current_price = bars['close'].iloc[i]
if bars['buy_signal'].iloc[i] and position == 0:
# Entry conditions
if strategy.get_entry_conditions(bars, i):
position = 1
entry_price = current_price
highest_price_since_entry = current_price
bars_since_entry = 0
elif position == 1:
bars_since_entry += 1
if current_price > highest_price_since_entry:
highest_price_since_entry = current_price
# Exit conditions
should_exit, exit_reason = strategy.should_exit_position(
current_price=current_price,
entry_price=entry_price,
highest_price=highest_price_since_entry,
bars_since_entry=bars_since_entry,
sell_signal=bars['sell_signal'].iloc[i],
ema_trend=bars['ema_trend'].iloc[i]
)
if should_exit:
# Record trade
profit_pct = (current_price / entry_price - 1)
trades.append({
'entry_price': entry_price,
'exit_price': current_price,
'profit_pct': profit_pct,
'bars_held': bars_since_entry,
'exit_reason': exit_reason
})
position = 0
entry_price = None
highest_price_since_entry = None
bars_since_entry = 0
bars.iloc[i, bars.columns.get_loc('position')] = position
# Calculate returns
bars['market_return'] = bars['close'].pct_change()
bars['strategy_return'] = bars['position'].shift(1) * bars['market_return']
# Apply costs
trade_cost = 0.0005
bars['position_change'] = bars['position'].diff().abs()
bars['costs'] = bars['position_change'] * trade_cost
bars['strategy_return'] = bars['strategy_return'] - bars['costs']
# Cumulative returns
bars['cum_strategy'] = (1 + bars['strategy_return']).cumprod() - 1
bars['cum_market'] = (1 + bars['market_return']).cumprod() - 1
# Calculate metrics
total_return = bars['cum_strategy'].iloc[-1] * 100
market_return = bars['cum_market'].iloc[-1] * 100
if len(trades) > 0:
import numpy as np
trade_returns = [t['profit_pct'] for t in trades]
trade_returns = np.array(trade_returns)
win_rate = (trade_returns > 0).mean() * 100
avg_win = trade_returns[trade_returns > 0].mean() * 100 if (trade_returns > 0).any() else 0
avg_loss = trade_returns[trade_returns < 0].mean() * 100 if (trade_returns < 0).any() else 0
max_win = trade_returns.max() * 100
max_loss = trade_returns.min() * 100
total_wins = trade_returns[trade_returns > 0].sum()
total_losses = abs(trade_returns[trade_returns < 0].sum())
profit_factor = total_wins / total_losses if total_losses > 0 else float('inf')
else:
win_rate = avg_win = avg_loss = max_win = max_loss = profit_factor = 0
# Max drawdown
running_max = bars['cum_strategy'].cummax()
drawdown = (bars['cum_strategy'] - running_max)
max_drawdown = drawdown.min() * 100
# Sharpe ratio
if bars['strategy_return'].std() > 0:
import numpy as np
sharpe = bars['strategy_return'].mean() / bars['strategy_return'].std() * np.sqrt(365 * 24)
else:
sharpe = 0
return {
'total_return': total_return,
'market_return': market_return,
'outperformance': total_return - market_return,
'sharpe_ratio': sharpe,
'max_drawdown': max_drawdown,
'total_trades': len(trades),
'win_rate': win_rate,
'profit_factor': profit_factor,
'avg_win': avg_win,
'avg_loss': avg_loss,
'max_win': max_win,
'max_loss': max_loss,
'time_in_market': (bars['position'] > 0).mean() * 100,
'trades': trades
}
def display_strategy_comparison(results):
"""Display side-by-side comparison"""
if len(results) < 2:
print("❌ Need at least 2 strategies to compare")
return
print("\n" + "=" * 100)
print("STRATEGY COMPARISON RESULTS")
print("=" * 100)
# Header
print(f"{'Metric':<25} {'Conservative':<20} {'Enhanced':<20} {'Difference':<15}")
print("-" * 100)
conservative = results.get('conservative', {})
enhanced = results.get('enhanced', {})
metrics = [
('Total Return', 'total_return', '%'),
('Market Return', 'market_return', '%'),
('Outperformance', 'outperformance', '%'),
('Sharpe Ratio', 'sharpe_ratio', ''),
('Max Drawdown', 'max_drawdown', '%'),
('Total Trades', 'total_trades', ''),
('Win Rate', 'win_rate', '%'),
('Profit Factor', 'profit_factor', ''),
('Avg Win', 'avg_win', '%'),
('Avg Loss', 'avg_loss', '%'),
('Max Win', 'max_win', '%'),
('Max Loss', 'max_loss', '%'),
('Time in Market', 'time_in_market', '%'),
]
for display_name, key, unit in metrics:
cons_val = conservative.get(key, 0)
enh_val = enhanced.get(key, 0)
diff = enh_val - cons_val
if unit == '%':
cons_str = f"{cons_val:>8.2f}%"
enh_str = f"{enh_val:>8.2f}%"
diff_str = f"{diff:>+8.2f}%"
else:
cons_str = f"{cons_val:>8.2f}"
enh_str = f"{enh_val:>8.2f}"
diff_str = f"{diff:>+8.2f}"
print(f"{display_name:<25} {cons_str:<20} {enh_str:<20} {diff_str:<15}")
print("=" * 100)
# Summary
print("\n📋 SUMMARY:")
if enhanced.get('total_return', 0) > conservative.get('total_return', 0):
winner = "Enhanced"
return_diff = enhanced.get('total_return', 0) - conservative.get('total_return', 0)
else:
winner = "Conservative"
return_diff = conservative.get('total_return', 0) - enhanced.get('total_return', 0)
print(f"🏆 Best performing strategy: {winner} (+{return_diff:.2f}% return)")
# Risk comparison
cons_risk = abs(conservative.get('max_drawdown', 0))
enh_risk = abs(enhanced.get('max_drawdown', 0))
if cons_risk < enh_risk:
print(f"🛡️ Lower risk strategy: Conservative ({cons_risk:.2f}% max drawdown)")
else:
print(f"🛡️ Lower risk strategy: Enhanced ({enh_risk:.2f}% max drawdown)")
print("\n💡 RECOMMENDATIONS:")
print("• Conservative: Better for letting big winners run, more relaxed exits")
print("• Enhanced: Better risk control, tighter stops for early losses")
print("• Choose based on your risk tolerance and market conditions")
if __name__ == "__main__":
run_strategy_comparison()

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"""
Configuration Package
Contains all configuration settings for the trading system.
"""

101
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"""
Trading Configuration Module
Contains all configuration settings for the trading system.
"""
from dataclasses import dataclass
from typing import Dict
import os
# Try to load environment variables if python-dotenv is available
try:
from dotenv import load_dotenv
load_dotenv()
except ImportError:
# python-dotenv not installed, will use os.getenv with defaults
pass
@dataclass
class AlpacaConfig:
"""Alpaca API configuration"""
api_key: str = os.getenv('ALPACA_API_KEY', 'PKXR08ET6CSGV5QFS89E')
secret_key: str = os.getenv('ALPACA_SECRET_KEY', '5ILlNGVM7WfPwJk8kPRlHhQwDO022H19RWBOkwgd')
base_url: str = 'https://paper-api.alpaca.markets' # Paper trading URL
data_url: str = 'https://data.alpaca.markets'
@dataclass
class TradingConfig:
"""Main trading configuration"""
# Strategy Selection
strategy_type: str = os.getenv('STRATEGY_TYPE', 'enhanced') # 'conservative' or 'enhanced'
# Symbol to trade
symbol: str = os.getenv('SYMBOL', 'ETH/USD')
# Position sizing
max_position_size: float = float(os.getenv('MAX_POSITION_SIZE', '0.95')) # Maximum 95% of portfolio
risk_per_trade: float = float(os.getenv('RISK_PER_TRADE', '0.02')) # Risk 2% per trade
# Trading parameters
trade_cost: float = 0.0005 # 0.05% trading cost
cooldown_hours: int = 24 # Hours between signals
# Risk management
max_drawdown_limit: float = float(os.getenv('MAX_DRAWDOWN_LIMIT', '0.15')) # Stop trading if 15% drawdown
daily_loss_limit: float = float(os.getenv('DAILY_LOSS_LIMIT', '0.05')) # Stop trading if 5% daily loss
# Timeframe
timeframe: str = 'Hour' # Trading timeframe
# Strategy parameters
ema_fast: int = 12
ema_slow: int = 30
ema_trend: int = 80
rsi_period: int = 14
macd_fast: int = 12
macd_slow: int = 26
macd_signal: int = 9
@dataclass
class StrategyConfig:
"""Strategy-specific configuration"""
# Entry conditions
min_trend_threshold: float = 1.02 # Must be 2% above major trend
min_momentum_threshold: float = 0.02 # 2% momentum required
rsi_oversold: float = 45
rsi_overbought: float = 75
volume_threshold: float = 0.7
# Exit conditions
trailing_stops: Dict[float, float] = None
emergency_stop_days: int = 7
emergency_stop_loss: float = -0.10
trend_break_threshold: float = 0.85
def __post_init__(self):
if self.trailing_stops is None:
self.trailing_stops = {
2.00: 0.60, # 200%+ profit: 40% trailing stop
1.50: 0.65, # 150%+ profit: 35% trailing stop
1.00: 0.70, # 100%+ profit: 30% trailing stop
0.75: 0.75, # 75%+ profit: 25% trailing stop
0.50: 0.78, # 50%+ profit: 22% trailing stop
0.25: 0.80, # 25%+ profit: 20% trailing stop
0.10: 0.85, # 10%+ profit: 15% trailing stop
0.00: 0.88 # < 10% profit: 12% trailing stop
}
@dataclass
class LoggingConfig:
"""Logging configuration"""
log_level: str = 'INFO'
log_file: str = 'logs/trading.log'
log_format: str = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
max_file_size: int = 10 * 1024 * 1024 # 10MB
backup_count: int = 5
# Global configuration instances
alpaca_config = AlpacaConfig()
trading_config = TradingConfig()
strategy_config = StrategyConfig()
logging_config = LoggingConfig()

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#!/usr/bin/env python3
"""
Main Trading Application
Entry point for the automated trading system.
"""
import sys
import time
import signal
import argparse
from datetime import datetime, timedelta
import logging
# Add src directory to path for imports
sys.path.append('src')
sys.path.append('config')
from src.logger_setup import setup_logging, log_trading_action, log_performance_metric
from src.trading_engine import TradingEngine
from config.trading_config import trading_config
# Global variables for graceful shutdown
running = True
trading_engine = None
def signal_handler(signum, frame):
"""Handle shutdown signals gracefully"""
global running
logger = logging.getLogger(__name__)
logger.info(f"Received signal {signum}, shutting down gracefully...")
running = False
def run_backtesting_mode(strategy_type=None):
"""Run backtesting using historical data"""
logger = logging.getLogger(__name__)
strategy_name = strategy_type or trading_config.strategy_type
logger.info(f"Starting backtesting mode with {strategy_name} strategy")
try:
# Import backtesting logic from existing files
from src.data_handler import DataHandler
from src.strategy_factory import get_strategy
data_handler = DataHandler()
strategy = get_strategy(strategy_type)
# Get historical data (180 days for comprehensive backtest)
bars = data_handler.get_historical_data(days=180)
if bars.empty:
logger.error("No historical data available for backtesting")
return
# Calculate technical indicators
bars = data_handler.calculate_technical_indicators(bars)
# Generate signals
bars = strategy.generate_signals(bars)
# Run backtest simulation
results = run_backtest_simulation(bars, strategy)
# Display results
display_backtest_results(results, strategy.name)
except Exception as e:
logger.error(f"Error in backtesting mode: {e}")
def run_backtest_simulation(bars, strategy):
"""Run backtest simulation with position management"""
logger = logging.getLogger(__name__)
logger.info(f"Running backtest simulation with {strategy.name}")
# Initialize tracking variables
position = 0
entry_price = None
highest_price_since_entry = None
bars_since_entry = 0
# Initialize position tracking
bars['position'] = 0
for i in range(1, len(bars)):
current_price = bars['close'].iloc[i]
if bars['buy_signal'].iloc[i] and position == 0:
# Use strategy-specific entry conditions
if strategy.get_entry_conditions(bars, i):
position = 1
entry_price = current_price
highest_price_since_entry = current_price
bars_since_entry = 0
elif position == 1:
bars_since_entry += 1
# Update highest price
if current_price > highest_price_since_entry:
highest_price_since_entry = current_price
# Use strategy-specific exit conditions
should_exit, exit_reason = strategy.should_exit_position(
current_price=current_price,
entry_price=entry_price,
highest_price=highest_price_since_entry,
bars_since_entry=bars_since_entry,
sell_signal=bars['sell_signal'].iloc[i],
ema_trend=bars['ema_trend'].iloc[i]
)
if should_exit:
position = 0
entry_price = None
highest_price_since_entry = None
bars_since_entry = 0
bars.iloc[i, bars.columns.get_loc('position')] = position
# Calculate returns
bars['market_return'] = bars['close'].pct_change()
bars['strategy_return'] = bars['position'].shift(1) * bars['market_return']
# Apply trading costs
trade_cost = trading_config.trade_cost
bars['position_change'] = bars['position'].diff().abs()
bars['costs'] = bars['position_change'] * trade_cost
bars['strategy_return'] = bars['strategy_return'] - bars['costs']
# Cumulative returns
bars['cum_market'] = (1 + bars['market_return']).cumprod() - 1
bars['cum_strategy'] = (1 + bars['strategy_return']).cumprod() - 1
return calculate_performance_metrics(bars)
def calculate_performance_metrics(bars):
"""Calculate comprehensive performance metrics"""
# Basic returns
total_return = bars['cum_strategy'].iloc[-1] * 100
market_return_pct = bars['cum_market'].iloc[-1] * 100
# Trade analysis
position_changes = bars['position'].diff()
buys = (position_changes == 1).sum()
sells = (position_changes == -1).sum()
# Calculate individual trade returns
trade_returns = []
entry_price = None
for i in range(len(bars)):
if position_changes.iloc[i] == 1:
entry_price = bars['close'].iloc[i]
elif position_changes.iloc[i] == -1 and entry_price is not None:
exit_price = bars['close'].iloc[i]
trade_return = (exit_price / entry_price - 1) - (2 * trading_config.trade_cost)
trade_returns.append(trade_return)
entry_price = None
# Trade statistics
if len(trade_returns) > 0:
import numpy as np
trade_returns = np.array(trade_returns)
win_rate = (trade_returns > 0).mean() * 100
avg_win = trade_returns[trade_returns > 0].mean() * 100 if (trade_returns > 0).any() else 0
avg_loss = trade_returns[trade_returns < 0].mean() * 100 if (trade_returns < 0).any() else 0
max_win = trade_returns.max() * 100
max_loss = trade_returns.min() * 100
total_wins = trade_returns[trade_returns > 0].sum()
total_losses = abs(trade_returns[trade_returns < 0].sum())
profit_factor = total_wins / total_losses if total_losses > 0 else float('inf')
else:
win_rate = avg_win = avg_loss = max_win = max_loss = profit_factor = 0
# Risk metrics
if bars['strategy_return'].std() > 0:
import numpy as np
sharpe = bars['strategy_return'].mean() / bars['strategy_return'].std() * np.sqrt(365 * 24)
else:
sharpe = 0
# Max drawdown
running_max = bars['cum_strategy'].cummax()
drawdown = (bars['cum_strategy'] - running_max)
max_drawdown = drawdown.min() * 100
# Time in market
time_in_market = (bars['position'] > 0).mean() * 100
return {
'total_return': total_return,
'market_return': market_return_pct,
'outperformance': total_return - market_return_pct,
'sharpe_ratio': sharpe,
'max_drawdown': max_drawdown,
'total_trades': len(trade_returns),
'win_rate': win_rate,
'profit_factor': profit_factor,
'avg_win': avg_win,
'avg_loss': avg_loss,
'max_win': max_win,
'max_loss': max_loss,
'time_in_market': time_in_market,
'buy_signals': buys,
'sell_signals': sells
}
def display_backtest_results(results, strategy_name):
"""Display backtest results in a formatted way"""
print("\n" + "="*60)
print(f"BACKTEST RESULTS - {strategy_name.upper()}")
print("="*60)
print(f"Strategy Return: {results['total_return']:>8.2f}%")
print(f"Market Return: {results['market_return']:>8.2f}%")
print(f"Outperformance: {results['outperformance']:>8.2f}%")
print(f"Sharpe Ratio: {results['sharpe_ratio']:>8.2f}")
print(f"Max Drawdown: {results['max_drawdown']:>8.2f}%")
print(f"Total Trades: {results['total_trades']:>8}")
print(f"Win Rate: {results['win_rate']:>8.2f}%")
print(f"Profit Factor: {results['profit_factor']:>8.2f}")
print(f"Avg Win: {results['avg_win']:>8.2f}%")
print(f"Avg Loss: {results['avg_loss']:>8.2f}%")
print(f"Max Win: {results['max_win']:>8.2f}%")
print(f"Max Loss: {results['max_loss']:>8.2f}%")
print(f"Time in Market: {results['time_in_market']:>8.1f}%")
print("="*60)
def run_live_trading_mode():
"""Run live trading mode"""
global trading_engine, running
logger = logging.getLogger(__name__)
logger.info("Starting live trading mode")
try:
# Initialize trading engine
trading_engine = TradingEngine(paper_trading=True)
# Main trading loop
cycle_count = 0
last_summary_time = datetime.now()
while running:
try:
# Run trading cycle
cycle_results = trading_engine.run_trading_cycle()
cycle_count += 1
# Log cycle results
if cycle_results['success']:
logger.info(f"Cycle {cycle_count} completed successfully")
if cycle_results['actions_taken']:
log_trading_action("trading_cycle", {
'cycle': cycle_count,
'actions': cycle_results['actions_taken'],
'price': cycle_results['current_price'],
'signals': cycle_results['signals']
})
# Log performance metrics periodically
if datetime.now() - last_summary_time > timedelta(hours=1):
summary = trading_engine.get_trading_summary()
if summary['current_position'] > 0 and summary['entry_price']:
current_profit = (cycle_results['current_price'] / summary['entry_price'] - 1) * 100
log_performance_metric("unrealized_pnl_pct", current_profit, {
'position': summary['current_position'],
'entry_price': summary['entry_price'],
'current_price': cycle_results['current_price']
})
last_summary_time = datetime.now()
else:
logger.error(f"Cycle {cycle_count} failed")
# Wait before next cycle (3600 seconds = 1 hour for hourly timeframe)
sleep_duration = 3600 # 1 hour
for _ in range(sleep_duration):
if not running:
break
time.sleep(1)
except KeyboardInterrupt:
logger.info("Received keyboard interrupt")
running = False
break
except Exception as e:
logger.error(f"Error in trading loop: {e}")
time.sleep(60) # Wait 1 minute before retrying
logger.info("Live trading mode stopped")
except Exception as e:
logger.error(f"Error in live trading mode: {e}")
def main():
"""Main application entry point"""
# Setup command line arguments
parser = argparse.ArgumentParser(description='Automated Trading System')
parser.add_argument('--mode', choices=['live', 'backtest'], default='backtest',
help='Trading mode: live or backtest (default: backtest)')
parser.add_argument('--strategy', choices=['conservative', 'enhanced'],
default=None, help='Strategy type (default: from config)')
parser.add_argument('--log-level', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'],
default='INFO', help='Logging level (default: INFO)')
args = parser.parse_args()
# Setup logging
setup_logging()
logger = logging.getLogger(__name__)
# Set log level from command line
logging.getLogger().setLevel(getattr(logging, args.log_level))
# Setup signal handlers for graceful shutdown
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
logger.info(f"Starting trading system in {args.mode} mode")
# Override strategy type if specified
if args.strategy:
logger.info(f"Strategy override: {args.strategy}")
# We'll pass this to the functions that need it
try:
if args.mode == 'backtest':
run_backtesting_mode(args.strategy)
elif args.mode == 'live':
run_live_trading_mode()
except Exception as e:
logger.error(f"Critical error in main: {e}")
return 1
logger.info("Trading system shutdown complete")
return 0
if __name__ == "__main__":
exit_code = main()
sys.exit(exit_code)

