🎮 Algame-MRIMADHA

📈 A Modern Algorithmic Trading & Backtesting Framework

By traders, for traders - Making algo trading accessible without compromising power

Installation • Quick Start • Documentation • Examples • Contributing

🌟 Why Algame?

After years of frustration with existing backtesting frameworks that were either too rigid or too complex, we created Algame with one goal: make algorithmic trading accessible while keeping it powerful.

Through our journey, we learned:

Traders need flexibility in strategy development
Real-world trading requires robust testing
Visual tools accelerate development
Code shouldn't be a barrier to algo trading

Our Solution? A Framework That:

🎯 Adapts to your style (code, visual, or both)
🔧 Handles complexity behind simple interfaces
📊 Provides professional-grade analysis
🔄 Integrates with tools you already use

🚀 Installation

pip install algame-mrimadha

Development Version:

pip install git+https://github.com/Legend101Zz/Algame-MRIMADHA.git

Development Setup

Quick Start for Developers

Clone the repository:

git clone https://github.com/Legend101Zz/Algame-MRIMADHA.git
cd Algame-MRIMADHA

Create and activate a virtual environment:

# Windows
python -m venv venv
venv\Scripts\activate

# Linux/Mac
python -m venv venv
source venv/bin/activate

Run the development setup script:

python scripts/dev_setup.py

Running the Package

After setup, you can:

Start the GUI:

python -m algame

Run the quick start example:

python examples/quickstart.py

Run tests:

pytest tests/

Project Structure

algame-MRIMADHA/
├── src/
│   └── algame/          # Main package directory
├── tests/               # Test files
├── examples/            # Example scripts
├── scripts/             # Development scripts
├── docs/                # Documentation
└── requirements/        # Requirement files

Contributing

Create a new branch for your feature/fix:

git checkout -b feature-name

Make your changes and run tests:

pytest tests/

Submit a pull request to the main repository.

Common Development Tasks

Run specific test module:

pytest tests/core/test_engine.py

Run with coverage report:

pytest --cov=algame tests/

Start GUI in debug mode:

python -m algame --debug

Build documentation:

cd docs
make html

Troubleshooting Development Setup

If you encounter issues:

Make sure you're in the virtual environment
Try removing and reinstalling dependencies:

pip uninstall algame-mrimadha
pip install -e .

Check the logs in ~/.algame/logs/

For more help, please open an issue on GitHub.

🏃‍♂️ Quick Start

Code Your First Strategy

from algame.strategy import StrategyBase
from algame.core import EngineManager
from algame.data import YahooData

class SimpleSMAStrategy(StrategyBase):
    def initialize(self):
        # Add 20-day SMA indicator
        self.sma = self.add_indicator('SMA', self.data.Close, period=20)

    def next(self):
        # Buy when price crosses above SMA
        if self.data.Close[-1] > self.sma[-1] and \
           self.data.Close[-2] <= self.sma[-2]:
            self.buy()

        # Sell when price crosses below SMA
        elif self.data.Close[-1] < self.sma[-1] and \
             self.data.Close[-2] >= self.sma[-2]:
            self.sell()

# Load data
data = YahooData.download('AAPL', '2020-01-01', '2023-12-31')

# Run backtest
engine = EngineManager()
results = engine.run_backtest(SimpleSMAStrategy, data)

# Print results
print(f"Total Return: {results.metrics['total_return']:.2f}%")
print(f"Sharpe Ratio: {results.metrics['sharpe_ratio']:.2f}")

Or Use the GUI

from algame.gui import start_app

start_app()

✨ Key Features

🎯 Multi-Everything Support

Multi-Asset Trading

# Trade multiple assets simultaneously
data = {
    'AAPL': yahoo.download('AAPL'),
    'GOOGL': yahoo.download('GOOGL'),
    'BTC-USD': yahoo.download('BTC-USD')
}

class MultiAssetStrategy(StrategyBase):
    def next(self):
        for symbol, asset_data in self.data.items():
            # Your logic here
            pass

