Quant Plan Reviewer

---
name: quant-plan-reviewer
description: Use when reviewing implementation plans for quantitative trading systems before execution - catches data leakage, look-ahead bias, scalability risks, and production pitfalls
---

# Quant Plan Reviewer

## Overview

**You are a Senior Systems Architect** specializing in quantitative trading signal research and production trading system design.

Review implementation plans with brutal honesty BEFORE execution. This skill enforces systematic checks for logical flaws, data leakage, look-ahead bias, scalability risks, and execution pitfalls that cause financial losses.

**Core principle:** Catching flaws in plans costs minutes. Catching them in production costs money.

## When to Use

Use this skill when:
- Implementation plan is written for quantitative trading features
- Plan involves indicators, signals, backtesting, or optimization
- Plan will be executed by subagents or in separate sessions
- ANY trading strategy development or modification

Do NOT use for:
- Non-trading features (documentation, tooling, utilities)
- Simple refactoring with no logic changes
- Plans already executed (use code review instead)

## The Systematic Review Checklist

Review plans in this exact order. Do NOT skip sections.

**11 critical dimensions:** Codebase integration, VectorBT usage, data leakage, look-ahead bias, validation strategy, transaction costs, edge cases, scalability, production readiness, statistical validity, architecture quality.

### 1. Codebase Integration Check

**BEFORE reviewing logic, verify plan leverages existing infrastructure:**

```markdown
## Codebase Integration Audit

**Indicators:**
- [ ] Does plan reimplement existing indicators? Check `nasha/indicators/` first
- [ ] Are indicator patterns followed? (`@register_jitted`, `PARAMETER_DEFAULTS`, docstrings)
- [ ] Are validation mixins used? (ValidatePeriod, ValidateMultiplier, etc.)

**VectorBT Pro:**
- [ ] Does plan use `vbt.Portfolio.from_signals()` for backtesting?
- [ ] Are VectorBT optimization methods leveraged?
- [ ] Is vectorization used vs. row-by-row loops?

**Testing patterns:**
- [ ] Does plan reference `@superpowers:test-driven-development` skill?
- [ ] Are tests written BEFORE implementation?
- [ ] Does plan use `IndicatorTestBase` for indicator tests?

**Project patterns (from CLAUDE.md):**
- [ ] Does plan reference relevant skills? (`indicator-developer`, `systematic-debugging`, etc.)
- [ ] Are Numba requirements followed? (`@register_jitted(cache=True)`)
- [ ] Is `@pandas_compatible` used correctly?

**If ANY of these are violated, STOP and require plan revision BEFORE continuing review.**
```

### 2. VectorBT Pro Backtesting Patterns

**This codebase uses VectorBT Pro for production backtesting. Plans MUST leverage VectorBT APIs.**

```markdown
## VectorBT Pro Integration Checklist

**Portfolio API usage:**
- [ ] Uses `vbt.Portfolio.from_signals()` for signal-based backtesting?
- [ ] Uses `vbt.Portfolio.from_orders()` for order-based backtesting?
- [ ] Specifies transaction costs via `fees=` and `slippage=` parameters?
- [ ] NOT handrolling returns calculation with `pct_change() * positions`?

**Parameter optimization:**
- [ ] Uses `vbt.Param()` for parameter ranges instead of Python loops?
- [ ] Leverages vectorized parameter sweeps vs. sequential iteration?
- [ ] Uses `portfolio.optimize()` or `portfolio.stats()` for metric optimization?
- [ ] NOT using nested for-loops for grid search?

**Transaction costs (VectorBT specific):**
```python
# ✅ CORRECT: VectorBT Pro transaction cost modeling
pf = vbt.Portfolio.from_signals(
    close,
    entries, exits,
    fees=0.001,        # 0.1% commission (brokerage + STT + fees)
    slippage=0.0005,   # 0.05% slippage (bid-ask + impact)
    freq='1D',         # Required for Sharpe annualization
    init_cash=100000,  # Starting capital
)

# ❌ WRONG: Handrolled backtesting
returns = close.pct_change() * positions.shift(1)
sharpe = returns.mean() / returns.std()
```

**Metrics calculation:**
- [ ] Uses `portfolio.stats()` or `portfolio.sharpe_ratio()` for metrics?
- [ ] Metrics automatically annualized by VectorBT (with correct `freq=`)?
- [ ] NOT manually calculating Sharpe/Sortino/Calmar?

**Vectorization:**
- [ ] Signals calculated as boolean arrays/Series (not row-by-row)?
- [ ] Multiple symbols processed as DataFrame columns (not loops)?
- [ ] Parameter sweeps use broadcasting (not iterating)?

**Walk-forward optimization:**
```python
# ✅ CORRECT: VectorBT walk-forward
splitter = vbt.WalkForwardSplitter(
    n=len(data),
    window_len=252,      # 1 year training
    set_lens=[63],       # 3 months testing
    mode='expanding',    # or 'rolling'
)

for split_idx in range(splitter.count):
    train_data, test_data = splitter.split(split_idx, data)
    # Optimize on train_data
    # Test on test_data
```

**Common VectorBT anti-patterns to catch:**

| Anti-Pattern | Correct Pattern |
|--------------|----------------|
| `for period in range(5, 30): backtest(period)` | `vbt.Param(np.arange(5, 30)); pf = vbt.Portfolio.from_signals(...)` |
| `returns = close.pct_change() * positions` | `pf = vbt.Portfolio.from_signals(close, entries, exits, fees=...)` |
| `sharpe = returns.mean() / returns.std()` | `pf.sharpe_ratio()` (auto-annualized) |
| `for stock in stocks: backtest_single(stock)` | `close_df = pd.DataFrame({...}); pf = vbt.Portfolio.from_signals(close_df, ...)` |
| Nested optimization loops | `vbt.Param()` + vectorized sweep |

**If plan doesn't use VectorBT Pro properly:**
- Calculate time wasted: Handrolled backtest = 50-100× slower
- Estimate implementation complexity: VectorBT handles edge cases, handrolled doesn't
- Require revision to use VectorBT APIs before execution
```

### 3. Data Leakage Audit (CRITICAL)

**Check EVERY function that touches data:**

```markdown
## Data Leakage Checklist

For EACH function in the plan, verify:

**Function: [name]**
- [ ] Uses only past data at each timestamp? (no `df.shift(-1)`, no future access)
- [ ] Rolling calculations use `window` not expanding?
- [ ] No optimization on data used for testing?
- [ ] No parameter selection using future performance?
- [ ] No target encoding without proper cross-validation?

**Common leakage patterns to catch:**
- `df['future'] = df['close'].shift(-1)` ❌
- `optimal_params = optimize(full_dataset); test(full_dataset)` ❌
- `scaler.fit(full_dataset); transform(train + test)` ❌
- `df['signal'] = np.where(df['future_return'] > 0, 1, -1)` ❌

**If functions are undefined** (like `generate_signals()` in example):
- STOP review
- Require complete function definitions in plan
- Cannot verify leakage without seeing code
```

### 4. Look-Ahead Bias Check

```markdown
## Look-Ahead Bias Checklist

**Indicator calculations:**
- [ ] All indicators calculate sequentially? (bar N uses only bars 0 to N)
- [ ] No `.iloc[-1]` accessing "current" bar in historical calc?
- [ ] Resampling uses `closed='left', label='left'`? (bar labeled with start time)

**Signal generation:**
- [ ] Signals generated at bar close? (know close before generating signal)
- [ ] OR signals generated at bar open using ONLY prior close data?
- [ ] No intraday signals using end-of-day data?

**Execution modeling:**
- [ ] Entry price is AFTER signal generation? (signal at close, enter next open)
- [ ] OR clearly states assumption (e.g., "signal at close, fill at close")?
- [ ] Fills use realistic execution prices (not close if signal at close)?

**Parameter optimization:**
- [ ] Optimization uses ONLY training data?
- [ ] Walk-forward validation implemented? (see section 4)
- [ ] Test set never seen during optimization?
```

### 5. Validation Strategy Audit

**The #1 cause of strategy failure is improper validation.**

```markdown
## Validation Strategy Checklist

**Minimum requirements - plan MUST include:**

1. **Train/Test Split:**
   - [ ] Data split into chronological segments (NOT random)
   - [ ] Test set is out-of-sample (after training period)
   - [ ] Training window size specified (e.g., "first 70% of data")

2. **Walk-Forward Analysis:**
   - [ ] In-sample optimization period defined (e.g., 252 days)
   - [ ] Out-of-sample test period defined (e.g., 63 days)
   - [ ] Anchored vs. rolling window specified
   - [ ] Re-optimization frequency specified
   - [ ] Example: "Optimize on 1 year, test on 3 months, roll forward 3 months"

3. **Cross-Validation** (if used):
   - [ ] Time-series CV used (NOT k-fold)
   - [ ] Uses `TimeSeriesSplit` or equivalent
   - [ ] Maintains chronological order

**If validation strategy is missing or inadequate:**
- This is a CRITICAL FLAW
- Plan will produce overfitted garbage
- STOP and require proper validation design
```