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# Core trading and data analysis libraries
alpaca-trade-api>=3.1.1
pandas>=1.5.0
numpy>=1.21.0
# Configuration and environment management
python-dotenv>=0.19.0
# Optional: For enhanced data analysis and visualization (uncomment if needed)
# matplotlib>=3.5.0
# seaborn>=0.11.0
# plotly>=5.0.0
# Optional: For advanced analytics (uncomment if needed)
# scikit-learn>=1.0.0
# scipy>=1.7.0
# Optional: For web interface (uncomment if needed)
# flask>=2.0.0
# dash>=2.0.0

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#!/usr/bin/env python3
"""
Quick Start Script
Easy way to run the trading system with common configurations.
"""
import sys
import subprocess
def install_requirements():
"""Install required packages"""
print("Installing requirements...")
try:
subprocess.check_call([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"])
print("✓ Requirements installed successfully")
return True
except subprocess.CalledProcessError as e:
print(f"✗ Failed to install requirements: {e}")
return False
def run_test():
"""Run system test"""
print("Running system test...")
try:
result = subprocess.run([sys.executable, "test_system.py"], capture_output=True, text=True)
print(result.stdout)
if result.stderr:
print("Errors:", result.stderr)
return result.returncode == 0
except Exception as e:
print(f"✗ Test failed: {e}")
return False
def run_backtest():
"""Run backtesting"""
print("Running backtest...")
try:
result = subprocess.run([sys.executable, "main.py", "--mode", "backtest"],
capture_output=False, text=True)
return result.returncode == 0
except Exception as e:
print(f"✗ Backtest failed: {e}")
return False
def run_live():
"""Run live trading"""
print("Starting live trading (Paper Trading)...")
print("Press Ctrl+C to stop")
try:
result = subprocess.run([sys.executable, "main.py", "--mode", "live"],
capture_output=False, text=True)
return result.returncode == 0
except KeyboardInterrupt:
print("\nLive trading stopped by user")
return True
except Exception as e:
print(f"✗ Live trading failed: {e}")
return False
def main():
"""Main menu"""
print("=" * 50)
print("AUTOMATED TRADING SYSTEM - QUICK START")
print("=" * 50)
while True:
print("\nSelect an option:")
print("1. Install requirements")
print("2. Run system test")
print("3. Run backtesting")
print("4. Start live trading (Paper)")
print("5. Exit")
choice = input("\nEnter your choice (1-5): ").strip()
if choice == "1":
install_requirements()
elif choice == "2":
run_test()
elif choice == "3":
run_backtest()
elif choice == "4":
print("\n⚠️ WARNING: This will start live paper trading!")
print("Make sure you have:")
print("- Valid Alpaca paper trading API keys")
print("- Reviewed the configuration settings")
print("- Tested with backtesting first")
confirm = input("\nContinue? (yes/no): ").strip().lower()
if confirm in ['yes', 'y']:
run_live()
else:
print("Live trading cancelled")
elif choice == "5":
print("Goodbye!")
break
else:
print("Invalid choice. Please enter 1-5.")
if __name__ == "__main__":
main()

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"""
Trading System Package
A professional automated trading system for cryptocurrency trading.
"""
__version__ = "1.0.0"
__author__ = "Trading System"
__description__ = "Automated cryptocurrency trading system with risk management"

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"""
Data Handler Module
Handles all market data fetching and processing operations.
"""
import pandas as pd
from datetime import datetime, timedelta
import logging
import alpaca_trade_api as tradeapi
from config.trading_config import alpaca_config, trading_config
logger = logging.getLogger(__name__)
class DataHandler:
"""Handles market data operations"""
def __init__(self):
"""Initialize the data handler with Alpaca API"""
self.api = tradeapi.REST(
alpaca_config.api_key,
alpaca_config.secret_key,
base_url=alpaca_config.data_url
)
logger.info("DataHandler initialized successfully")
def get_historical_data(self,
symbol: str = None,
days: int = 180,
timeframe: str = None) -> pd.DataFrame:
"""
Fetch historical market data
Args:
symbol: Trading symbol (default from config)
days: Number of days of historical data
timeframe: Data timeframe (default from config)
Returns:
DataFrame with OHLCV data and datetime index
"""
symbol = symbol or trading_config.symbol
timeframe = timeframe or trading_config.timeframe
end_date = datetime.now().strftime('%Y-%m-%d')
start_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
try:
logger.info(f"Fetching {days} days of {symbol} data from {start_date} to {end_date}")
# Convert timeframe string to Alpaca TimeFrame
tf_map = {
'Minute': tradeapi.TimeFrame.Minute,
'Hour': tradeapi.TimeFrame.Hour,
'Day': tradeapi.TimeFrame.Day
}
tf = tf_map.get(timeframe, tradeapi.TimeFrame.Hour)
bars = self.api.get_crypto_bars(symbol, tf, start_date, end_date).df
bars.index = pd.to_datetime(bars.index)
logger.info(f"Successfully fetched {len(bars)} bars of data")
return bars
except Exception as e:
logger.error(f"Error fetching historical data: {e}")
raise
def calculate_technical_indicators(self, bars: pd.DataFrame) -> pd.DataFrame:
"""
Calculate all technical indicators for the strategy
Args:
bars: OHLCV DataFrame
Returns:
DataFrame with technical indicators added
"""
logger.info("Calculating technical indicators")
try:
# EMAs
bars['ema_fast'] = bars['close'].ewm(span=trading_config.ema_fast).mean()
bars['ema_slow'] = bars['close'].ewm(span=trading_config.ema_slow).mean()
bars['ema_trend'] = bars['close'].ewm(span=trading_config.ema_trend).mean()
# RSI
bars['rsi'] = self._calculate_rsi(bars['close'], trading_config.rsi_period)
# MACD
ema_12 = bars['close'].ewm(span=trading_config.macd_fast).mean()
ema_26 = bars['close'].ewm(span=trading_config.macd_slow).mean()
bars['macd'] = ema_12 - ema_26
bars['macd_signal'] = bars['macd'].ewm(span=trading_config.macd_signal).mean()
# Price momentum
bars['price_momentum_6h'] = bars['close'].pct_change(periods=6)
# Volume indicators
bars['volume_ma'] = bars['volume'].rolling(window=20).mean()
bars['volume_ok'] = bars['volume'] > bars['volume_ma'] * 0.7
logger.info("Technical indicators calculated successfully")
return bars
except Exception as e:
logger.error(f"Error calculating technical indicators: {e}")
raise
@staticmethod
def _calculate_rsi(prices: pd.Series, window: int = 14) -> pd.Series:
"""Calculate RSI indicator"""
delta = prices.diff()
gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
rs = gain / loss
rsi = 100 - (100 / (1 + rs))
return rsi
def get_latest_price(self, symbol: str = None) -> float:
"""
Get the latest price for a symbol
Args:
symbol: Trading symbol (default from config)
Returns:
Latest price as float
"""
symbol = symbol or trading_config.symbol
try:
# Get latest bar
bars = self.api.get_crypto_bars(
symbol,
tradeapi.TimeFrame.Minute,
datetime.now() - timedelta(minutes=5),
datetime.now()
).df
if not bars.empty:
return float(bars['close'].iloc[-1])
else:
logger.warning(f"No recent data available for {symbol}")
return None
except Exception as e:
logger.error(f"Error fetching latest price for {symbol}: {e}")
return None
def validate_data_quality(self, bars: pd.DataFrame) -> bool:
"""
Validate the quality of market data
Args:
bars: Market data DataFrame
Returns:
True if data quality is acceptable
"""
if bars.empty:
logger.error("Data is empty")
return False
# Check for excessive missing data
missing_pct = bars.isnull().sum().sum() / (len(bars) * len(bars.columns))
if missing_pct > 0.05: # More than 5% missing
logger.warning(f"High percentage of missing data: {missing_pct:.2%}")
return False
# Check for reasonable price ranges
price_change = bars['close'].pct_change().abs()
extreme_moves = (price_change > 0.20).sum() # 20% moves
if extreme_moves > len(bars) * 0.01: # More than 1% of data points
logger.warning(f"Excessive extreme price moves detected: {extreme_moves}")
return False
logger.info("Data quality validation passed")
return True

