From c19ff46b5eb0a65f0da14ce4e471515ebce3de6e Mon Sep 17 00:00:00 2001 From: aguru-venkata-saisantosh-patnaik Date: Mon, 8 Jun 2026 03:46:20 +0530 Subject: [PATCH] Remove images from README, replace with tables and text --- README.md | 42 +++++++++++++++++++++++++++++++----------- 1 file changed, 31 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index ab450e2..4ec80c2 100644 --- a/README.md +++ b/README.md @@ -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 -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 -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. -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