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 Source	Description	Frequency
FX OHLCV	Low-volatility major currency pair	Hourly
U.S. Treasury spread	10Y–2Y yield spread	Daily (joined hourly)
WTI crude oil	Daily close prices	Daily (joined hourly)
Gold	Daily close prices	Daily (joined hourly)
Volume	Oil trading volume as market activity proxy	Daily

• 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

ACF/PACF analysis confirmed serial autocorrelation in key engineered features — validating their predictive relevance.

Feature Group	Autocorrelation Finding	Implication
Session-tagged returns	Significant lag-1 autocorrelation during London+NY overlap	Session labelling is predictive
ADX (2-period)	Strong partial autocorrelation at lags 1–3	Short-window ADX captures momentum persistence
CCI ratio (2/20)	Decaying ACF with slow taper	CCI ratio is a trend-following signal, not noise
Keltner width	Significant at lag-1, drops at lag-2	ATR-based volatility is a 1-bar leading indicator

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

---

Model Training

XGBoost Multi-class Classifier

Critical design choices:

1. Purged Time-Series CV — 5-fold with 5-bar gap between train and validation to prevent leakage; chronological ordering maintained strictly
2. Randomised Hyperparameter Search — 30 trials over learning rate (η), max depth, subsampling, regularisation
3. Early stopping — 150 rounds to select optimal iteration
4. 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. Max drawdown held at 7.3% even through the highest-volatility period.

Performance Breakdown

Period	Characteristic	Strategy Behaviour	Annualised Vol
2019	Low volatility baseline	Steady accumulation	~8%
2020	COVID spike	Navigated March 2020 drawdown, recovered	~18%
2021–2022	Rate uncertainty, USD strengthening	Macro features gave edge on USD pairs	~22% (peak)
2023–2024	Normalisation, range markets	Session-based features exploited intraday patterns	~12%
2024–2025	Continued trend	Consistent compounding to $187K	~10%

---

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:

1. Data loading (data.csv, gold.csv, oil.csv)
2. Feature engineering (all 3 categories)
3. Target labelling and purged CV split
4. XGBoost training with hyperparameter search
5. 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

---

Contact

agurusantosh@gmail.com