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RSI STRATEGY DESIGN

RSI Pair Trading Strategy in Python for IBKR: Multi-Timeframe Signals and Basket Logic

How a multi-timeframe RSI pair-trader can be translated into a Python IBKR application with current RSI triggers, pullback confirmation, recovery logic and basket P&L control.

IBKR RSI Pair Trading Strategy interface preview
Reviewed September 18, 2026 | Educational case study | No executable, account data, credentials or private source code included

What This Page Covers

The goal of this RSI pair-trader design is to make the strategy rules objective enough for software: which timeframe triggers, when Trade 1 opens, when Trade 2 opens, what counts as recovery and when the basket is finished.

M5 to D1 RSI
Current candle trigger
Pullback entry
Recovery leg
Basket P&L
Paper validation

Technical Breakdown

Signal model

The app watches RSI values across enabled timeframes from M5 to D1. Current-candle RSI can trigger an initial overbought or oversold entry, while previous-closed and current RSI values can be used for the second pullback entry.

Basket state

The strategy tracks Trade 1 and Trade 2 separately even though Interactive Brokers may net the actual broker position. Internal order references and fill accounting allow the app to calculate each basket leg.

Recovery trigger

Instead of hard-coding MT5 lot math, the Python version scales the recovery loss threshold from configured quantity blocks. This lets the same concept work for shares, forex quantity or other IBKR instruments.

Exit behavior

Trade 1 can close at its own target, while Trade 2 and the combined basket can close when the configured combined P&L objective is reached. Daily close rules can flatten positions near the chosen market close time.

Validation approach

The safe path is paper testing first, then very small live quantity, with logs reviewed for RSI trigger, contract resolution, fill price, commission and session behavior.

Sanitized Application Previews

These images are public UI previews based on the Tkinter application structure. They intentionally avoid real account IDs, live balances, broker credentials, downloadable executables and proprietary source code.

Dashboard preview
Dashboard preview
Connection preview
Connection preview
Strategy preview
Strategy preview
Architecture preview
Architecture preview

Important Risk and Security Notes

  • This is software-development content, not investment advice or a profit guarantee.
  • No IBKR username, password, 2FA code, account number, token, private executable or full source code is published here.
  • Any live IBKR automation should be tested first in paper mode and reviewed against the current Interactive Brokers API behavior, account permissions and market-data subscriptions.

Related IBKR Python Guides

IBKR RSI Pair Trader Python App A technical case-study style guide to a Python/Tkinter Interactive Brokers RSI pair trader: TWS connection, multi-timeframe RSI, basket logic, risk controls, news/session filters and safe paper-first deployment. IBKR Python Trading Bot Architecture How to design an Interactive Brokers Python trading bot with a clean TWS API bridge, market data state machine, contract resolution, order tagging and safe execution controls. IBKR Tkinter Trading Dashboard A practical guide to building a client-ready Tkinter trading dashboard for Interactive Brokers with connection tabs, strategy inputs, filters, dashboard state, logs and emergency controls. IBKR Python Bot Risk Controls The practical risk controls a Python Interactive Brokers bot should include before live use: simulation mode, spread filters, session guards, news blackout, daily loss limit and emergency stop. IBKR Paper Trading Bot Setup A practical setup checklist for testing Python trading apps with Interactive Brokers paper accounts, TWS/Gateway ports, market data permissions, simulation mode and log review. IBKR Multi-Asset Python Trading App How a custom IBKR Python trading app should handle stocks, ETFs, forex pairs and spot metals with correct contract mapping, data type selection and session behavior.

FAQ

Is RSI alone enough for a profitable strategy?

Not by itself. RSI is a signal component. Execution cost, spread, market regime, session timing, risk limits and validation matter heavily.

Can the RSI periods and thresholds be changed?

Yes. A client-ready app should expose period and threshold inputs instead of hard-coding them.

Can this be converted from an MT5 EA?

A logic port is possible, but broker execution and position accounting must be redesigned for IBKR because it is not the same runtime as MetaTrader.

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