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SurgeSoftwareSolutions/intraday-btc-model-11yr
intraday-btc-model-11yr is a machine learning model from SurgeSoftwareSolutions. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
A Python-based research and backtesting system for leveraged intraday Bitcoin trading with automated profit siphoning and multi-layer capital protection.
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Updated Nov 27, 2025
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From the Hugging Face model README
A Python-based research and backtesting system for leveraged intraday Bitcoin trading with automated profit siphoning and multi-layer capital protection.
We verified three distinct strategies catering to different trading styles. Choose the one that fits your goal:
| Strategy Style | Timeframe | Return | Trades/Day | Drawdown | Best For... |
|---|---|---|---|---|---|
| 🥇 Balanced Growth | 1-Hour | +86.88% | 0.6 | -23% | Best Overall. High return with low risk. |
| 🚀 Long-Term Wealth | Daily | +1,041% | 0.01 | -50% | Investors. Set and forget for years. |
| ⚡ Active Trading | 15-Min | +23.88% | 1.7 | -40% | Traders. Frequent action, higher risk. |
Note: Returns verified on historical data (2023-2025 for intraday, 2014-2025 for daily).
intraday_btc_model/
│
├── data/
│ ├── btc_1h_binance.csv # 1-Hour Verified Data
│ ├── btc_15min_binance.csv # 15-Min Verified Data
│ └── btc_daily_2014_2025.csv # 11-Year Historical Data
│
├── core/
│ ├── data_loader.py # CSV loading and validation
│ ├── features.py # Technical indicator calculation
│ ├── signal_model.py # Signal generation
│ ├── position_sizing.py # Position sizing with leverage
│ ├── risk_management.py # Risk controls and profit siphon
│ └── backtester.py # Main backtesting engine
│
├── reports/ # Generated results
│
├── config.py # Configuration parameters
├── run_backtest.py # Main entry point
├── verify_best_strategy.py # Run the 1-Hour Strategy Verification
├── verify_15m_strategy.py # Run the 15-Min Active Strategy Verification
├── verify_long_term.py # Run the 11-Year Daily Strategy Verification
└── README.md # This file
pip install -r requirements.txt
To see the performance of the 1-Hour optimal strategy:
python intraday_btc_model/verify_best_strategy.py
To see the performance of the 15-Minute active trading strategy:
python intraday_btc_model/verify_15m_strategy.py
To see how an aggressive daily strategy performs over 11 years:
python intraday_btc_model/verify_long_term.py
To push this model and data to your Hugging Face account:
huggingface-cli login
python push_to_hub.py
reports/equity_curve.csv)Contains time series of:
e_trading: Trading equity (active capital)v_vault: Vault equity (protected profits)total: Total equity (E_t + V_t)reports/trades.csv)Individual trade records with:
Current strategy (v1): Moving Average Crossover
After closing a winning trade:
E_t > E_base:
V_t += (E_t - E_base)E_t = E_baseThis protects profits from being risked in future trades.
At the start of each day, record E_day_start.
During the day:
E_t ≤ E_day_start × (1 - D_max):
This prevents catastrophic single-day losses.
This project is for educational and research purposes. Use at your own risk.
Disclaimer: Trading cryptocurrencies involves substantial risk of loss. This software is provided "as is" without warranty of any kind. The authors are not responsible for any losses incurred through the use of this software.