Documentation Index
Fetch the complete documentation index at: https://docs.novosky.app/llms.txt
Use this file to discover all available pages before exploring further.
What is NOVOSKY?
NOVOSKY is a live BTCUSD M15 trading system that combines a 4-model ML ensemble with a fully autonomous weekly optimization pipeline. It ingests real-time candles, scores every bar with three signal models, filters trades through circuit breakers, sizes positions dynamically via an equity-aware risk multiplier, and manages open positions with a dedicated hold/exit model. Every Sunday at 2 am UTC it retrains itself — SHAP analysis, Optuna hyperparameter search, fresh model training, config sweep, OOS validation — then either commits the improvement or rolls back automatically. Zero human input required after initial setup.For traders
Install, run the onboarding wizard, answer 6 questions about your risk tolerance, and let the bot trade 24/7. No coding required.
For developers
Full Python codebase. Sweep configs, retrain models, run backtests, push to Hugging Face Hub.
For ML researchers
4-model ensemble with 62 engineered features. LightGBM risk multiplier, dynamic SL/TP regression, SHAP-driven feature analysis, Optuna tuning loop.
Go binary distribution
Compiled binary with all models embedded. License-locked per MT5 account. Customers receive one executable — no Python, no model files, no HF Hub.
Live performance
Performance is updated after each weekly optimization run. The OOS window uses the final 30% of data as a true holdout — never seen by the sweep.
| Metric | Latest OOS result |
|---|---|
| Win rate | 71.7% |
| Profit factor | 5.46 |
| Max drawdown | 4.3% |
| Sharpe ratio | 59.54 |
| Return | +194.6% |
| Trades (37d OOS) | 184 |
| Score | 18.48 |
How it works
See How it works for a full walkthrough of each stage.Key capabilities
4-model ML ensemble
Signal model (RF + XGB + LGB majority vote), position model (HOLD/EXIT/ADD), dynamic SL/TP regression, and a risk multiplier that scales exposure with equity health.
Autonomous weekly retraining
13-phase pipeline runs every Sunday: data refresh → SHAP → Optuna → retrain (4 models) → sweep → OOS validation → auto-rollback if score drops.
5-profile risk system
From Steady Income (0.5% risk/trade) to Aggressive (4%). Every profile has a hard halt that calls
sys.exit(99) before margin call is even close.Broker-agnostic design
Broker-agnostic by design. Symbol, account type, pip value, digits, and server UTC offset are all auto-detected from the live MT5 API. Currently running on RoboForex BTCUSD at 1:500 leverage.
Equity-aware position sizing
Lot size scales with account equity. The SL/TP hard cap adjusts automatically so drawdown never exceeds your profile’s limit regardless of account size.
Model calibration pipeline
After every retrain,
calibrate_models() runs automatically. Inference priority: calibrated pkl → ONNX → raw pkl. Hugging Face Hub stores all revisions.Quick start
Run the onboarding wizard
.env, pulls the latest models from Hugging Face Hub, asks 6 risk questions, and runs the first optimization. Takes ~2.5 hours.