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"""
Logging Setup Module
Configures comprehensive logging for the trading system.
"""
import logging
import logging.handlers
import os
from datetime import datetime
from config.trading_config import logging_config
def setup_logging():
"""Setup comprehensive logging configuration"""
# Create logs directory if it doesn't exist
log_dir = os.path.dirname(logging_config.log_file)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
# Create root logger
root_logger = logging.getLogger()
root_logger.setLevel(getattr(logging, logging_config.log_level))
# Remove any existing handlers
for handler in root_logger.handlers[:]:
root_logger.removeHandler(handler)
# Create formatter
formatter = logging.Formatter(logging_config.log_format)
# File handler with rotation
file_handler = logging.handlers.RotatingFileHandler(
logging_config.log_file,
maxBytes=logging_config.max_file_size,
backupCount=logging_config.backup_count
)
file_handler.setLevel(logging.DEBUG)
file_handler.setFormatter(formatter)
root_logger.addHandler(file_handler)
# Console handler
console_handler = logging.StreamHandler()
console_handler.setLevel(getattr(logging, logging_config.log_level))
console_handler.setFormatter(formatter)
root_logger.addHandler(console_handler)
# Create separate handlers for different types of logs
# Trading actions log
trading_logger = logging.getLogger('trading')
trading_handler = logging.handlers.RotatingFileHandler(
'logs/trading_actions.log',
maxBytes=logging_config.max_file_size,
backupCount=logging_config.backup_count
)
trading_handler.setFormatter(formatter)
trading_logger.addHandler(trading_handler)
# Risk management log
risk_logger = logging.getLogger('risk')
risk_handler = logging.handlers.RotatingFileHandler(
'logs/risk_management.log',
maxBytes=logging_config.max_file_size,
backupCount=logging_config.backup_count
)
risk_handler.setFormatter(formatter)
risk_logger.addHandler(risk_handler)
# Performance log
performance_logger = logging.getLogger('performance')
performance_handler = logging.handlers.RotatingFileHandler(
'logs/performance.log',
maxBytes=logging_config.max_file_size,
backupCount=logging_config.backup_count
)
performance_handler.setFormatter(formatter)
performance_logger.addHandler(performance_handler)
# Log startup message
root_logger.info("="*50)
root_logger.info("Trading System Started")
root_logger.info(f"Timestamp: {datetime.now()}")
root_logger.info("="*50)
def log_trading_action(action: str, details: dict):
"""Log trading actions with details"""
trading_logger = logging.getLogger('trading')
log_msg = f"ACTION: {action}"
for key, value in details.items():
log_msg += f" | {key}: {value}"
trading_logger.info(log_msg)
def log_risk_event(event: str, details: dict):
"""Log risk management events"""
risk_logger = logging.getLogger('risk')
log_msg = f"RISK: {event}"
for key, value in details.items():
log_msg += f" | {key}: {value}"
risk_logger.warning(log_msg)
def log_performance_metric(metric: str, value: float, additional_info: dict = None):
"""Log performance metrics"""
performance_logger = logging.getLogger('performance')
log_msg = f"METRIC: {metric} = {value}"
if additional_info:
for key, val in additional_info.items():
log_msg += f" | {key}: {val}"
performance_logger.info(log_msg)

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"""
Risk Management Module
Handles all risk management operations including position sizing,
drawdown monitoring, and emergency stops.
"""
import pandas as pd
import logging
from datetime import datetime
from config.trading_config import trading_config
logger = logging.getLogger(__name__)
class RiskManager:
"""
Risk management system to protect capital and enforce trading rules
"""
def __init__(self):
"""Initialize risk manager"""
self.daily_pnl = 0.0
self.session_start_value = None
self.max_drawdown_reached = False
self.trading_halted = False
self.last_reset_date = datetime.now().date()
logger.info("RiskManager initialized")
def check_risk_limits(self,
current_portfolio_value: float,
peak_portfolio_value: float) -> dict:
"""
Check all risk limits and return status
Args:
current_portfolio_value: Current portfolio value
peak_portfolio_value: Historical peak portfolio value
Returns:
Dictionary with risk status information
"""
risk_status = {
'trading_allowed': True,
'warnings': [],
'violations': [],
'current_drawdown': 0.0,
'daily_pnl_pct': 0.0
}
try:
# Calculate current drawdown
if peak_portfolio_value > 0:
current_drawdown = (peak_portfolio_value - current_portfolio_value) / peak_portfolio_value
risk_status['current_drawdown'] = current_drawdown
# Check maximum drawdown limit
if current_drawdown > trading_config.max_drawdown_limit:
risk_status['violations'].append(
f"Maximum drawdown exceeded: {current_drawdown:.2%} > {trading_config.max_drawdown_limit:.2%}"
)
risk_status['trading_allowed'] = False
self.max_drawdown_reached = True
elif current_drawdown > trading_config.max_drawdown_limit * 0.8:
risk_status['warnings'].append(
f"Approaching maximum drawdown: {current_drawdown:.2%}"
)
# Check daily loss limit
if self.session_start_value is not None:
daily_pnl_pct = (current_portfolio_value - self.session_start_value) / self.session_start_value
risk_status['daily_pnl_pct'] = daily_pnl_pct
if daily_pnl_pct < -trading_config.daily_loss_limit:
risk_status['violations'].append(
f"Daily loss limit exceeded: {daily_pnl_pct:.2%} < -{trading_config.daily_loss_limit:.2%}"
)
risk_status['trading_allowed'] = False
elif daily_pnl_pct < -trading_config.daily_loss_limit * 0.8:
risk_status['warnings'].append(
f"Approaching daily loss limit: {daily_pnl_pct:.2%}"
)
# Update trading status
if risk_status['violations']:
self.trading_halted = True
logger.warning(f"Trading halted due to risk violations: {risk_status['violations']}")
if risk_status['warnings']:
logger.warning(f"Risk warnings: {risk_status['warnings']}")
return risk_status
except Exception as e:
logger.error(f"Error in risk limit check: {e}")
# Fail safe - halt trading on error
risk_status['trading_allowed'] = False
risk_status['violations'].append(f"Risk check error: {e}")
return risk_status
def calculate_position_size(self,
account_value: float,
asset_price: float,
volatility: float = None,
risk_override: float = None) -> dict:
"""
Calculate appropriate position size based on risk parameters
Args:
account_value: Current account value
asset_price: Current asset price
volatility: Asset volatility (optional)
risk_override: Risk amount override (optional)
Returns:
Dictionary with position sizing information
"""
try:
# Base risk amount
if risk_override:
risk_amount = risk_override
else:
risk_amount = account_value * trading_config.risk_per_trade
# Maximum position value
max_position_value = account_value * trading_config.max_position_size
# Calculate position size based on stop loss
# Using conservative 8% stop loss for position sizing
stop_loss_pct = 0.08
if volatility:
# Adjust stop loss based on volatility
stop_loss_pct = max(0.05, min(0.15, volatility * 2))
position_value = risk_amount / stop_loss_pct
# Apply maximum position limit
position_value = min(position_value, max_position_value)
# Calculate number of shares/units
position_size = position_value / asset_price
position_info = {
'position_value': position_value,
'position_size': position_size,
'risk_amount': risk_amount,
'stop_loss_pct': stop_loss_pct,
'risk_per_trade_pct': risk_amount / account_value,
'position_pct_of_account': position_value / account_value
}
logger.info(f"Position sizing: ${position_value:.2f} "
f"({position_info['position_pct_of_account']:.1%} of account, "
f"{position_info['risk_per_trade_pct']:.1%} risk)")
return position_info
except Exception as e:
logger.error(f"Error calculating position size: {e}")
return {
'position_value': 0,
'position_size': 0,
'risk_amount': 0,
'stop_loss_pct': 0,
'risk_per_trade_pct': 0,
'position_pct_of_account': 0
}
def update_daily_tracking(self, current_portfolio_value: float):
"""
Update daily tracking metrics
Args:
current_portfolio_value: Current portfolio value
"""
current_date = datetime.now().date()
# Reset daily tracking if new day
if current_date != self.last_reset_date:
self.session_start_value = current_portfolio_value
self.daily_pnl = 0.0
self.last_reset_date = current_date
logger.info(f"Daily tracking reset for {current_date}")
# Set session start value if not set
if self.session_start_value is None:
self.session_start_value = current_portfolio_value
def should_reduce_position(self,
current_drawdown: float,
consecutive_losses: int) -> bool:
"""
Determine if position size should be reduced
Args:
current_drawdown: Current portfolio drawdown
consecutive_losses: Number of consecutive losing trades
Returns:
True if position should be reduced
"""
# Reduce position if significant drawdown
if current_drawdown > trading_config.max_drawdown_limit * 0.5:
logger.info("Recommending position size reduction due to drawdown")
return True
# Reduce position after multiple consecutive losses
if consecutive_losses >= 3:
logger.info("Recommending position size reduction due to consecutive losses")
return True
return False
def get_emergency_exit_signal(self,
bars: pd.DataFrame,
current_position_pct: float) -> bool:
"""
Check for emergency exit conditions
Args:
bars: Market data DataFrame
current_position_pct: Current position as percentage of portfolio
Returns:
True if emergency exit is recommended
"""
if bars.empty or len(bars) < 20:
return False
try:
# Check for extreme volatility
recent_returns = bars['close'].pct_change().tail(20)
volatility = recent_returns.std()
if volatility > 0.05: # 5% hourly volatility
logger.warning(f"High volatility detected: {volatility:.3f}")
return True
# Check for flash crash conditions
max_decline = recent_returns.min()
if max_decline < -0.15: # 15% decline in one period
logger.warning(f"Flash crash condition detected: {max_decline:.2%}")
return True
# Check for sustained negative momentum
momentum_periods = bars['price_momentum_6h'].tail(10)
if (momentum_periods < -0.02).sum() >= 7: # 7 out of 10 periods negative
logger.warning("Sustained negative momentum detected")
return True
return False
except Exception as e:
logger.error(f"Error in emergency exit check: {e}")
return True # Fail safe - recommend exit on error
def reset_risk_state(self):
"""Reset risk management state (use carefully)"""
self.max_drawdown_reached = False
self.trading_halted = False
self.daily_pnl = 0.0
logger.warning("Risk management state has been reset")
def get_risk_summary(self) -> dict:
"""Get current risk management summary"""
return {
'trading_halted': self.trading_halted,
'max_drawdown_reached': self.max_drawdown_reached,
'daily_pnl': self.daily_pnl,
'session_start_value': self.session_start_value,
'last_reset_date': self.last_reset_date.strftime('%Y-%m-%d')
}

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"""
Trading Strategy Module
Contains the core trading strategy logic and signal generation.
"""
import pandas as pd
import logging
from config.trading_config import strategy_config, trading_config
logger = logging.getLogger(__name__)
class TrendFollowingStrategy:
"""
Trend-following strategy implementation
Generates buy/sell signals based on EMA, RSI, MACD, and momentum indicators
"""
def __init__(self):
"""Initialize the strategy"""
self.last_signal_idx = -50
self.position = 0
self.entry_price = None
self.highest_price_since_entry = None
self.bars_since_entry = 0
logger.info("TrendFollowingStrategy initialized")
def generate_signals(self, bars: pd.DataFrame) -> pd.DataFrame:
"""
Generate buy and sell signals based on strategy rules
Args:
bars: DataFrame with OHLCV data and technical indicators
Returns:
DataFrame with buy_signal and sell_signal columns added
"""
logger.info("Generating trading signals")
try:
# Generate raw signals
bars = self._generate_buy_conditions(bars)
bars = self._generate_sell_conditions(bars)
# Apply cooldown period
bars = self._apply_signal_cooldown(bars)
logger.info("Trading signals generated successfully")
return bars
except Exception as e:
logger.error(f"Error generating signals: {e}")
raise
def _generate_buy_conditions(self, bars: pd.DataFrame) -> pd.DataFrame:
"""Generate buy signal conditions"""
# Basic trend requirement - must be above major trend
trend_condition = bars['close'] > bars['ema_trend']
# Entry trigger conditions
ema_bullish = (
(bars['ema_fast'] > bars['ema_slow']) &
(bars['close'] > bars['ema_fast']) &
(bars['rsi'] > strategy_config.rsi_oversold) &
(bars['rsi'] < strategy_config.rsi_overbought)
)
macd_bullish = (
(bars['macd'] > bars['macd_signal']) &
(bars['price_momentum_6h'] > strategy_config.min_momentum_threshold) &
(bars['rsi'] > 40) & (bars['rsi'] < 70)
)
breakout_condition = (
(bars['close'] > bars['ema_fast']) &
(bars['close'] > bars['ema_slow']) &
(bars['close'] > bars['ema_trend']) &
(bars['volume_ok']) &
(bars['price_momentum_6h'] > 0.015)
)
# Combine all conditions
buy_conditions = trend_condition & (ema_bullish | macd_bullish | breakout_condition)
bars['buy_signal_raw'] = buy_conditions
return bars
def _generate_sell_conditions(self, bars: pd.DataFrame) -> pd.DataFrame:
"""Generate sell signal conditions"""
# EMA death cross with major breakdown
ema_bearish = (
(bars['ema_fast'] < bars['ema_slow']) &
(bars['close'] < bars['ema_trend'] * 0.90) &
(bars['rsi'] < 35)
)
# Sustained breakdown below major trend
trend_breakdown = (
(bars['close'] < bars['ema_trend'] * 0.88) &
(bars['price_momentum_6h'] < -0.04)
)
# Extreme overbought with reversal signs
overbought_reversal = (
(bars['rsi'] > 85) &
(bars['rsi'].shift(1) > bars['rsi']) &
(bars['rsi'].shift(2) > bars['rsi'].shift(1)) &
(bars['rsi'].shift(3) > bars['rsi'].shift(2)) &
(bars['macd'] < bars['macd_signal']) &
(bars['price_momentum_6h'] < -0.02)
)
# Combine all sell conditions
sell_conditions = ema_bearish | trend_breakdown | overbought_reversal
bars['sell_signal_raw'] = sell_conditions
return bars
def _apply_signal_cooldown(self, bars: pd.DataFrame) -> pd.DataFrame:
"""Apply cooldown period between signals"""
bars['buy_signal'] = False
bars['sell_signal'] = False
last_signal_idx = -50
cooldown_hours = trading_config.cooldown_hours
for i in range(len(bars)):
if i - last_signal_idx >= cooldown_hours:
if bars['buy_signal_raw'].iloc[i]:
bars.iloc[i, bars.columns.get_loc('buy_signal')] = True
last_signal_idx = i
elif bars['sell_signal_raw'].iloc[i]:
bars.iloc[i, bars.columns.get_loc('sell_signal')] = True
last_signal_idx = i
return bars
def calculate_position_size(self,
current_price: float,
account_value: float,
risk_amount: float = None) -> float:
"""
Calculate position size based on risk management rules
Args:
current_price: Current asset price
account_value: Total account value
risk_amount: Amount to risk (optional, uses config default)
Returns:
Position size in dollars
"""
if risk_amount is None:
risk_amount = account_value * trading_config.risk_per_trade
# Maximum position size
max_position = account_value * trading_config.max_position_size
# Risk-based position size (simplified for trend following)
# Using 10% stop loss for position sizing
stop_loss_pct = 0.10
position_size = risk_amount / stop_loss_pct
# Apply maximum position limit
position_size = min(position_size, max_position)
logger.info(f"Calculated position size: ${position_size:.2f}")
return position_size
def get_trailing_stop_price(self,
entry_price: float,
highest_price: float,
current_profit_pct: float) -> float:
"""
Calculate trailing stop price based on profit level
Args:
entry_price: Original entry price
highest_price: Highest price since entry
current_profit_pct: Current profit percentage
Returns:
Trailing stop price
"""
# Get appropriate trailing stop percentage
trailing_stop_pct = 0.88 # Default
for profit_threshold in sorted(strategy_config.trailing_stops.keys(), reverse=True):
if current_profit_pct >= profit_threshold:
trailing_stop_pct = strategy_config.trailing_stops[profit_threshold]
break
trailing_stop_price = highest_price * trailing_stop_pct
logger.debug(f"Trailing stop: {trailing_stop_price:.2f} "
f"(profit: {current_profit_pct:.2%}, "
f"stop %: {(1-trailing_stop_pct):.1%})")
return trailing_stop_price
def should_exit_position(self,
current_price: float,
entry_price: float,
highest_price: float,
bars_since_entry: int,
sell_signal: bool,
ema_trend: float) -> tuple:
"""
Determine if position should be exited
Args:
current_price: Current asset price
entry_price: Entry price
highest_price: Highest price since entry
bars_since_entry: Number of bars since entry
sell_signal: Whether sell signal is active
ema_trend: Current EMA trend value
Returns:
Tuple of (should_exit: bool, exit_reason: str)
"""
profit_pct = (current_price / entry_price - 1)
# Get trailing stop price
trailing_stop_price = self.get_trailing_stop_price(
entry_price, highest_price, profit_pct
)
# Check exit conditions
if sell_signal:
return True, "sell_signal"
if current_price <= trailing_stop_price:
return True, f"trailing_stop (profit: {profit_pct:.2%})"
# Emergency time-based stops
emergency_hours = strategy_config.emergency_stop_days * 24
if (bars_since_entry >= emergency_hours and
profit_pct < strategy_config.emergency_stop_loss):
return True, f"emergency_stop ({bars_since_entry}h, {profit_pct:.2%})"
# Major trend break
if current_price < ema_trend * strategy_config.trend_break_threshold:
return True, f"trend_break ({profit_pct:.2%})"
return False, ""
def reset_position_state(self):
"""Reset position tracking state"""
self.position = 0
self.entry_price = None
self.highest_price_since_entry = None
self.bars_since_entry = 0
logger.info("Position state reset")

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"""
Conservative Trading Strategy
Based on backtesting_short.py - Simple but effective approach with relaxed exit conditions.
"""
import pandas as pd
import logging
logger = logging.getLogger(__name__)
class ConservativeTrendStrategy:
"""
Conservative trend-following strategy
- Simple entry conditions
- Relaxed trailing stops (18% minimum)
- Very relaxed emergency stops (28 days, -30%)
- Focuses on letting winners run
"""
def __init__(self):
"""Initialize the conservative strategy"""
self.name = "Conservative Trend Strategy"
self.last_signal_idx = -50
self.position = 0
self.entry_price = None
self.highest_price_since_entry = None
self.bars_since_entry = 0
logger.info(f"{self.name} initialized")
def generate_signals(self, bars: pd.DataFrame) -> pd.DataFrame:
"""Generate buy and sell signals"""
logger.info("Generating conservative strategy signals")
try:
bars = self._generate_buy_conditions(bars)
bars = self._generate_sell_conditions(bars)
bars = self._apply_signal_cooldown(bars)
logger.info("Conservative strategy signals generated successfully")
return bars
except Exception as e:
logger.error(f"Error generating conservative signals: {e}")
raise
def _generate_buy_conditions(self, bars: pd.DataFrame) -> pd.DataFrame:
"""Generate conservative buy conditions - same as original"""
# Basic trend requirement - must be above major trend (no tolerance)
trend_condition = bars['close'] > bars['ema_trend']
# Entry trigger conditions (same as original backtesting_short.py)
ema_bullish = (
(bars['ema_fast'] > bars['ema_slow']) &
(bars['close'] > bars['ema_fast']) &
(bars['rsi'] > 45) & (bars['rsi'] < 75) # Balanced RSI
)
macd_bullish = (
(bars['macd'] > bars['macd_signal']) &
(bars['price_momentum_6h'] > 0.02) & # 2% momentum required
(bars['rsi'] > 40) & (bars['rsi'] < 70) # Not overbought
)
breakout_condition = (
(bars['close'] > bars['ema_fast']) &
(bars['close'] > bars['ema_slow']) &
(bars['close'] > bars['ema_trend']) &
(bars['volume_ok']) &
(bars['price_momentum_6h'] > 0.015) # Strong momentum
)
# Combine all conditions
buy_conditions = trend_condition & (ema_bullish | macd_bullish | breakout_condition)
bars['buy_signal_raw'] = buy_conditions
return bars
def _generate_sell_conditions(self, bars: pd.DataFrame) -> pd.DataFrame:
"""Generate conservative sell conditions - ultra-conservative"""
# EMA death cross with major breakdown
ema_bearish = (
(bars['ema_fast'] < bars['ema_slow']) &
(bars['close'] < bars['ema_trend'] * 0.90) & # 10% below trend
(bars['rsi'] < 35) # Very weak momentum
)
# Sustained breakdown below major trend
trend_breakdown = (
(bars['close'] < bars['ema_trend'] * 0.88) & # 12% below major trend
(bars['price_momentum_6h'] < -0.04) # Stronger decline
)
# Extreme overbought with strong reversal signs
overbought_reversal = (
(bars['rsi'] > 85) & # Higher threshold
(bars['rsi'].shift(1) > bars['rsi']) & # RSI declining
(bars['rsi'].shift(2) > bars['rsi'].shift(1)) & # For 2 periods
(bars['rsi'].shift(3) > bars['rsi'].shift(2)) & # For 3 periods
(bars['macd'] < bars['macd_signal']) & # MACD bearish
(bars['price_momentum_6h'] < -0.02) # Plus price weakness
)
# Combine all sell conditions
sell_conditions = ema_bearish | trend_breakdown | overbought_reversal
bars['sell_signal_raw'] = sell_conditions
return bars
def _apply_signal_cooldown(self, bars: pd.DataFrame) -> pd.DataFrame:
"""Apply 24-hour cooldown between signals"""
bars['buy_signal'] = False
bars['sell_signal'] = False
last_signal_idx = -50
cooldown_hours = 24 # Conservative: 24-hour cooldown
for i in range(len(bars)):
if i - last_signal_idx >= cooldown_hours:
if bars['buy_signal_raw'].iloc[i]:
bars.iloc[i, bars.columns.get_loc('buy_signal')] = True
last_signal_idx = i
elif bars['sell_signal_raw'].iloc[i]:
bars.iloc[i, bars.columns.get_loc('sell_signal')] = True
last_signal_idx = i
return bars
def get_entry_conditions(self, bars: pd.DataFrame, i: int) -> bool:
"""Conservative entry - must be above trend"""
return bars['close'].iloc[i] > bars['ema_trend'].iloc[i]
def get_trailing_stop_price(self,
entry_price: float,
highest_price: float,
current_profit_pct: float) -> float:
"""Conservative trailing stops - let big winners run!"""
# Conservative trailing stops (wider stops to let winners run)
if current_profit_pct > 2.00: # 200%+ profit: 40% trailing stop
trailing_stop_pct = 0.60
elif current_profit_pct > 1.50: # 150%+ profit: 35% trailing stop
trailing_stop_pct = 0.65
elif current_profit_pct > 1.00: # 100%+ profit: 30% trailing stop
trailing_stop_pct = 0.70
elif current_profit_pct > 0.75: # 75%+ profit: 25% trailing stop
trailing_stop_pct = 0.75
elif current_profit_pct > 0.50: # 50%+ profit: 22% trailing stop
trailing_stop_pct = 0.78
elif current_profit_pct > 0.25: # 25%+ profit: 20% trailing stop
trailing_stop_pct = 0.80
else: # < 25% profit: 18% trailing stop
trailing_stop_pct = 0.82
trailing_stop_price = highest_price * trailing_stop_pct
logger.debug(f"Conservative trailing stop: {trailing_stop_price:.2f} "
f"(profit: {current_profit_pct:.2%}, stop: {(1-trailing_stop_pct):.1%})")
return trailing_stop_price
def should_exit_position(self,
current_price: float,
entry_price: float,
highest_price: float,
bars_since_entry: int,
sell_signal: bool,
ema_trend: float) -> tuple:
"""Conservative exit conditions - very relaxed"""
profit_pct = (current_price / entry_price - 1)
trailing_stop_price = self.get_trailing_stop_price(entry_price, highest_price, profit_pct)
# Check exit conditions
if sell_signal:
return True, "sell_signal"
if current_price <= trailing_stop_price:
return True, f"trailing_stop (profit: {profit_pct:.2%})"
# Very relaxed emergency stops
if bars_since_entry >= 672 and profit_pct < -0.30: # 28 days & -30%
return True, f"emergency_stop_28d ({profit_pct:.2%})"
# Major trend break (very relaxed)
if current_price < ema_trend * 0.75: # 25% below major trend
return True, f"major_trend_break ({profit_pct:.2%})"
return False, ""
def reset_position_state(self):
"""Reset position tracking state"""
self.position = 0
self.entry_price = None
self.highest_price_since_entry = None
self.bars_since_entry = 0
logger.info(f"{self.name} position state reset")

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"""
Enhanced Trading Strategy
Based on backtesting_long.py - More sophisticated approach with better risk management.
"""
import pandas as pd
import logging
logger = logging.getLogger(__name__)
class EnhancedTrendStrategy:
"""
Enhanced trend-following strategy
- Stricter entry conditions (2% above major trend)
- Tighter trailing stops with better risk management
- More aggressive time-based stops (7-14 days)
- Better loss control for early positions
"""
def __init__(self):
"""Initialize the enhanced strategy"""
self.name = "Enhanced Trend Strategy"
self.last_signal_idx = -50
self.position = 0
self.entry_price = None
self.highest_price_since_entry = None
self.bars_since_entry = 0
logger.info(f"{self.name} initialized")
def generate_signals(self, bars: pd.DataFrame) -> pd.DataFrame:
"""Generate buy and sell signals"""
logger.info("Generating enhanced strategy signals")
try:
bars = self._generate_buy_conditions(bars)
bars = self._generate_sell_conditions(bars)
bars = self._apply_signal_cooldown(bars)
logger.info("Enhanced strategy signals generated successfully")
return bars
except Exception as e:
logger.error(f"Error generating enhanced signals: {e}")
raise
def _generate_buy_conditions(self, bars: pd.DataFrame) -> pd.DataFrame:
"""Generate enhanced buy conditions - same as original"""
# Basic trend requirement - must be above major trend (no tolerance)
trend_condition = bars['close'] > bars['ema_trend']
# Entry trigger conditions (same as original)
ema_bullish = (
(bars['ema_fast'] > bars['ema_slow']) &
(bars['close'] > bars['ema_fast']) &
(bars['rsi'] > 45) & (bars['rsi'] < 75) # Balanced RSI
)
macd_bullish = (
(bars['macd'] > bars['macd_signal']) &
(bars['price_momentum_6h'] > 0.02) & # 2% momentum required
(bars['rsi'] > 40) & (bars['rsi'] < 70) # Not overbought
)
breakout_condition = (
(bars['close'] > bars['ema_fast']) &
(bars['close'] > bars['ema_slow']) &
(bars['close'] > bars['ema_trend']) &
(bars['volume_ok']) &
(bars['price_momentum_6h'] > 0.015) # Strong momentum
)
# Combine all conditions
buy_conditions = trend_condition & (ema_bullish | macd_bullish | breakout_condition)
bars['buy_signal_raw'] = buy_conditions
return bars
def _generate_sell_conditions(self, bars: pd.DataFrame) -> pd.DataFrame:
"""Generate enhanced sell conditions - same as conservative"""
# EMA death cross with major breakdown
ema_bearish = (
(bars['ema_fast'] < bars['ema_slow']) &
(bars['close'] < bars['ema_trend'] * 0.90) & # 10% below trend
(bars['rsi'] < 35) # Very weak momentum
)
# Sustained breakdown below major trend
trend_breakdown = (
(bars['close'] < bars['ema_trend'] * 0.88) & # 12% below major trend
(bars['price_momentum_6h'] < -0.04) # Stronger decline
)
# Extreme overbought with strong reversal signs
overbought_reversal = (
(bars['rsi'] > 85) & # Higher threshold
(bars['rsi'].shift(1) > bars['rsi']) & # RSI declining
(bars['rsi'].shift(2) > bars['rsi'].shift(1)) & # For 2 periods
(bars['rsi'].shift(3) > bars['rsi'].shift(2)) & # For 3 periods
(bars['macd'] < bars['macd_signal']) & # MACD bearish
(bars['price_momentum_6h'] < -0.02) # Plus price weakness
)
# Combine all sell conditions
sell_conditions = ema_bearish | trend_breakdown | overbought_reversal
bars['sell_signal_raw'] = sell_conditions
return bars
def _apply_signal_cooldown(self, bars: pd.DataFrame) -> pd.DataFrame:
"""Apply 24-hour cooldown between signals"""
bars['buy_signal'] = False
bars['sell_signal'] = False
last_signal_idx = -50
cooldown_hours = 24 # Same 24-hour cooldown
for i in range(len(bars)):
if i - last_signal_idx >= cooldown_hours:
if bars['buy_signal_raw'].iloc[i]:
bars.iloc[i, bars.columns.get_loc('buy_signal')] = True
last_signal_idx = i
elif bars['sell_signal_raw'].iloc[i]:
bars.iloc[i, bars.columns.get_loc('sell_signal')] = True
last_signal_idx = i
return bars
def get_entry_conditions(self, bars: pd.DataFrame, i: int) -> bool:
"""Enhanced entry - MORE STRICT (2% above trend + fast EMA above slow)"""
return (bars['close'].iloc[i] > bars['ema_trend'].iloc[i] * 1.02 and # 2% above major trend
bars['ema_fast'].iloc[i] > bars['ema_slow'].iloc[i] * 1.01) # Fast EMA clearly above slow
def get_trailing_stop_price(self,
entry_price: float,
highest_price: float,
current_profit_pct: float) -> float:
"""Enhanced trailing stops with better risk management"""
# Enhanced trailing stops with better risk management
if current_profit_pct > 2.00: # 200%+ profit: 40% trailing stop
trailing_stop_pct = 0.60
elif current_profit_pct > 1.50: # 150%+ profit: 35% trailing stop
trailing_stop_pct = 0.65
elif current_profit_pct > 1.00: # 100%+ profit: 30% trailing stop
trailing_stop_pct = 0.70
elif current_profit_pct > 0.75: # 75%+ profit: 25% trailing stop
trailing_stop_pct = 0.75
elif current_profit_pct > 0.50: # 50%+ profit: 22% trailing stop
trailing_stop_pct = 0.78
elif current_profit_pct > 0.25: # 25%+ profit: 20% trailing stop
trailing_stop_pct = 0.80
elif current_profit_pct > 0.10: # 10%+ profit: 15% trailing stop
trailing_stop_pct = 0.85
else: # < 10% profit: 12% trailing stop (tighter for early losses)
trailing_stop_pct = 0.88
trailing_stop_price = highest_price * trailing_stop_pct
logger.debug(f"Enhanced trailing stop: {trailing_stop_price:.2f} "
f"(profit: {current_profit_pct:.2%}, stop: {(1-trailing_stop_pct):.1%})")
return trailing_stop_price
def should_exit_position(self,
current_price: float,
entry_price: float,
highest_price: float,
bars_since_entry: int,
sell_signal: bool,
ema_trend: float) -> tuple:
"""Enhanced exit conditions - better loss control"""
profit_pct = (current_price / entry_price - 1)
trailing_stop_price = self.get_trailing_stop_price(entry_price, highest_price, profit_pct)
# Check exit conditions
if sell_signal:
return True, "sell_signal"
if current_price <= trailing_stop_price:
return True, f"trailing_stop (profit: {profit_pct:.2%})"
# Tighter time-based stops to prevent long drawdowns
if bars_since_entry >= 168 and profit_pct < -0.10: # 7 days & -10%
return True, f"emergency_stop_7d ({profit_pct:.2%})"
if bars_since_entry >= 336 and profit_pct < -0.05: # 14 days & -5%
return True, f"emergency_stop_14d ({profit_pct:.2%})"
# Major trend break (tighter)
if current_price < ema_trend * 0.85: # 15% below major trend
return True, f"trend_break_15pct ({profit_pct:.2%})"
return False, ""
def reset_position_state(self):
"""Reset position tracking state"""
self.position = 0
self.entry_price = None
self.highest_price_since_entry = None
self.bars_since_entry = 0
logger.info(f"{self.name} position state reset")

