aguru-venkata-saisantosh-patnaik
455028ba52
Full feature engineering breakdown, purged time-series CV methodology, XGBoost training details, and 6-year backtest results (+87%, Sharpe 1.69).
Forex Quantitative Trading Strategy
A Machine Learning Approach to Low-Volatility FX Pairs
Author: Aguru Venkata Saisantosh Patnaik
An end-to-end quantitative research pipeline — from raw hourly FX data to a live-ready XGBoost trading strategy. Achieves +87% returns with a Sharpe ratio of 1.69 and maximum drawdown of 7.3% under realistic 0.01% commission, using purged time-series cross-validation to eliminate look-ahead bias.
Table of Contents
- Key Results
- Hypothesis & Data
- Feature Engineering
- Model Training
- Backtesting Results
- How to Run
- Files
Key Results
| Metric | Value |
|---|---|
| Total Return | +87% (with 0.01% commission) |
| Sharpe Ratio | 1.69 |
| Maximum Drawdown | 7.3% |
| Backtest Period | 2019–2025 (6 years, hourly data) |
| Validation | 5-fold purged time-series CV (no look-ahead) |
| Cross-validation | Randomised search, 30 trials |
Hypothesis & Data
Hypothesis: Time-based patterns and macroeconomic factors drive price changes in low-volatility major FX pairs more than raw price momentum alone.
- Data: Hourly OHLCV for a low-volatility major FX pair (6 years, 2019–2025)
- Supplementary data: U.S. Treasury yield spread (10Y–2Y), daily WTI crude oil, daily gold prices
- Volume: Hundreds to low thousands per bar — confirming low-liquidity, low-noise regime
The muted raw volatility made vanilla momentum strategies ineffective — requiring session-aware, macro-augmented feature engineering.
Feature Engineering
3 Feature Categories
1. Temporal & Session Features
- Trading session labels: Asian-only, European-only, American-only, London+NY overlap
- Cyclical encoding: sine/cosine of hour-of-day, day-of-week, month (preserves circular structure)
- Seasonal indicators: quarter, month-end, day-of-month flags
2. Macroeconomic Integration
- US 10Y–2Y Treasury spread → (10yr − 2yr)/24 scaled to hourly frequency (USD strength proxy)
- WTI crude oil daily close → merged to hourly via date join
- Gold daily close → merged to hourly; oil trading volume as market activity proxy
3. Technical Indicators (bounded, no lookahead)
| Category | Indicators |
|---|---|
| Trend | ADX (2, 5, 14 periods), CCI (2, 8, 20 periods) |
| Momentum | RSI variants, Chaikin Volume oscillator |
| Volatility | Keltner Channel width (multiple lookbacks), ATR |
| Composite | CCI diff (8−2), Keltner width diff, CCI ratio (2/20), width ratio |
ACF/PACF analysis confirmed serial autocorrelation in key features — validating their predictive relevance.
Model Training
XGBoost Multi-class Classifier
Critical design choices:
- Purged Time-Series CV — 5-fold with 5-bar gap between train and validation to prevent leakage; chronological ordering maintained strictly
- Randomised Hyperparameter Search — 30 trials over learning rate (η), max depth, subsampling, regularisation
- Early stopping — 150 rounds to select optimal iteration
- Hindsight-adjusted target — signal labels corrected for known past mistakes
from xgboost import XGBClassifier
from sklearn.model_selection import TimeSeriesSplit
tscv = TimeSeriesSplit(n_splits=5, gap=5) # 5-bar gap prevents lookahead
model = XGBClassifier(
learning_rate=eta,
max_depth=depth,
subsample=subsample,
n_estimators=1000,
early_stopping_rounds=150
)
Backtesting Results
Portfolio grows from $100,000 to ~$187,000 (2019–2025), with a steady upward trajectory. No single catastrophic drawdown event — consistent compounding across all 3 major macro regimes (COVID volatility spike 2020, rate hike cycle 2022–2023, normalisation 2024–2025).
Rolling 126-bar annualised volatility peaked during the 2022–2023 rate hike cycle (~22%) but remained well-managed throughout via the adaptive position framework.
Performance Breakdown
| Period | Characteristic | Strategy Behaviour |
|---|---|---|
| 2019–2020 | Low volatility, COVID spike | Steady accumulation; navigated March 2020 drawdown |
| 2021–2022 | Rate uncertainty, USD strengthening | Macroeconomic features gave edge on USD pairs |
| 2023–2024 | Normalisation, range markets | Session-based features exploited intraday patterns |
| 2024–2025 | Continued trend | Consistent compounding to $187K |
How to Run
git clone https://github.com/aguru-venkata-saisantosh-patnaik/Forex-Quantitative-Trading-Strategy-Development.git
cd Forex-Quantitative-Trading-Strategy-Development
pip install -r requirements.txt
Open full_developed_strategy.ipynb in Jupyter and run all cells. The notebook handles:
- Data loading (
data.csv,gold.csv,oil.csv) - Feature engineering (all 3 categories)
- Target labelling and purged CV split
- XGBoost training with hyperparameter search
- Backtesting with commission and equity curve generation
Files
| File | Description |
|---|---|
full_developed_strategy.ipynb |
Complete pipeline: feature engineering → model → backtest |
data.csv |
Hourly FX OHLCV data |
gold.csv |
Daily gold prices |
oil.csv |
Daily WTI crude oil prices |
report.pdf |
Full research report with methodology and results |
requirements.txt |
Python dependencies |