🚀 One Year of Building My Python Trading Brain

For the last 12 months I’ve been crafting a Python system that watches the market live, crunches 100+ indicators across multiple timeframes, and helps me decide when to go Short or Long—with real, actionable entries sent straight to my Telegram.

What it does (in plain English):

‱ Live market feed + futures stats, funding, OI, order book microstructure.

‱ Multi-TF signal engine that blends momentum, trend, volatility, and pattern detectors.

‱ Over 100 indicators (RSI/MACD/ADX/Ichimoku/BB/Donchian/Alligator/SuperTrend/TTM-style squeeze, and more).

‱ Smart bias & context: aligns coin-level setups with overall market regime and session.

‱ Telegram alerts with confidence scoring so I can act fast.

‱ Continuous learning loop: after each batch of real trades, it analyzes outcomes, tunes the rules, and iterates—better entries, repeated.

Why I’m excited:

‱ It’s not a static “strategy.” It’s an evolving signal lab that rewards confluence and penalizes counter-trend noise.

‱ It focuses on risk first: clear SL/TP logic, R:R awareness, and session-aware behavior.

‱ It’s designed to avoid FOMO and wait for high-quality, aligned conditions.

I’ll keep sharing insights, equity curves, and lessons learned here. This is not financial advice, just my engineering journey—shipped in Python, battle-tested in the wild, improving every week. If you’re into data-driven trading and systematic iteration, follow along. đŸ§Șâš™ïžđŸ“ˆ

@IRONMIND_BR

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