tradingbot

A research framework for 5-minute gold / forex machine-learning trading.

It covers the full pipeline: downloading and cleaning candle data, engineering technical features, training several model architectures, turning predictions into risk-managed signals, and backtesting them with walk-forward optimization.

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Project structure

tradingbot/
├── tradingbot/
│   ├── config.py                 # central config (reads secrets from env)
│   ├── data/
│   │   ├── oanda_connector.py     # OANDA v20 historical candle download
│   │   ├── loader.py              # CSV loading + time-gap analysis
│   │   ├── processor.py           # GoldDataProcessor: cleaning, gap fill, outliers
│   │   └── features.py            # GoldFeatureEngineer + GoldFeaturePipeline
│   ├── models/
│   │   ├── tree_ensemble.py       # TreeEnsemblePredictor (stacked trees + HMM)
│   │   ├── neural_ensemble.py     # NeuralEnsemblePredictor (Transformer/LSTM + Bayesian NN)
│   │   └── model_manager.py       # GoldModelManager (Optuna, Boruta, SHAP, 5-class)
│   ├── signals/
│   │   └── generator.py           # GoldSignalGenerator (signals + stops/targets)
│   ├── backtest/
│   │   ├── walk_forward.py        # WalkForwardOptimizer
│   │   └── metrics.py             # compute_performance_metrics
│   └── viz/
│       └── visualize.py           # visualize_signals + plotting helpers
├── notebooks/                     # exploratory research notebook
├── requirements.txt
└── .gitignore

Model approaches

The framework ships three model architectures that can be used independently:

1. **TreeEnsemblePredictor** (models/tree_ensemble.py) — a binary up/down classifier that stacks XGBoost, LightGBM, CatBoost, RandomForest and ExtraTrees, with two meta-models and an HMM market-regime filter.
2. **NeuralEnsemblePredictor** (models/neural_ensemble.py) — stacks gradient-boosted trees with a Transformer + BiLSTM network and a Bayesian network (Monte-Carlo dropout) for uncertainty-aware filtering.
3. **GoldModelManager pipeline** (models/model_manager.py + signals/generator.py + backtest/walk_forward.py) — the most complete path: a 5-class classifier (strong/weak up, sideways, weak/strong down) with Boruta/RFE/PCA feature selection, Optuna tuning, SHAP analysis, volatility regime-aware ensembling, and walk-forward backtesting.

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Installation

pip install -r requirements.txt

TA-Lib also requires the underlying C library:

• macOS: brew install ta-lib
• Ubuntu/Debian: install ta-lib (package or build from source)
• Windows: install a prebuilt TA-Lib wheel / binaries

For live data downloads, set your OANDA token (never commit it):

export OANDA_ACCESS_TOKEN="your-token-here"

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Usage

1. Download data (optional — or bring your own OHLCV CSV)

from tradingbot.data.oanda_connector import download

# Saves e.g. EURUSD_M5_<start>_to_<end>.csv in the current directory.
download(instrument="EUR_USD", timeframe="M5", lookback_days=1825)

A CSV is expected with a timestamp column plus open, high, low, close, volume.

2. Quick look at the data

from tradingbot.data.loader import load_ohlcv_csv, summarize, analyze_time_gaps

data = load_ohlcv_csv("EURUSD_M5_20200309_to_20250308.csv")
summarize(data)
analyze_time_gaps(data)

3a. Tree-ensemble approach

from tradingbot.models.tree_ensemble import run_model

predictor, metrics, results, returns = run_model(
    data_path="XAUUSD_M5_20200222_to_20250220.csv",
    forecast_bars=24,            # 2 hours of 5-minute bars
    confidence_threshold=0.67,
)

Visualize the result:

from tradingbot.viz.visualize import visualize_signals

visualize_signals(data, results, returns, metrics, save_path="signals.png")

3b. Neural-ensemble approach

import pandas as pd
from tradingbot.models.neural_ensemble import NeuralEnsemblePredictor

data = pd.read_csv("XAUUSD_H1_...csv", parse_dates=["timestamp"], index_col="timestamp")
train, test = data[:int(len(data) * 0.9)], data[int(len(data) * 0.9):]

predictor = NeuralEnsemblePredictor(forecast_period=12, confidence_threshold=0.6)
predictor.fit(train)
signals = predictor.predict(test)

3c. Full pipeline + walk-forward backtest

from tradingbot.data.features import GoldFeaturePipeline
from tradingbot.backtest.walk_forward import WalkForwardOptimizer

# Engineer features and the 5-class target.
processed = GoldFeaturePipeline(forecast_horizon=6).process(
    "XAUUSD_M5_20200222_to_20250220.csv"
)

# Walk-forward optimization (windows are in days).
wf = WalkForwardOptimizer(
    train_window=35,
    step_size=5,
    test_window=5,
    feature_selection_interval=4,
)
results = wf.optimize(processed)

Generate signals from a trained model:

from tradingbot.models.model_manager import GoldModelManager
from tradingbot.signals.generator import GoldSignalGenerator

manager = GoldModelManager(model_path="models/step_1", feature_selection_method="importance")
manager.load_models()

gen = GoldSignalGenerator(confidence_threshold=0.7, risk_reward_min=1.5, model_manager=manager)
signals = gen.generate_signals(processed.iloc[-2880:])  # last ~10 days
active = signals[signals["signal"] != 0]

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Disclaimer

This is research / educational code for exploring ML trading ideas on historical data. It is not financial advice and makes no guarantee of profitability. Markets carry real risk of loss, validate any strategy thoroughly before risking capital.