OPEN-SOURCE SCRIPT
MLP - BTC Breakout Probability [Deep Learning] [Open Source]
I trained a single Multilayer Perceptron on 13 years of Bitcoin price history and open-sourced the result. Not because it's perfect, but because the idea is worth sharing.
The concept is simple.
Most breakout strategies are rule-based. Fixed levels, static conditions. This one is different, instead of predicting direction, the model learned the distribution of Bitcoin's daily price moves. You pick a threshold, it gives you the probability. Same model, any level.
How to use it
Pick a percentage threshold , by doing that you're asking the model to evaluate. When price breaks that level and the model is showing meaningful confidence, a label is shown on the chart.. Daily only. BTC only.
Under the hood
A lightweight Multilayer Perceptron (MLP) trained on ~4,700 daily candles of raw OHLC data from May 2009 to May 2022. The architecture is two hidden layers (16→8), ReLU activations throughout, and a sigmoid output that squashes the result into a clean 0–1 probability score. ReLU keeps the internal representations sparse and non-linear, sigmoid makes the output as a probability.
What makes this interesting is that the model didn't just learn a raw number, it learned the underlying distribution of Bitcoin's daily price moves. That's what allows a single model to answer probability questions across different thresholds rather than being hardcoded to one fixed level.
The output isn't a prediction, it's a calibrated belief about where price is likely to go, derived from 13 years of market structure.
Honest limitations
Fat tails eat this model alive. The features are correlated and the model has no concept of liquidity. It underestimates the extremes.
Daily timeframe only. Bitcoin only. Long only.
This was built as a personal project, mostly for fun and to serve as a working example of how ML concepts can be applied to market data.
Disclaimer
This indicator is provided for educational and informational purposes only. It does not constitute financial advice, trading recommendations, or a guarantee of future results. Past performance does not predict future returns. You alone are responsible for your trading decisions. Always test thoroughly in a simulated environment before trading with real capital.
The concept is simple.
Most breakout strategies are rule-based. Fixed levels, static conditions. This one is different, instead of predicting direction, the model learned the distribution of Bitcoin's daily price moves. You pick a threshold, it gives you the probability. Same model, any level.
How to use it
Pick a percentage threshold , by doing that you're asking the model to evaluate. When price breaks that level and the model is showing meaningful confidence, a label is shown on the chart.. Daily only. BTC only.
Under the hood
A lightweight Multilayer Perceptron (MLP) trained on ~4,700 daily candles of raw OHLC data from May 2009 to May 2022. The architecture is two hidden layers (16→8), ReLU activations throughout, and a sigmoid output that squashes the result into a clean 0–1 probability score. ReLU keeps the internal representations sparse and non-linear, sigmoid makes the output as a probability.
What makes this interesting is that the model didn't just learn a raw number, it learned the underlying distribution of Bitcoin's daily price moves. That's what allows a single model to answer probability questions across different thresholds rather than being hardcoded to one fixed level.
The output isn't a prediction, it's a calibrated belief about where price is likely to go, derived from 13 years of market structure.
Honest limitations
Fat tails eat this model alive. The features are correlated and the model has no concept of liquidity. It underestimates the extremes.
Daily timeframe only. Bitcoin only. Long only.
This was built as a personal project, mostly for fun and to serve as a working example of how ML concepts can be applied to market data.
Disclaimer
This indicator is provided for educational and informational purposes only. It does not constitute financial advice, trading recommendations, or a guarantee of future results. Past performance does not predict future returns. You alone are responsible for your trading decisions. Always test thoroughly in a simulated environment before trading with real capital.
开源脚本
秉承TradingView的精神，该脚本的作者将其开源，以便交易者可以查看和验证其功能。向作者致敬！您可以免费使用该脚本，但请记住，重新发布代码须遵守我们的网站规则。
免责声明
这些信息和出版物并非旨在提供，也不构成TradingView提供或认可的任何形式的财务、投资、交易或其他类型的建议或推荐。请阅读使用条款了解更多信息。
开源脚本
秉承TradingView的精神，该脚本的作者将其开源，以便交易者可以查看和验证其功能。向作者致敬！您可以免费使用该脚本，但请记住，重新发布代码须遵守我们的网站规则。
免责声明
这些信息和出版物并非旨在提供，也不构成TradingView提供或认可的任何形式的财务、投资、交易或其他类型的建议或推荐。请阅读使用条款了解更多信息。