Pairs trading on Indian equities works best when two correlated names diverge from their historical relationship. alphabench's Quant agent can find candidate pairs, estimate dynamic hedge ratios, and backtest a Z-score-based mean-reversion strategy in one conversation.
1. Pick (or discover) a pair
You can either name the pair directly or ask the Researcher agent to find one:
"Find me 5 NSE pairs of large-cap banking stocks with high cointegration over the last 3 years."
For a known pair:
"Backtest a pairs strategy on HDFCBANK and ICICIBANK."
2. Configure the spread
The Quant agent computes the spread as A - β * B where β is the hedge ratio. By
default, β is estimated by rolling OLS regression over a 60-day window, so the hedge is
dynamic — it adapts as the relationship between the two stocks evolves.
"Use a 90-day rolling window for the hedge ratio estimate. Enter long when Z-score
< -2, short when Z > 2, exit when |Z| < 0.5."
3. Run the backtest
The agent calls backtest_pairs with:
- Pair: HDFCBANK / ICICIBANK
- Hedge window: 90 days
- Entry/exit thresholds: ±2 / ±0.5
- Position sizing: equal notional on each leg
You'll get the standard metrics (Sharpe, CAGR, max DD, win rate) plus pairs-specific diagnostics:
- Average holding period — pairs trades should mean-revert in days, not months
- Hedge ratio drift chart — flat is good, drifting is dangerous
- Z-score distribution — should look approximately normal
4. Sanity-check the cointegration
Before trusting any pairs result, ask:
"Run a Monte Carlo test with 1000 simulations and a walk-forward test on this pair."
The Quant agent calls monte_carlo_test and walk_forward_test. If walk-forward Sharpe
collapses below 0.5, the relationship was probably spurious or has decayed.
5. Common pitfalls
- Single-pair strategies fail: trade a basket of 5–10 pairs to diversify away pair-specific blow-ups.
- Hedge ratio instability: if β swings wildly across the rolling window, the relationship isn't stable enough for pairs trading.
- Capital intensity: each pair needs notional on both legs, so capital efficiency is roughly half of a directional strategy. Account for this in position sizing.
6. Next: deploy to paper trading
Once the strategy validates, deploy it to the paper trading engine through the UI for live tick-level testing before risking capital.