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"""
Strategy Factory
Handles creation and selection of different trading strategies.
"""
import logging
from config.trading_config import trading_config
from src.strategy_conservative import ConservativeTrendStrategy
from src.strategy_enhanced import EnhancedTrendStrategy
logger = logging.getLogger(__name__)
class StrategyFactory:
"""Factory class for creating trading strategies"""
AVAILABLE_STRATEGIES = {
'conservative': ConservativeTrendStrategy,
'enhanced': EnhancedTrendStrategy
}
@classmethod
def create_strategy(cls, strategy_type: str = None):
"""
Create a strategy instance based on configuration
Args:
strategy_type: Strategy type override (optional)
Returns:
Strategy instance
"""
strategy_type = strategy_type or trading_config.strategy_type
if strategy_type not in cls.AVAILABLE_STRATEGIES:
available = ', '.join(cls.AVAILABLE_STRATEGIES.keys())
raise ValueError(f"Unknown strategy type: {strategy_type}. Available: {available}")
strategy_class = cls.AVAILABLE_STRATEGIES[strategy_type]
strategy = strategy_class()
logger.info(f"Created strategy: {strategy.name}")
return strategy
@classmethod
def get_available_strategies(cls) -> list:
"""Get list of available strategy types"""
return list(cls.AVAILABLE_STRATEGIES.keys())
@classmethod
def get_strategy_description(cls, strategy_type: str) -> str:
"""Get description of a strategy type"""
descriptions = {
'conservative': "Conservative approach with relaxed exits, lets big winners run (18-40% trailing stops, 28-day emergency stops)",
'enhanced': "Enhanced approach with better risk control, tighter stops for early losses (12-40% trailing stops, 7-14 day emergency stops)"
}
return descriptions.get(strategy_type, "No description available")
def get_strategy(strategy_type: str = None):
"""
Convenience function to get a strategy instance
Args:
strategy_type: Strategy type ('conservative' or 'enhanced')
Returns:
Strategy instance
"""
return StrategyFactory.create_strategy(strategy_type)

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"""
Trading Engine Module
Core trading engine that executes trades and manages positions.
"""
import logging
from datetime import datetime
import alpaca_trade_api as tradeapi
from config.trading_config import alpaca_config, trading_config
from src.data_handler import DataHandler
from src.strategy_factory import get_strategy
from src.risk_manager import RiskManager
logger = logging.getLogger(__name__)
class TradingEngine:
"""
Main trading engine that coordinates all trading operations
"""
def __init__(self, paper_trading: bool = True):
"""
Initialize the trading engine
Args:
paper_trading: Whether to use paper trading (default: True)
"""
self.paper_trading = paper_trading
# Initialize components
self.data_handler = DataHandler()
self.strategy = get_strategy() # Use strategy factory
self.risk_manager = RiskManager()
# Initialize Alpaca trading API
base_url = alpaca_config.base_url if paper_trading else 'https://api.alpaca.markets'
self.trading_api = tradeapi.REST(
alpaca_config.api_key,
alpaca_config.secret_key,
base_url=base_url
)
# Trading state
self.current_position = 0
self.entry_price = None
self.highest_price_since_entry = None
self.bars_since_entry = 0
self.consecutive_losses = 0
self.peak_portfolio_value = None
logger.info(f"TradingEngine initialized (paper_trading: {paper_trading})")
def get_account_info(self) -> dict:
"""Get current account information"""
try:
account = self.trading_api.get_account()
account_info = {
'equity': float(account.equity),
'cash': float(account.cash),
'buying_power': float(account.buying_power),
'day_trade_count': int(account.day_trade_count),
'pattern_day_trader': account.pattern_day_trader
}
# Update peak portfolio value
if self.peak_portfolio_value is None or account_info['equity'] > self.peak_portfolio_value:
self.peak_portfolio_value = account_info['equity']
# Update risk manager daily tracking
self.risk_manager.update_daily_tracking(account_info['equity'])
return account_info
except Exception as e:
logger.error(f"Error getting account info: {e}")
return None
def get_current_position(self, symbol: str = None) -> dict:
"""Get current position information"""
symbol = symbol or trading_config.symbol
try:
positions = self.trading_api.list_positions()
for position in positions:
if position.symbol == symbol:
return {
'symbol': position.symbol,
'qty': float(position.qty),
'market_value': float(position.market_value),
'avg_entry_price': float(position.avg_entry_price),
'unrealized_pl': float(position.unrealized_pl),
'unrealized_plpc': float(position.unrealized_plpc)
}
# No position found
return {
'symbol': symbol,
'qty': 0,
'market_value': 0,
'avg_entry_price': 0,
'unrealized_pl': 0,
'unrealized_plpc': 0
}
except Exception as e:
logger.error(f"Error getting position info: {e}")
return None
def place_order(self,
symbol: str,
qty: float,
side: str,
order_type: str = 'market',
time_in_force: str = 'gtc') -> dict:
"""
Place a trading order
Args:
symbol: Trading symbol
qty: Quantity to trade
side: 'buy' or 'sell'
order_type: Order type (default: 'market')
time_in_force: Time in force (default: 'gtc')
Returns:
Order information dictionary
"""
try:
logger.info(f"Placing {side} order: {qty:.6f} {symbol} at {order_type}")
# Convert quantity to string with appropriate precision
qty_str = f"{qty:.6f}"
order = self.trading_api.submit_order(
symbol=symbol,
qty=qty_str,
side=side,
type=order_type,
time_in_force=time_in_force
)
order_info = {
'id': order.id,
'symbol': order.symbol,
'qty': float(order.qty),
'side': order.side,
'order_type': order.order_type,
'status': order.status,
'submitted_at': order.submitted_at
}
logger.info(f"Order placed successfully: {order_info}")
return order_info
except Exception as e:
logger.error(f"Error placing order: {e}")
return None
def execute_buy_signal(self, current_price: float, account_value: float) -> bool:
"""
Execute a buy signal
Args:
current_price: Current asset price
account_value: Current account value
Returns:
True if order was placed successfully
"""
try:
# Check if we already have a position
if self.current_position > 0:
logger.info("Buy signal ignored - already have position")
return False
# Calculate position size
position_info = self.risk_manager.calculate_position_size(
account_value, current_price
)
position_size = position_info['position_size']
if position_size <= 0:
logger.warning("Position size is 0 or negative - skipping buy")
return False
# Place buy order
order_info = self.place_order(
symbol=trading_config.symbol,
qty=position_size,
side='buy'
)
if order_info:
# Update position tracking
self.current_position = position_size
self.entry_price = current_price
self.highest_price_since_entry = current_price
self.bars_since_entry = 0
logger.info(f"Buy executed: {position_size:.6f} at ${current_price:.2f}")
return True
return False
except Exception as e:
logger.error(f"Error executing buy signal: {e}")
return False
def execute_sell_signal(self, current_price: float, reason: str = "") -> bool:
"""
Execute a sell signal
Args:
current_price: Current asset price
reason: Reason for selling
Returns:
True if order was placed successfully
"""
try:
# Check if we have a position to sell
if self.current_position <= 0:
logger.info("Sell signal ignored - no position to sell")
return False
# Place sell order
order_info = self.place_order(
symbol=trading_config.symbol,
qty=self.current_position,
side='sell'
)
if order_info:
# Calculate trade performance
if self.entry_price:
profit_pct = (current_price / self.entry_price - 1)
profit_amount = (current_price - self.entry_price) * self.current_position
logger.info(f"Sell executed: {self.current_position:.6f} at ${current_price:.2f}")
logger.info(f"Trade result: {profit_pct:.2%} (${profit_amount:.2f}) - {reason}")
# Update consecutive losses counter
if profit_pct < 0:
self.consecutive_losses += 1
else:
self.consecutive_losses = 0
# Reset position tracking
self.strategy.reset_position_state()
self.current_position = 0
self.entry_price = None
self.highest_price_since_entry = None
self.bars_since_entry = 0
return True
return False
except Exception as e:
logger.error(f"Error executing sell signal: {e}")
return False
def run_trading_cycle(self) -> dict:
"""
Run one complete trading cycle
Returns:
Dictionary with cycle results
"""
cycle_results = {
'timestamp': datetime.now(),
'success': False,
'account_info': None,
'position_info': None,
'signals': {'buy': False, 'sell': False},
'actions_taken': [],
'risk_status': None,
'current_price': None
}
try:
logger.info("Starting trading cycle")
# Get account information
account_info = self.get_account_info()
if not account_info:
logger.error("Failed to get account information")
return cycle_results
cycle_results['account_info'] = account_info
# Check risk limits
risk_status = self.risk_manager.check_risk_limits(
account_info['equity'],
self.peak_portfolio_value or account_info['equity']
)
cycle_results['risk_status'] = risk_status
if not risk_status['trading_allowed']:
logger.warning("Trading not allowed due to risk limits")
cycle_results['actions_taken'].append("trading_halted")
return cycle_results
# Get current position
position_info = self.get_current_position()
if not position_info:
logger.error("Failed to get position information")
return cycle_results
cycle_results['position_info'] = position_info
# Sync position tracking with actual position
self.current_position = position_info['qty']
if self.current_position > 0 and not self.entry_price:
self.entry_price = position_info['avg_entry_price']
self.highest_price_since_entry = position_info['avg_entry_price']
# Get market data and generate signals
bars = self.data_handler.get_historical_data(days=30)
if bars.empty:
logger.error("No market data available")
return cycle_results
# Validate data quality
if not self.data_handler.validate_data_quality(bars):
logger.error("Data quality check failed")
return cycle_results
# Calculate technical indicators
bars = self.data_handler.calculate_technical_indicators(bars)
# Generate trading signals
bars = self.strategy.generate_signals(bars)
# Get current price and latest signals
current_price = bars['close'].iloc[-1]
buy_signal = bars['buy_signal'].iloc[-1]
sell_signal = bars['sell_signal'].iloc[-1]
cycle_results['current_price'] = current_price
cycle_results['signals'] = {'buy': buy_signal, 'sell': sell_signal}
# Update tracking for existing position
if self.current_position > 0 and self.entry_price:
self.bars_since_entry += 1
if current_price > self.highest_price_since_entry:
self.highest_price_since_entry = current_price
# Check exit conditions using strategy-specific method
should_exit, exit_reason = self.strategy.should_exit_position(
current_price=current_price,
entry_price=self.entry_price,
highest_price=self.highest_price_since_entry,
bars_since_entry=self.bars_since_entry,
sell_signal=sell_signal,
ema_trend=bars['ema_trend'].iloc[-1]
)
if should_exit:
if self.execute_sell_signal(current_price, exit_reason):
cycle_results['actions_taken'].append(f"sell_{exit_reason}")
# Check for buy signal (only if no position)
elif buy_signal and self.current_position == 0:
# Use strategy-specific entry validation
if self.strategy.get_entry_conditions(bars, len(bars)-1):
if self.execute_buy_signal(current_price, account_info['equity']):
cycle_results['actions_taken'].append("buy_signal")
# Check emergency exit conditions
if (self.current_position > 0 and
self.risk_manager.get_emergency_exit_signal(bars, self.current_position)):
if self.execute_sell_signal(current_price, "emergency_exit"):
cycle_results['actions_taken'].append("emergency_exit")
cycle_results['success'] = True
logger.info("Trading cycle completed successfully")
except Exception as e:
logger.error(f"Error in trading cycle: {e}")
cycle_results['actions_taken'].append(f"error: {str(e)}")
return cycle_results
def get_trading_summary(self) -> dict:
"""Get current trading status summary"""
return {
'current_position': self.current_position,
'entry_price': self.entry_price,
'highest_price_since_entry': self.highest_price_since_entry,
'bars_since_entry': self.bars_since_entry,
'consecutive_losses': self.consecutive_losses,
'peak_portfolio_value': self.peak_portfolio_value,
'risk_summary': self.risk_manager.get_risk_summary()
}

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"""
System Test Script
Verifies that the trading system is properly installed and configured.
"""
import sys
import os
def test_imports():
"""Test that all required modules can be imported"""
print("Testing imports...")
try:
import pandas as pd
print("✓ pandas imported successfully")
except ImportError as e:
print(f"✗ pandas import failed: {e}")
return False
try:
import alpaca_trade_api as tradeapi
print("✓ alpaca_trade_api imported successfully")
except ImportError as e:
print(f"✗ alpaca_trade_api import failed: {e}")
return False
# Test local imports
sys.path.append('src')
sys.path.append('config')
try:
from config.trading_config import trading_config, alpaca_config
print("✓ trading configuration imported successfully")
except ImportError as e:
print(f"✗ trading configuration import failed: {e}")
return False
try:
from src.data_handler import DataHandler
print("✓ DataHandler imported successfully")
except ImportError as e:
print(f"✗ DataHandler import failed: {e}")
return False
try:
from src.strategy import TrendFollowingStrategy
print("✓ TrendFollowingStrategy imported successfully")
except ImportError as e:
print(f"✗ TrendFollowingStrategy import failed: {e}")
return False
try:
from src.risk_manager import RiskManager
print("✓ RiskManager imported successfully")
except ImportError as e:
print(f"✗ RiskManager import failed: {e}")
return False
try:
from src.trading_engine import TradingEngine
print("✓ TradingEngine imported successfully")
except ImportError as e:
print(f"✗ TradingEngine import failed: {e}")
return False
return True
def test_configuration():
"""Test configuration settings"""
print("\nTesting configuration...")
try:
from config.trading_config import trading_config, alpaca_config
print(f"✓ Trading symbol: {trading_config.symbol}")
print(f"✓ Risk per trade: {trading_config.risk_per_trade}")
print(f"✓ Max position size: {trading_config.max_position_size}")
print(f"✓ Alpaca base URL: {alpaca_config.base_url}")
return True
except Exception as e:
print(f"✗ Configuration test failed: {e}")
return False
def test_data_connection():
"""Test connection to data source"""
print("\nTesting data connection...")
try:
from src.data_handler import DataHandler
data_handler = DataHandler()
print("✓ DataHandler initialized")
# Test getting a small amount of recent data
try:
bars = data_handler.get_historical_data(days=1)
if not bars.empty:
print(f"✓ Data connection successful - got {len(bars)} data points")
print(f"✓ Latest price: ${bars['close'].iloc[-1]:.2f}")
return True
else:
print("✗ No data received")
return False
except Exception as e:
print(f"✗ Data fetch failed: {e}")
return False
except Exception as e:
print(f"✗ DataHandler initialization failed: {e}")
return False
def test_directory_structure():
"""Test that required directories exist"""
print("\nTesting directory structure...")
required_dirs = ['src', 'config', 'logs']
for dir_name in required_dirs:
if os.path.exists(dir_name):
print(f"✓ {dir_name}/ directory exists")
else:
print(f"✗ {dir_name}/ directory missing")
return False
return True
def main():
"""Run all tests"""
print("=" * 50)
print("TRADING SYSTEM INSTALLATION TEST")
print("=" * 50)
tests = [
("Directory Structure", test_directory_structure),
("Module Imports", test_imports),
("Configuration", test_configuration),
("Data Connection", test_data_connection)
]
results = []
for test_name, test_func in tests:
print(f"\n--- {test_name} ---")
try:
result = test_func()
results.append((test_name, result))
except Exception as e:
print(f"✗ {test_name} failed with exception: {e}")
results.append((test_name, False))
print("\n" + "=" * 50)
print("TEST SUMMARY")
print("=" * 50)
all_passed = True
for test_name, result in results:
status = "PASS" if result else "FAIL"
symbol = "✓" if result else "✗"
print(f"{symbol} {test_name}: {status}")
if not result:
all_passed = False
print("=" * 50)
if all_passed:
print("🎉 All tests passed! The trading system is ready to use.")
print("\nNext steps:")
print("1. Review configuration in config/trading_config.py")
print("2. Run backtesting: python main.py --mode backtest")
print("3. For live trading: python main.py --mode live")
else:
print("❌ Some tests failed. Please fix the issues before proceeding.")
print("\nCommon solutions:")
print("1. Install dependencies: pip install -r requirements.txt")
print("2. Check API credentials in .env file")
print("3. Verify internet connection")
return 0 if all_passed else 1
if __name__ == "__main__":
exit_code = main()
sys.exit(exit_code)