Multi-Timeframe Analysis

# Mix timeframes in your strategy
class MultiTimeframeStrategy(StrategyBase):
    def initialize(self):
        # Daily timeframe
        self.daily_sma = self.add_indicator('SMA', self.data['1d'].Close, 20)
        # Hourly timeframe
        self.hourly_rsi = self.add_indicator('RSI', self.data['1h'].Close, 14)

🔌 Pluggable Architecture

Custom Data Sources

from algame.data import DataSourceBase

class MyDataSource(DataSourceBase):
    def fetch_data(self, symbol, start, end):
        # Your data fetching logic
        pass

# Register and use
register_data_source('my_source', MyDataSource)
data = load_data('AAPL', source='my_source')

Custom Indicators

from algame.indicators import IndicatorBase

class CustomIndicator(IndicatorBase):
    def calculate(self, data):
        # Your indicator logic
        return result

# Use in strategy
class MyStrategy(StrategyBase):
    def initialize(self):
        self.custom = self.add_indicator(CustomIndicator, self.data.Close)

🎨 Strategy Building

Visual Builder

Template-Based Development

from algame.strategy import MomentumTemplate

class MyStrategy(MomentumTemplate):
    def setup_parameters(self):
        self.lookback = 20
        self.threshold = 0.02

    def generate_signals(self):
        # Customize momentum logic
        pass

🔄 PineScript Converter

Convert your TradingView strategies:

from algame.tools.converter import convert_strategy

# PineScript code
pine_code = """
//@version=5
strategy("My Strategy", overlay=true)

// Inputs
fast_length = input(10, "Fast Length")
slow_length = input(20, "Slow Length")

// Calculations
fast_ma = ta.sma(close, fast_length)
slow_ma = ta.sma(close, slow_length)

// Entry conditions
if ta.crossover(fast_ma, slow_ma)
    strategy.entry("Long", strategy.long)

if ta.crossunder(fast_ma, slow_ma)
    strategy.close("Long")
"""

# Convert to Python
python_code = convert_strategy(pine_code)
print(python_code)

📊 Advanced Analysis

from algame.analysis import PerformanceMetrics, RiskAnalysis

# Calculate metrics
metrics = PerformanceMetrics(results.equity_curve, results.trades)
print(f"Sharpe Ratio: {metrics.sharpe_ratio:.2f}")
print(f"Max Drawdown: {metrics.max_drawdown:.2f}%")
print(f"Win Rate: {metrics.win_rate:.1f}%")

# Risk analysis
risk = RiskAnalysis(results.returns, results.positions)
optimal_size = risk.calculate_position_size(
    risk_per_trade=0.02,  # 2% risk per trade
    stop_loss=0.05        # 5% stop loss
)

📘 Documentation

API Reference

Core APIs

Engine API

The engine API provides the core backtesting functionality.

EngineManager

from algame.core import EngineManager

manager = EngineManager(config=None)

Methods:

run_backtest(strategy, data, parameters=None): Run a backtest

results = manager.run_backtest(
    strategy=MyStrategy,
    data=market_data,
    parameters={'sma_period': 20}
)

optimize_strategy(strategy, parameter_space, **kwargs): Optimize strategy parameters

results = manager.optimize_strategy(
    strategy=MyStrategy,
    parameter_space={
        'sma_period': range(10, 50, 5),
        'rsi_period': range(10, 30, 5)
    },
    metric='sharpe_ratio'
)

BacktestResult

Contains backtest results and performance metrics.

Attributes:

equity_curve: Equity curve as pandas Series
trades: List of Trade objects
metrics: Dictionary of performance metrics
drawdowns: Drawdown series
returns: Return series

Data API

The data API handles market data management.

DataManager

from algame.core.data import DataManager

manager = DataManager()

Methods:

get_data(symbol, source='yahoo', **kwargs): Load market data

data = manager.get_data(
    symbol='AAPL',
    source='yahoo',
    start='2020-01-01',
    end='2023-12-31'
)

register_source(source_name, source_class): Register custom data source
```
manager.register_source('my_source', MyDataSource)
```

MarketData

Container for market data.

