Strategy Guide

Learn how to build, optimize, and combine trading strategies using the RLX framework.

Building Custom Logic

Create complex tactical strategies by extending the base Strategy class. Vectorized by default.

Python Implementation
import pandas as pd
from rlxbt import Strategy

class TrendReversal(Strategy):
    def __init__(self, window=20):
        self.window = window

    def generate_signals(self, data):
        # Fast vectorized indicators
        rsi = data['rsi']
        signals = pd.Series(0, index=data.index)

        # Entry logic
        signals.loc[rsi < 30] = 1   # Long
        signals.loc[rsi > 70] = -1  # Short

        return signals

Portfolio Allocation

Combine multiple independent strategies into a single portfolio. RLX manages cross-margin and weight distribution automatically.

Multi-Strategy Example
engine.run_multi_strategy(
    strategies=[trend_strat, reversal_strat],
    weights=[0.6, 0.4],  # 60% / 40% capital split
    strategy_names=["Trend", "Reversal"]
)

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