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Quick Reference - v3.1.0 Features

🎯 Strategy Selection Guide

When to Use Each Strategy Type

Strategy Type Best For Requirements
SMA_Crossover Trend following, beginners None
RSI_Reversion Range-bound markets None
BreakoutATR Volatile markets, breakouts None
LSTM_Strategy Time series prediction TensorFlow, 200+ samples
SVM_Strategy Classification-based trading scikit-learn, 100+ samples
Ensemble_ML Robust predictions scikit-learn, 100+ samples
Transformer Pattern recognition Transformers, 200+ samples
RL_Strategy Autonomous learning Gymnasium, Stable-Baselines3, 500+ samples
Multi_Timeframe Multi-timeframe analysis Data provider for multiple timeframes

🔧 Optimization Tools Quick Reference

Genetic Algorithm

from forexsmartbot.optimization import GeneticOptimizer

optimizer = GeneticOptimizer(
    param_bounds={'param': (min, max)},
    population_size=50,
    generations=30
)
best_params, fitness = optimizer.optimize(fitness_function)

Hyperparameter Optimization

from forexsmartbot.optimization import HyperparameterOptimizer

optimizer = HyperparameterOptimizer(
    param_space={'param': {'type': 'float', 'low': 0, 'high': 1}},
    n_trials=100
)
best_params, value = optimizer.optimize(objective_function)

Walk-Forward Analysis

from forexsmartbot.optimization import WalkForwardAnalyzer

analyzer = WalkForwardAnalyzer(
    train_period=252,
    test_period=63,
    step_size=21
)
results = analyzer.analyze(data, strategy_factory, optimize, params)

Monte Carlo Simulation

from forexsmartbot.optimization import MonteCarloSimulator

simulator = MonteCarloSimulator(n_simulations=1000, confidence_level=0.95)
results = simulator.simulate(returns, initial_balance=10000.0)

Parameter Sensitivity

from forexsmartbot.optimization import ParameterSensitivityAnalyzer

analyzer = ParameterSensitivityAnalyzer(n_points=10)
results = analyzer.analyze(strategy_factory, base_params, ranges, performance)

📊 Monitoring Quick Reference

Strategy Monitor

from forexsmartbot.monitoring import StrategyMonitor

monitor = StrategyMonitor()
monitor.register_strategy("MyStrategy")
monitor.record_signal("MyStrategy", execution_time=0.1)
health = monitor.get_health("MyStrategy")

Performance Tracker

from forexsmartbot.monitoring import PerformanceTracker

tracker = PerformanceTracker()
tracker.record_trade("MyStrategy", trade_data)
metrics = tracker.calculate_metrics("MyStrategy")

Health Checker

from forexsmartbot.monitoring import HealthChecker

checker = HealthChecker(monitor)
health = checker.check("MyStrategy")

🏗️ Strategy Builder Quick Reference

Create from Template

from forexsmartbot.builder import StrategyTemplate

builder = StrategyTemplate.get_template("SMA Crossover")

Build Custom

from forexsmartbot.builder import StrategyBuilder, CodeGenerator
from forexsmartbot.builder.strategy_builder import ComponentType

builder = StrategyBuilder()
indicator_id = builder.add_component(ComponentType.INDICATOR, "SMA", {"period": 20})
signal_id = builder.add_component(ComponentType.SIGNAL, "Signal", {})
builder.connect_components(indicator_id, signal_id)

generator = CodeGenerator(builder)
code = generator.generate_code()

🛒 Marketplace Quick Reference

Add Listing

from forexsmartbot.marketplace import StrategyMarketplace, StrategyListing

marketplace = StrategyMarketplace()
listing = StrategyListing(...)
marketplace.add_listing(listing)

Search

results = marketplace.search_listings(query="SMA", min_rating=4.0)

🚀 Common Patterns

Pattern 1: Optimize and Deploy

# 1. Optimize
optimizer = GeneticOptimizer(param_bounds)
best_params, _ = optimizer.optimize(fitness)

# 2. Create strategy
strategy = get_strategy('SMA_Crossover', **best_params)

# 3. Monitor
monitor = StrategyMonitor()
monitor.register_strategy("OptimizedStrategy")

Pattern 2: Multi-Strategy Ensemble

strategies = [
    get_strategy('SMA_Crossover'),
    get_strategy('RSI_Reversion'),
    get_strategy('Ensemble_ML_Strategy')
]

# Run all and combine signals

Pattern 3: Risk Assessment

# 1. Run backtest
results = backtest_service.run_backtest(...)

# 2. Monte Carlo simulation
returns = calculate_returns(results)
mc_results = monte_carlo.simulate(returns)

# 3. Check VaR/CVaR
print(f"VaR: {mc_results['var']:.4f}")

📝 Parameter Ranges (Common Strategies)

SMA_Crossover

  • fast_period: 10-30
  • slow_period: 40-80
  • atr_period: 10-20

RSI_Reversion

  • rsi_period: 10-20
  • oversold_level: 20-40
  • overbought_level: 60-80

BreakoutATR

  • lookback_period: 15-30
  • atr_period: 10-20
  • atr_multiplier: 1.0-3.0

⚡ Performance Tips

  1. ML Strategies: Use during development, simpler strategies for production
  2. Optimization: Run overnight or on separate machines
  3. Monitoring: Enable only for production strategies
  4. Multi-Timeframe: Cache data to avoid repeated fetches
  5. Parallel Processing: Use for multiple backtests

🔍 Troubleshooting

Issue Solution
Import error for ML strategy Install dependencies: pip install tensorflow torch
Strategy not found Check list_strategies() for available strategies
Optimization too slow Reduce population_size or n_trials
Memory issues Use smaller datasets or simpler strategies
Training fails Ensure 200+ samples for ML strategies

📚 File Locations

  • Strategies: forexsmartbot/strategies/
  • Optimization: forexsmartbot/optimization/
  • Monitoring: forexsmartbot/monitoring/
  • Builder: forexsmartbot/builder/
  • Marketplace: forexsmartbot/marketplace/
  • Examples: examples/
  • Configs: config/
  • Scripts: scripts/

🎓 Learning Path

  1. Start: Use existing strategies (SMA_Crossover, RSI_Reversion)
  2. Optimize: Learn parameter optimization (GeneticOptimizer)
  3. Analyze: Understand sensitivity (ParameterSensitivityAnalyzer)
  4. Validate: Use walk-forward (WalkForwardAnalyzer)
  5. Assess: Run Monte Carlo (MonteCarloSimulator)
  6. Advanced: Try ML strategies (LSTM_Strategy, Ensemble_ML_Strategy)
  7. Build: Create custom strategies (StrategyBuilder)
  8. Monitor: Track performance (StrategyMonitor, PerformanceTracker)

Quick Links: