Can we predict the heartbeat of Indian monetary policy one week ahead?
Ten years, 545 weeks, 117 features, two monetary-policy regimes, and one overnight rate — the WACMR — that quietly settles every bank's cash book. We combined NDAP RBI data with market prices and 75 curated policy events to build a forecasting model with 70.9% directional accuracy, explain it with SHAP, and let you interrogate it.
The WACMR is the thermometer of Indian monetary policy.
The Weighted Average Call Money Rate is the interest rate at which scheduled Indian banks lend to each other overnight, settled on the books of the Reserve Bank of India. It's the point where the RBI's policy rate, the banking system's liquidity state, and market expectations all have to agree.
If you want to know whether monetary policy is actually transmitting — whether the repo rate is biting — the WACMR is where you look. Forecasting it a week ahead is genuinely hard: the series lives inside a tight corridor, but with sharp, regime-dependent excursions around COVID, rate cycles, and liquidity operations.
This project frames the problem, pulls together open data from NDAP and elsewhere, engineers a feature set, clusters the dataset into market regimes, trains a walk-forward-validated XGBoost forecaster, opens the model with SHAP, and turns the whole thing into an interactive artefact you can play with.
What the data told us.
Mean |SHAP| concentrates on the repo rate, its one-week lag, and the WACMR–repo spread. Equity and forex features do not appear in the top 15.
PCA+K-Means on 90%-variance components splits the sample into a pre-COVID tightening regime and a post-COVID accommodation regime at a silhouette-optimal k=2.
Directional accuracy on one-week-ahead predictions using an expanding-window XGBoost with a 156-week minimum train set (RMSE 0.102, MAE 0.065).
Six stages, reproducibly.
8 RBI datasets + Yahoo Finance, aligned to a Friday weekly grid
Technical indicators, lags, spreads — 117 features
PCA → K-Means (k=2) with silhouette validation
XGBoost, expanding-window walk-forward
SHAP TreeExplainer per week + aggregate
75 curated policy events with manual sentiment
Where to go next.
Policy Counterfactual Simulator
Drag a slider for a repo-rate change. See the model's predicted WACMR, the 90% walk-forward CI, and per-feature attribution.
AI Research Agent
Chat with the dataset in natural language. The agent writes SQL, plots charts, explains SHAP, and runs counterfactuals.
Regimes
PCA projection coloured by regime. Regime fact sheets, transition timeline, and comparative statistics.
Forecast & SHAP
Actual vs predicted, walk-forward metrics, SHAP summary, and per-week waterfall explanations.
Dashboard
Interactive time series, correlation heatmap, distribution, and regime composition.
Data Explorer
Browse every week and every column. Filter by date, regime, or search columns.
News & NLP
75 curated RBI policy events with sentiment. Timeline overlay on WACMR and correlation stats.
Full Report
Long-form methodology, findings, and recommendations generated from the pipeline.