CME Bitcoin & Micro Bitcoin Bot Suite | 4 Python Strategies

Study four approaches to CME Bitcoin futures in Python

Compare a Micro Bitcoin short-breakdown model, two Micro Bitcoin long models, and a full-size Bitcoin trend strategy in one source-code bundle. Built for developers and quantitative-trading researchers who want to inspect signal rules, adapt risk controls, and run their own tests, not buy a promise of returns.

Price: $47 for the suite. Format: Python source-code files with documented strategy logic. Skill level: Best for readers comfortable with Python and futures market data.

Four strategies in the bundle

• Micro Bitcoin Short v2: A short-side breakdown strategy using trend, volatility, and ATR-based risk inputs.
• MBT Statistical Mean-Reversion Long: A Z-score and RSI-based approach to researching oversold conditions.
• MBT Momentum & Trend Engine: A moving-average and RSI trend model with trailing-stop logic.
• BTC Full-Size Trend Runner: A long-side trend model with staged profit-taking logic for full-size Bitcoin futures.

What you can explore

Read the indicator and signal calculations, compare how each strategy sizes and manages positions, and use the documented integration points as a starting place for your own data-feed and execution setup. The listing identifies these four source files: bot_g2m_mbt_short_portable.py, bar_mbt_long_20260914_194807_gen2_portable.py, bar_mbt_long_20260918_013013_gen2_portable.py, and bar_btc_long_20260916_212911_portable.py.

Before you buy

• Integration work is required: Data feeds, broker connections, order routing, and live execution are not presented here as turnkey services. Inspect the actual files and dependencies before attempting to run them.
• Results are limited: The listed MBT historical test uses just two trades on 4-hour OHLCV data. A 100% win rate or 0% drawdown in that sample is not evidence of durable performance.
• BTC figures are estimates: The advertised BTC metrics are medians across 45 analyzed configurations, not verified results from this exact engine. No future return is implied.
• Contract codes require attention: The examples reference dated MBTM6, MBTU6, and BTCU6 contracts. Review contract selection, rollover, data quality, trading costs, slippage, and risk limits in your own tests.

Technical starting point

Python 3.10+ and suitable OHLCV futures data. The product description specifies placeholder broker/data interfaces, so plan to implement and validate integration for your environment. Start with reproducible paper tests before considering any live use.

For educational and research purposes only. This is not financial or investment advice. Futures and cryptocurrency trading involve substantial risk of loss. Historical, hypothetical, and peer-derived estimates do not predict live outcomes.