Scripts reference

Entry points

These are the top-level entry points (not in scripts/):

Script	Purpose
`trading.py`	Start the live trading bot
`backtest.py`	Run a backtest
`scripts/retrain.py`	Train all 4 models (signal, position, SLTP, risk)

# trading.py
python trading.py           # live
python trading.py --dry     # dry run — no real orders

# backtest.py
python backtest.py --balance 500 --no-swap --leverage 500 --spread 14.59 --oos-only --no-chart

# scripts/retrain.py
python scripts/retrain.py                   # all 4 models
python scripts/retrain.py --shap --refresh  # with SHAP + fresh data
python scripts/retrain.py --trials 50       # Optuna tuning first

scripts/

onboarding.py

First-time setup wizard. Run once.

python scripts/onboarding.py --balance 500

Prompts for .env credentials
Pulls latest models from HF Hub
Runs risk questionnaire (6 questions → profile 1–5)
Runs first weekly optimize

weekly_optimize.py

Autonomous 13-phase weekly pipeline.

python scripts/weekly_optimize.py                           # full + questionnaire
python scripts/weekly_optimize.py --profile balanced        # skip questionnaire
python scripts/weekly_optimize.py --profile 3               # by profile number
python scripts/weekly_optimize.py --skip-retrain            # sweep only (~45 min)
python scripts/weekly_optimize.py --from-phase 5            # resume after crash
python scripts/weekly_optimize.py --dry-run                 # show plan, don't run

Profiles: steady_income (1), conservative (2), balanced (3), growth (4), aggressive (5).

retrain.py

Standalone retrain — always trains all 4 models together to prevent feature/risk mismatches.

python scripts/retrain.py               # retrain all 4 models
python scripts/retrain.py --shap        # include SHAP analysis after training
python scripts/retrain.py --refresh     # fetch fresh OHLCV from MT5 API first
python scripts/retrain.py --trials 50   # run Optuna tuning before retraining

sweep.py

Unified config parameter sweep. Replaces the old sweep_signal.py and sweep_pos_model.py.

# Signal parameter sweeps (--target signal)
python scripts/sweep.py --target signal --mode confidence
python scripts/sweep.py --target signal --mode sltp
python scripts/sweep.py --target signal --mode risk
python scripts/sweep.py --target signal --mode full        # all signal modes
python scripts/sweep.py --target signal --mode ml_sltp     # confidence_sl/tp_adjust
python scripts/sweep.py --target signal --mode kelly       # Kelly lot-sizing toggle
python scripts/sweep.py --target signal --mode custom --param confidence_threshold 0.55 0.60 0.65

# Position model sweep (--target pos)
python scripts/sweep.py --target pos
python scripts/sweep.py --target pos --full    # include partial_close + trailing sweeps

# Both sweeps sequentially (--target both)
python scripts/sweep.py --target both --no-chart

Broker params (spread, leverage) are loaded from the live MT5 API on each run; override with --spread / --leverage.

optimize_loop.py

Iterative optimization loop — runs SHAP → tune → retrain → OOS in a loop, committing only when score improves by ≥ 2%.

python scripts/optimize_loop.py                     # 1 iteration
python scripts/optimize_loop.py --iterations 3      # 3 iterations
python scripts/optimize_loop.py --trials 50         # 50 Optuna trials per iteration
python scripts/optimize_loop.py --analyze-only      # SHAP + backtest, no retrain
python scripts/optimize_loop.py --retrain-only      # skip SHAP, just tune+retrain

Feature dropping (--drop-threshold) is disabled by default (0.0). Near-zero SHAP at train time does not mean a feature is useless — news and session features score near-zero during training but carry live signal at inference time. Do not enable dropping without careful review.

_PKL_FILES must include .onnx files alongside their .pkl counterparts. If ONNX files are omitted from the snapshot, rollback restores the pkl scaler but leaves a stale ONNX — the predictor then uses mismatched models silently.

config_sync.py

Shared config authority helpers (imported by other scripts — not a standalone CLI). Validates config.json and ml_config.json on startup and exposes sync_generated_configs().

from scripts.config_sync import sync_generated_configs, assert_generated_configs_in_sync

notify.py

Send a manual Telegram notification.

python scripts/notify.py "deploy complete — bot restarted"
python scripts/notify.py --level error "MT5 connection failed"

ml/hf_hub.py

Hugging Face Hub model management.