Methods:

resample(timeframe): Resample to new timeframe
add_indicator(name, function, *args): Add technical indicator
validate(): Validate data quality

Strategy API

The strategy API defines the interface for trading strategies.

StrategyBase

Base class for all trading strategies.

from algame.strategy import StrategyBase

class MyStrategy(StrategyBase):
    def initialize(self):
        """Initialize strategy components."""
        pass

    def next(self):
        """Generate trading signals."""
        pass

Methods:

initialize(): Setup strategy (indicators, parameters)
next(): Generate trading signals
buy(size=1.0, **kwargs): Place buy order
sell(size=1.0, **kwargs): Place sell order
close(): Close current position

Analysis API

The analysis API provides performance analysis tools.

PerformanceMetrics

from algame.analysis import PerformanceMetrics

metrics = PerformanceMetrics(equity_curve, trades)

Metrics:

Total Return
Annual Return
Sharpe Ratio
Sortino Ratio
Max Drawdown
Win Rate
Profit Factor

RiskAnalysis

from algame.analysis import RiskAnalysis

risk = RiskAnalysis(returns, positions)

Methods:

calculate_position_size(risk_per_trade, stop_loss)
stress_test(scenarios)
analyze_drawdowns()

GUI API

The GUI API provides the graphical interface components.

MainWindow

from algame.gui import MainWindow

window = MainWindow(root)

Components:

DataPanel: Data management
StrategyPanel: Strategy development
ResultsPanel: Results analysis
ConverterPanel: PineScript conversion

Tools API

Additional utilities and tools.

PineScriptConverter

from algame.tools.converter import PineScriptConverter

converter = PineScriptConverter()

Methods:

convert(pine_code): Convert PineScript to Python
convert_file(input_file, output_file): Convert PineScript file
Strategy Development Guide

Strategy Development Guide

Introduction

This guide covers:

Basic strategy structure
Technical indicators
Entry/exit signals
Risk management
Advanced techniques

Basic Strategy Structure

Every strategy inherits from StrategyBase:

from algame.strategy import StrategyBase

class MyStrategy(StrategyBase):
    def __init__(self, parameters=None):
        super().__init__(parameters)
        self.parameters = parameters or {
            'sma_period': 20,
            'rsi_period': 14,
            'risk_per_trade': 0.02
        }

    def initialize(self):
        """Setup strategy components."""
        # Add technical indicators
        self.sma = self.add_indicator('SMA', self.data.Close,
                                    period=self.parameters['sma_period'])
        self.rsi = self.add_indicator('RSI', self.data.Close,
                                    period=self.parameters['rsi_period'])

    def next(self):
        """Generate trading signals."""
        if not self.position.is_open:
            if self.data.Close[-1] > self.sma[-1] and \
               self.rsi[-1] < 70:
                self.buy()
        else:
            if self.data.Close[-1] < self.sma[-1] or \
               self.rsi[-1] > 70:
                self.sell()

Using Technical Indicators

Built-in Indicators

# Moving Averages
self.sma = self.add_indicator('SMA', self.data.Close, period=20)
self.ema = self.add_indicator('EMA', self.data.Close, period=20)
self.wma = self.add_indicator('WMA', self.data.Close, period=20)

# Oscillators
self.rsi = self.add_indicator('RSI', self.data.Close, period=14)
self.macd = self.add_indicator('MACD', self.data.Close,
                              fast=12, slow=26, signal=9)
self.stoch = self.add_indicator('Stochastic', self.data.Close, period=14)

# Volatility
self.bb = self.add_indicator('Bollinger', self.data.Close,
                            period=20, std_dev=2)
self.atr = self.add_indicator('ATR', self.data, period=14)

# Volume
self.obv = self.add_indicator('OBV', self.data.Close, self.data.Volume)
self.vwap = self.add_indicator('VWAP', self.data)

Custom Indicators

from algame.indicators import IndicatorBase

class CustomIndicator(IndicatorBase):
    def __init__(self, period):
        self.period = period

    def calculate(self, data):
        # Your calculation logic
        return result

# Use in strategy
self.custom = self.add_indicator(CustomIndicator(20), self.data.Close)

Entry/Exit Signals

Signal Types

Price Action:

def next(self):
    # Breakout
    if self.data.Close[-1] > self.data.High[-20:].max():
        self.buy()

    # Support/Resistance
    if self.data.Close[-1] < self.data.Low[-20:].min():
        self.sell()

Indicator Crossovers:

def next(self):
    # MA Crossover
    if self.fast_ma[-1] > self.slow_ma[-1] and \
       self.fast_ma[-2] <= self.slow_ma[-2]:
        self.buy()

    # RSI Conditions
    if self.rsi[-1] < 30:
        self.buy()
    elif self.rsi[-1] > 70:
        self.sell()

Multiple Conditions:

def next(self):
    # Trend following with volume confirmation
    trend_up = self.data.Close[-1] > self.sma[-1]
    volume_high = self.data.Volume[-1] > self.data.Volume[-20:].mean()
    rsi_ok = self.rsi[-1] < 70

    if trend_up and volume_high and rsi_ok:
        self.buy()

Risk Management

Position Sizing

def calculate_position_size(self):
    """Calculate position size based on risk."""
    risk_amount = self.portfolio_value * self.parameters['risk_per_trade']
    stop_distance = self.atr[-1] * 2  # 2 ATR stop

    if stop_distance > 0:
        return risk_amount / stop_distance
    return 0

def next(self):
    if self.entry_signal():
        size = self.calculate_position_size()
        if size > 0:
            self.buy(size=size)

Stop Loss & Take Profit

def next(self):
    if not self.position.is_open:
        if self.entry_signal():
            entry_price = self.data.Close[-1]
            atr = self.atr[-1]

            self.buy(
                size=1.0,
                sl=entry_price - (atr * 2),  # 2 ATR stop
                tp=entry_price + (atr * 3)   # 3 ATR target
            )

Advanced Techniques

Multi-Timeframe Analysis

class MultiTimeframeStrategy(StrategyBase):
    def initialize(self):
        # Daily timeframe
        self.daily_sma = self.add_indicator('SMA', self.data['1d'].Close, 20)
        # Hourly timeframe
        self.hourly_rsi = self.add_indicator('RSI', self.data['1h'].Close, 14)

    def next(self):
        # Use both timeframes
        if self.data['1d'].Close[-1] > self.daily_sma[-1] and \
           self.hourly_rsi[-1] < 30:
            self.buy()

Portfolio Management

class PortfolioStrategy(StrategyBase):
    def initialize(self):
        # Setup indicators for each asset
        self.indicators = {}
        for symbol in self.data.keys():
            self.indicators[symbol] = {
                'sma': self.add_indicator('SMA', self.data[symbol].Close, 20),
                'rsi': self.add_indicator('RSI', self.data[symbol].Close, 14)
            }

    def next(self):
        # Analyze each asset
        for symbol, indicators in self.indicators.items():
            if self.entry_signal(symbol, indicators):
                # Calculate position size
                weight = self.calculate_weight(symbol)
                self.buy(symbol, size=weight)

Machine Learning Integration

from sklearn.ensemble import RandomForestClassifier

class MLStrategy(StrategyBase):
    def initialize(self):
        # Prepare features
        self.feature_windows = [5, 10, 20]
        self.features = self.prepare_features()

        # Train model
        self.model = RandomForestClassifier()
        self.model.fit(self.features, self.labels)

    def prepare_features(self):
        # Create technical features
        features = []
        for window in self.feature_windows:
            features.extend([
                self.data.Close.rolling(window).mean(),
                self.data.Close.rolling(window).std(),
                self.data.Volume.rolling(window).mean()
            ])
        return features

    def next(self):
        # Make prediction
        current_features = self.get_current_features()
        prediction = self.model.predict(current_features)

        if prediction == 1:
            self.buy()
        elif prediction == -1:
            self.sell()

