Connors RSI Dual-Strategy Trading Bot

Problem

This skill targets automated OKX perpetual trading, where manual monitoring, inconsistent signal execution, and fragmented risk control are common pain points. It puts two strategy families into one execution framework: Strategy A trend resonance for trending markets, using 1H/4H/1D alignment, pullback entries, and trailing stops; Strategy B Connors RSI for range markets, using RSI(2) extreme readings and ADX(14) filters to avoid fading strong trends.

How It Works

The implementation is organized around scanning, risk control, order execution, and backtesting:

• bot.py is the Strategy A entry point for market scanning, signal scoring, multi-filter validation, and automated execution.
• rsi2_bot.py is the Strategy B entry point for mean-reversion signals such as long entries on RSI(2) < 10 and short entries on RSI(2) > 90.
• risk_manager.py handles daily loss limits, exposure caps, position counts, and cooldown rules after consecutive losses.
• trade_executor.py manages entries, stop-loss, take-profit, and trailing-stop logic.
• backtest_rsi2.py supports historical validation of the RSI-based strategy.

A typical workflow is to configure the API key and risk parameters in scripts/config.ini, choose a strategy to start, then verify connectivity and monitor status. The documentation stresses that the API key must have perpetual-trading permission and recommends running in demo mode first.

Boundaries and Caveats

This is best treated as an engineering scaffold for a trading bot, not a guaranteed return system. The two strategies maintain independent risk budgets, so simultaneous runs still require manual oversight of total exposure. High leverage amplifies slippage, latency, and tail-risk exposure. Before live use, verify API permissions, spend limits, stop logic, and whether the backtest coverage matches the intended instruments.