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@@ -35,17 +35,31 @@ An end-to-end quantitative research pipeline — from raw hourly FX data to a li
**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
| 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 |
The muted raw volatility made vanilla momentum strategies ineffective — requiring session-aware, macro-augmented feature engineering.
- **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
<img src="images/acf_pacf.png" width="700" alt="ACF/PACF Analysis of Key Features"/>
### ACF/PACF Analysis
Serial autocorrelation analysis confirmed predictive relevance of key engineered features:
| 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
@@ -68,8 +82,6 @@ The muted raw volatility made vanilla momentum strategies ineffective — requir
| 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
@@ -100,13 +112,21 @@ model = XGBClassifier(
## Backtesting Results
<img src="images/equity_curve.png" width="700" alt="Equity Curve 2019–2025"/>
### Portfolio Growth (2019–2025)
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).
Portfolio grows from $100,000 to ~$187,000 (6 years) with steady compounding and no single catastrophic drawdown event across 3 major macro regimes.
<img src="images/rolling_volatility.png" width="700" alt="Rolling Annualised Volatility"/>
| Period | Market Regime | Portfolio Behaviour | Annualised Vol |
|--------|--------------|--------------------|-|
| 2019 | Low volatility baseline | Steady accumulation | ~8% |
| 2020 | COVID volatility 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% |
Rolling 126-bar annualised volatility peaked during the 2022–2023 rate hike cycle (~22%) but remained well-managed throughout via the adaptive position framework.
### Rolling Annualised Volatility
Rolling 126-bar annualised volatility peaked during the 2022–2023 rate hike cycle (~22%) but remained well-managed throughout via the adaptive position framework. The strategy's max drawdown stayed at 7.3% even through the highest-volatility period.
### Performance Breakdown