Advanced Features

Advanced Features

Event-Driven Backtesting

Custom Risk Metrics

Creating Custom Risk Metrics

from algame.analysis import RiskMetric

class CustomDrawdownMetric(RiskMetric):
    def __init__(self, lookback=20):
        self.lookback = lookback

    def calculate(self, returns):
        """Calculate custom drawdown metric."""
        equity = (1 + returns).cumprod()
        rolling_max = equity.rolling(self.lookback).max()
        drawdowns = (equity - rolling_max) / rolling_max
        return drawdowns.min()

# Use in strategy
class MyStrategy(StrategyBase):
    def initialize(self):
        self.risk_metric = CustomDrawdownMetric(lookback=20)

    def next(self):
        risk_score = self.risk_metric.calculate(self.returns)
        if risk_score < -0.1:  # 10% drawdown threshold
            self.reduce_exposure()

Advanced Backtesting Features

Walk-Forward Analysis

from algame.core.engine import WalkForwardTest

# Define test parameters
wf_test = WalkForwardTest(
    strategy=MyStrategy,
    train_period='6M',
    test_period='2M',
    overlap=0.5
)

# Run walk-forward optimization
results = wf_test.run(
    data=market_data,
    parameter_space={
        'sma_period': range(10, 50, 5),
        'risk_per_trade': [0.01, 0.02, 0.03]
    }
)

# Analyze results
wf_test.plot_performance()
wf_test.parameter_stability()

Monte Carlo Analysis

from algame.analysis import MonteCarloSimulation

class MonteCarloStrategy(StrategyBase):
    def analyze_risk(self):
        # Setup simulation
        mc = MonteCarloSimulation(
            strategy=self,
            num_simulations=1000,
            variables={
                'slippage': ('normal', 0.001, 0.0002),
                'execution_delay': ('uniform', 0, 2)
            }
        )

        # Run simulation
        results = mc.run()

        # Analyze results
        confidence_intervals = mc.get_confidence_intervals()
        var_estimate = mc.calculate_var(confidence=0.95)

        return {
            'confidence': confidence_intervals,
            'var_95': var_estimate,
            'worst_case': results.min()
        }

Machine Learning Integration

Feature Engineering

from algame.ml import FeatureEngineering

class MLStrategy(StrategyBase):
    def create_features(self):
        engineer = FeatureEngineering(self.data)

        # Technical features
        engineer.add_ta_features([
            ('sma', [20, 50, 200]),
            ('rsi', [14]),
            ('bbands', [20, 2.0])
        ])

        # Price features
        engineer.add_price_features([
            'returns',
            'log_returns',
            'realized_volatility'
        ])

        # Volume features
        engineer.add_volume_features([
            'vwap',
            'volume_profile',
            'volume_delta'
        ])

        return engineer.get_features()

