API Reference

import rlx
import pandas as pd

# Load market data
df = pd.read_csv("BTCUSDT_1h.csv")

# Create environment
env = rlx.RLEnvironment(
    initial_capital=100000.0,
    window_size=10
)
env.load_data(df)

# Training loop
obs, info = env.reset()
done = False

while not done:
    action = 1  # Example: Always go Long
    obs, reward, done, truncated, info = env.step(action)
    print(f"Reward: {reward:.2f}%, Portfolio: ${info['portfolio_value']:.2f}")

import rlx

# Define exit rules
rules = rlx.ExitRules(
    hold_bars=12,               # Max 12 bars in position
    max_drawdown_percent=3.0,   # Stop loss at 3% drawdown
    min_profit_percent=1.5,    # Take profit at 1.5%
)

# Create exit controller
exit_controller = rlx.ExitController(rules)

# Create environment with exit rules
env = rlx.RLEnvironment(
    initial_capital=100000.0,
    commission=0.001,            # 0.1% commission
    slippage=0.0,
    window_size=20,
    exit_controller=exit_controller
)
env.load_data(df)

import rlx
import json

# Run backtest
engine = rlx.TradingEngine(initial_capital=100000)
result = engine.execute_backtest(strategy, data)

# Convert result to JSON
result_json = json.dumps(result.__dict__, default=lambda o: o.__dict__)

# Create analyzer
analyzer = rlx.GraphAnalyzer(result_json)

# Find institutional patterns
patterns = analyzer.find_hedge_fund_patterns()
print(f"Found {len(patterns)} patterns")

# Generate report
report = analyzer.generate_pattern_report()
print(report)

# Explain specific pattern
if patterns:
    explanation = analyzer.explain_pattern(patterns[0].id)
    print(explanation)

# Export for visualization
graph_json = analyzer.export_graph()
with open("trade_graph.json", "w") as f:
    f.write(graph_json)