python ml/hf_hub.py --pull                # download latest
python ml/hf_hub.py --push                # upload after retrain
python ml/hf_hub.py --push --tag phase-16 # push with version tag
python ml/hf_hub.py --list                # show available revisions

Go binary scripts

These scripts are used when building the compiled Go binary. They are not needed for the Python bot.

scripts/export_scalers.py

Exports fitted scikit-learn StandardScaler objects from .pkl to JSON for Go inference.

python scripts/export_scalers.py

Outputs (in models/):

ensemble_scaler.json
position_scaler.json
risk_scaler.json
sltp_scaler.json

Run after every retrain before packing the Go binary.

scripts/export_onnx_regression.py

Converts LightGBM regression models (risk and SLTP) from their native .txt format to ONNX using onnxmltools.

python scripts/export_onnx_regression.py

Outputs (in models/):

risk_lgb.onnx — 7 features
sltp_sl_lgb.onnx — market features + signal_direction
sltp_tp_lgb.onnx — same

Includes a smoke test with onnxruntime after each export.

Ensemble and position ONNX files are exported automatically by the Python trainer. Only risk and SLTP need this script.

scripts/pack_embedded.sh

Copies model files and configs into internal/embedded/ for Go’s //go:embed. Must be run before every go build.

bash scripts/pack_embedded.sh            # dev build (placeholder license)
bash scripts/pack_embedded.sh --license  # production (copies license.json from repo root)

What it copies:

models/*.onnx → internal/embedded/models/
models/*_scaler.json, models/*_metadata.json → internal/embedded/models/
ml_config.json, strategy_params.json → internal/embedded/
license.json (if --license) → internal/embedded/license.json

cmd/licgen — license generator

A separate binary the owner builds to generate signed license.json files. Never ship licgen to customers.

# Build licgen with the production key
go build \
  -ldflags="-X github.com/mokatific/novosky/internal/license.masterKey=SECRET" \
  -o licgen ./cmd/licgen/

# Create accounts.json (private — never ship this)
cat > accounts.json <<'EOF'
{
  "123456":  {"start": "2026-05-13", "end": "2026-08-13"},
  "1234567": {"start": "2026-05-13", "end": "2027-05-13"}
}
EOF

# Generate signed license.json
./licgen -key "NOVA-2026-XYZ" -f accounts.json -o license.json

Options:

Flag	Description
`-key`	Human-readable product key (required)
`-f`	Path to accounts JSON file (required)
`-o`	Output path (default: `license.json`)

Go binary — full build sequence

After make train or python scripts/retrain.py:

# 1. Export models to ONNX and JSON
python scripts/export_scalers.py
python scripts/export_onnx_regression.py

# 2. (Production only) generate license
./licgen -key "NOVA-2026-XYZ" -f accounts.json -o license.json

# 3. Pack all assets into internal/embedded/
bash scripts/pack_embedded.sh             # dev
bash scripts/pack_embedded.sh --license   # production

# 4. Build
go build -o novosky_go ./cmd/novosky/     # dev
go build \
  -ldflags="-X github.com/mokatific/novosky/internal/license.masterKey=SECRET" \
  -o novosky_go ./cmd/novosky/            # production

# 5. Verify
./novosky_go --dry

Codebase

Contributing & roadmap

Scripts reference

Entry points

scripts/

onboarding.py

weekly_optimize.py

retrain.py

sweep.py

optimize_loop.py

config_sync.py

notify.py

ml/hf_hub.py

Go binary scripts

scripts/export_scalers.py

scripts/export_onnx_regression.py

scripts/pack_embedded.sh

cmd/licgen — license generator

Go binary — full build sequence

​Entry points

​scripts/

​onboarding.py

​weekly_optimize.py

​retrain.py

​sweep.py

​optimize_loop.py

​config_sync.py

​notify.py

​ml/hf_hub.py

​Go binary scripts

​scripts/export_scalers.py

​scripts/export_onnx_regression.py

​scripts/pack_embedded.sh

​cmd/licgen — license generator

​Go binary — full build sequence

Entry points

scripts/

onboarding.py

weekly_optimize.py

retrain.py

sweep.py

optimize_loop.py

config_sync.py

notify.py

ml/hf_hub.py

Go binary scripts

scripts/export_scalers.py

scripts/export_onnx_regression.py

scripts/pack_embedded.sh

cmd/licgen — license generator

Go binary — full build sequence