### Model Training & Prediction

```python
from algame.ml import ModelTrainer
from sklearn.ensemble import GradientBoostingClassifier

class PredictiveStrategy(StrategyBase):
    def initialize(self):
        # Prepare data
        X, y = self.prepare_training_data()

        # Train model
        self.trainer = ModelTrainer(
            model=GradientBoostingClassifier(),
            features=X,
            labels=y,
            cv_splits=5
        )

        self.model = self.trainer.train()

    def next(self):
        # Get current features
        features = self.get_current_features()

        # Make prediction
        prob = self.model.predict_proba(features)[0]

        # Trade based on prediction confidence
        if prob[1] > 0.7:  # 70% confidence for long
            self.buy()
        elif prob[0] > 0.7:  # 70% confidence for short
            self.sell()

Custom Data Integration

Real-Time Data Handlers

from algame.data import RealTimeHandler

class CustomDataHandler(RealTimeHandler):
    def __init__(self, symbols):
        super().__init__(symbols)
        self.subscribers = []

    def connect(self):
        """Connect to data source."""
        # Your connection logic
        pass

    def subscribe(self, callback):
        """Subscribe to updates."""
        self.subscribers.append(callback)

    def on_data(self, data):
        """Handle incoming data."""
        # Process data
        processed = self.process_data(data)

        # Notify subscribers
        for callback in self.subscribers:
            callback(processed)

# Use in live trading
handler = CustomDataHandler(['AAPL', 'GOOGL'])
strategy = MyStrategy(data_handler=handler)

Advanced GUI Components

Custom Indicators Panel

from algame.gui.components import IndicatorPanel

class CustomIndicatorPanel(IndicatorPanel):
    def __init__(self, master):
        super().__init__(master)
        self.create_controls()

    def create_controls(self):
        """Create indicator controls."""
        # Add parameters
        self.add_parameter('period', 14, (5, 50))
        self.add_parameter('threshold', 0.5, (0, 1))

        # Add plot options
        self.add_plot_option('color', 'blue')
        self.add_plot_option('style', 'line')

    def calculate(self):
        """Calculate indicator values."""
        # Your calculation logic
        pass

    def plot(self, ax):
        """Plot indicator."""
        # Your plotting logic
        pass

Strategy Analytics Dashboard

from algame.gui.components import AnalyticsDashboard

class StrategyDashboard(AnalyticsDashboard):
    def __init__(self, master):
        super().__init__(master)
        self.create_panels()

    def create_panels(self):
        """Create dashboard panels."""
        # Performance metrics
        self.add_metrics_panel()

        # Charts
        self.add_equity_chart()
        self.add_drawdown_chart()

        # Trade analysis
        self.add_trade_list()
        self.add_trade_statistics()

    def update(self, results):
        """Update dashboard with new results."""
        self.update_metrics(results.metrics)
        self.update_charts(results.equity_curve)
        self.update_trades(results.trades)

These advanced features make Algame a powerful platform for both research and trading. Use them to:

Implement sophisticated strategies
Integrate machine learning
Create custom analysis tools
Build professional-grade GUIs Events

from algame.core.engine import Event, EventType

class MyStrategy(StrategyBase):
    def initialize(self):
        # Register custom event
        self.register_event(
            EventType.CUSTOM,
            'volatility_spike',
            self.handle_volatility_spike
        )

    def next(self):
        # Check for volatility spike
        if self.data.High[-1] - self.data.Low[-1] > self.atr[-1] * 2:
            self.emit_event('volatility_spike', {
                'magnitude': self.data.High[-1] - self.data.Low[-1],
                'volume': self.data.Volume[-1]
            })

    def handle_volatility_spike(self, event):
        """Handle volatility spike event."""
        if event.data['volume'] > self.data.Volume[-20:].mean():
            self.close()  # Close positions on high volatility

Advanced Order Types

Conditional Orders

class MyStrategy(StrategyBase):
    def place_conditional_order(self):
        # OCO (One-Cancels-Other)
        self.buy(
            size=1.0,
            condition={
                'type': 'OCO',
                'orders': [
                    {'type': 'limit', 'price': self.data.Close[-1] * 0.98},
                    {'type': 'stop', 'price': self.data.Close[-1] * 1.02}
                ]
            }
        )

    def place_trailing_stop(self):
        # Trailing stop
        self.sell(
            size=self.position.size,
            condition={
                'type': 'trailing_stop',
                'distance': self.atr[-1] * 2
            }
        )

Portfolio Optimization

Risk Parity

from algame.analysis import RiskParity

class RiskParityStrategy(StrategyBase):
    def optimize_weights(self):
        # Calculate risk-parity weights
        optimizer = RiskParity(
            returns=self.get_returns(),
            risk_measure='conditional_var'
        )

        weights = optimizer.optimize()
        return weights

    def rebalance_portfolio(self):
        weights = self.optimize_weights()
        for asset, weight in weights.items():
            current_weight = self.get_position_weight(asset)
            if abs(current_weight - weight) > 0.05:  # 5% threshold
                self.rebalance_position(asset, weight)

Best Practices

Best Practices

Strategy Development

1. Code Structure

✅ Do:

class WellStructuredStrategy(StrategyBase):
    def __init__(self, parameters=None):
        super().__init__(parameters)
        self.validate_parameters()

    def initialize(self):
        # Group indicator initialization
        self.setup_indicators()
        # Group risk parameters
        self.setup_risk_management()

    def setup_indicators(self):
        """Initialize technical indicators."""
        self.sma = self.add_indicator('SMA', self.data.Close,
                                    self.parameters['sma_period'])
        self.rsi = self.add_indicator('RSI', self.data.Close,
                                    self.parameters['rsi_period'])

    def setup_risk_management(self):
        """Setup risk parameters."""
        self.max_risk = self.parameters['risk_per_trade']
        self.stop_atr = self.parameters['stop_atr']

    def next(self):
        # Clear decision flow
        if not self.position.is_open:
            if self.entry_signal():
                self.enter_position()
        else:
            if self.exit_signal():
                self.exit_position()

    def entry_signal(self):
        """Generate entry signals."""
        return (self.trend_signal() and
                self.momentum_signal() and
                self.risk_allows_entry())

    def exit_signal(self):
        """Generate exit signals."""
        return (self.stop_loss_hit() or
                self.take_profit_hit() or
                self.trend_reversal())

❌ Don't:

class PoorlyStructuredStrategy(StrategyBase):
    def next(self):
        # Hard to understand and maintain
        if (self.data.Close[-1] > self.data.Close[-20:].mean() and
            self.data.Volume[-1] > 1000000 and
            abs(self.data.Close[-1] - self.data.Close[-2]) < 0.02 and
            self.position.size == 0):
            self.buy(size=1.0)
        elif self.position.size > 0 and self.data.Close[-1] < self.data.Close[-20:].mean():
            self.sell()

2. Risk Management

✅ Do:

def calculate_position_size(self):
    """Calculate position size based on risk."""
    account_risk = self.portfolio_value * self.max_risk
    stop_distance = self.calculate_stop_distance()

    if stop_distance <= 0:
        return 0

    return account_risk / stop_distance

def calculate_stop_distance(self):
    """Calculate adaptive stop distance."""
    atr = self.atr[-1]
    return max(
        atr * self.stop_atr,  # ATR-based stop
        self.data.Close[-1] * 0.01  # Minimum 1% stop
    )

❌ Don't:

def next(self):
    # Fixed position size ignores risk
    self.buy(size=100)

    # Fixed stop loss ignores volatility
    self.set_stop_loss(self.data.Close[-1] * 0.95)

3. Data Handling

✅ Do:

def validate_data(self):
    """Validate input data."""
    required_columns = ['Open', 'High', 'Low', 'Close', 'Volume']

    # Check required columns
    if not all(col in self.data.columns for col in required_columns):
        raise ValueError(f"Missing required columns: {required_columns}")

    # Check for missing values
    if self.data.isnull().any().any():
        self.handle_missing_data()

def handle_missing_data(self):
    """Handle missing values."""
    # Forward fill prices
    self.data.loc[:, ['Open', 'High', 'Low', 'Close']] = \
        self.data[['Open', 'High', 'Low', 'Close']].ffill()

    # Fill volume with 0
    self.data['Volume'] = self.data['Volume'].fillna(0)

❌ Don't:

def next(self):
    # Dangerous direct indexing without checks
    price = self.data.Close[-1]
    if np.isnan(price):
        price = 0  # Bad handling of missing data

4. Performance Optimization

✅ Do:

class OptimizedStrategy(StrategyBase):
    def initialize(self):
        # Pre-calculate expensive computations
        self.setup_indicators()

        # Use numpy arrays for performance
        self._prices = self.data.Close.to_numpy()
        self._volumes = self.data.Volume.to_numpy()

    def next(self):
        # Use vectorized operations
        returns = np.diff(self._prices[-20:])
        volatility = returns.std()

        # Efficient boolean logic
        trend_up = self._prices[-1] > self.sma[-1]
        vol_high = self._volumes[-1] > self._volumes[-20:].mean()

        if trend_up and vol_high:
            self.buy()

❌ Don't:

def next(self):
    # Expensive calculations in loop
    for i in range(20):
        if self.data.Close[-i] > self.data.Close[-i-1]:
            # Process each point
            pass

5. Testing & Validation

✅ Do:

class RobustStrategy(StrategyBase):
    def validate_parameters(self):
        """Validate strategy parameters."""
        required = ['sma_period', 'rsi_period', 'risk_per_trade']

        # Check required parameters
        for param in required:
            if param not in self.parameters:
                raise ValueError(f"Missing required parameter: {param}")

        # Validate values
        if self.parameters['risk_per_trade'] > 0.02:
            raise ValueError("Risk per trade cannot exceed 2%")

    def validate_results(self, results):
        """Validate strategy results."""
        # Check for realistic performance
        if results.metrics['max_drawdown'] > 30:
            logger.warning("High drawdown detected")

        # Check for excessive trading
        trades_per_day = len(results.trades) / len(self.data)
        if trades_per_day > 5:
            logger.warning("High trading frequency detected")

❌ Don't:

def next(self):
    # No parameter validation
    size = self.parameters.get('size', 100)
    self.buy(size=size)  # Could be dangerous

    # No results validation
    if self.pnl > 1000000:
        logger.info("Great success!")  # Could be unrealistic

Common Pitfalls to Avoid

Lookahead Bias

# ❌ Wrong: Using future data
def next(self):
    future_high = self.data.High[len(self.data)].max()  # Lookahead!

# ✅ Correct: Only use available data
def next(self):
    current_high = self.data.High[:len(self.data)].max()

Survivorship Bias

# ❌ Wrong: Using current S&P 500 list historically
universe = sp500_current_symbols

# ✅ Correct: Use point-in-time data
universe = get_historical_sp500_composition(date)

🔧 Under Development

Features coming soon:

👥 Authors

Mrigesh Thakur - Lead Developer - GitHub
Dharuva Thakur - Core Developer - GitHub
Maanas Sood - Core Developer - GitHub

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

Made with ❤️ by traders who code

⬆ back to top

Name		Name	Last commit message	Last commit date
Latest commit History 67 Commits
.github/workflows		.github/workflows
assets		assets
docs		docs
src		src
.gitignore		.gitignore
.readthedocs.yaml		.readthedocs.yaml
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
pyproject.toml		pyproject.toml
setup.cfg		setup.cfg
setup.py		setup.py

License

Legend101Zz/Algame-MRIMADHA

Folders and files

Latest commit

History

Repository files navigation

🎮 Algame-MRIMADHA

🌟 Why Algame?

Our Solution? A Framework That:

🚀 Installation

Development Setup

Quick Start for Developers

Running the Package

Project Structure

Contributing

Common Development Tasks

Troubleshooting Development Setup

🏃‍♂️ Quick Start

Code Your First Strategy

Or Use the GUI

✨ Key Features

🎯 Multi-Everything Support

Multi-Asset Trading

Multi-Timeframe Analysis

🔌 Pluggable Architecture

Custom Data Sources

Custom Indicators

🎨 Strategy Building

Visual Builder

Template-Based Development

🔄 PineScript Converter

📊 Advanced Analysis

📘 Documentation

API Reference

Core APIs

Engine API

EngineManager

BacktestResult

Data API

DataManager

MarketData

Strategy API

StrategyBase

Analysis API

PerformanceMetrics

RiskAnalysis

GUI API

MainWindow

Tools API

PineScriptConverter

Strategy Development Guide

Introduction

Basic Strategy Structure

Using Technical Indicators

Built-in Indicators

Custom Indicators

Entry/Exit Signals

Signal Types

Risk Management

Position Sizing

Stop Loss & Take Profit

Advanced Techniques

Multi-Timeframe Analysis

Portfolio Management

Machine Learning Integration

Advanced Features

Event-Driven Backtesting

Custom Risk Metrics

Creating Custom Risk Metrics

Advanced Backtesting Features

Walk-Forward Analysis

Monte Carlo Analysis

Machine Learning Integration

Feature Engineering

Custom Data Integration

Real-Time Data Handlers

Advanced GUI Components

Custom Indicators Panel

Strategy Analytics Dashboard

Advanced Order Types

Packages