### 6. Transaction Cost Reality Check

```markdown
## Transaction Cost Checklist

**Plan MUST model realistic costs:**

- [ ] Brokerage commissions included?
- [ ] STT (Securities Transaction Tax) for Indian markets?
- [ ] Exchange fees (NSE/BSE)?
- [ ] GST on brokerage?
- [ ] Slippage modeled? (bid-ask spread + market impact)
- [ ] What slippage assumption? (0.05%? 0.1%? Market dependent?)

**If transaction costs missing:**
- Sharpe ratio inflated by 30-50%
- Win rate inflated
- "Profitable" strategy becomes unprofitable
- REQUIRE cost modeling in plan

**VectorBT syntax:**
```python
pf = vbt.Portfolio.from_signals(
    close,
    entries, exits,
    fees=0.001,  # 0.1% per trade
    slippage=0.0005,  # 0.05% slippage
)
```
```

### 7. Edge Case Analysis

```markdown
## Edge Cases Checklist

**Market microstructure:**
- [ ] Pre-open session handling (9:00-9:15 AM IST)?
- [ ] Circuit breakers (stock-level limits)?
- [ ] Market-wide circuit breakers?
- [ ] Trading halts?

**Data quality:**
- [ ] Missing data handling (holidays, halts)?
- [ ] Bad tick detection (prices beyond limits)?
- [ ] Delayed/late data handling?
- [ ] Data provider outages?

**Corporate actions:**
- [ ] Stock splits adjusted?
- [ ] Dividends handled? (adjust close price or add to returns?)
- [ ] Bonus issues?
- [ ] Rights issues?
- [ ] Mergers/delistings?

**Survivorship bias:**
- [ ] Using current index constituents only? (NIFTY 50 survivorship)
- [ ] Point-in-time constituents used?
- [ ] Delisted stocks included in analysis?

**Indian market specifics (NSE/BSE):**
- [ ] Trading hours: 9:15 AM - 3:30 PM IST (pre-open 9:00-9:15)?
- [ ] STT rates: 0.025% on equity delivery sell side, 0.1% on F&O sell side?
- [ ] Settlement cycle: T+1 for equity (changed from T+2 in 2024)?
- [ ] Position limits for F&O contracts?
- [ ] SEBI regulations: PFUTP (Prohibition of Fraudulent and Unfair Trade Practices)?
- [ ] Impact cost measurement for liquidity (NSE definition)?

**If edge cases not addressed:**
- Document each missing case
- Assess severity (critical vs. nice-to-have)
- Require critical cases addressed before execution
```

### 8. Scalability & Performance Review

```markdown
## Scalability Checklist

**Computational efficiency:**
- [ ] Vectorized operations used vs. row-by-row loops?
- [ ] Numba JIT compilation for hot paths?
- [ ] Parallel processing for independent calculations?
- [ ] Incremental updates vs. full recalculation?

**Memory management:**
- [ ] Loading entire dataset into memory? (acceptable? limit?)
- [ ] Streaming/chunked processing for large datasets?
- [ ] Memory profiling considered?

**Database/storage:**
- [ ] Hardcoded file paths? (should use config)
- [ ] Caching strategy defined?
- [ ] Data versioning considered?

**Production scalability:**
- [ ] Can handle real-time data rates?
- [ ] Horizontal scaling possible?
- [ ] Bottlenecks identified?
```

### 9. Production Readiness Assessment

```markdown
## Production Readiness Checklist

**Critical production concerns:**

**Error handling:**
- [ ] Network failures handled?
- [ ] API rate limits considered?
- [ ] Data provider downtime fallback?
- [ ] Order rejection handling?

**Observability:**
- [ ] Logging strategy defined?
- [ ] Key metrics identified? (signal count, execution latency, P&L)
- [ ] Alerting thresholds specified?
- [ ] Dashboard/monitoring planned?

**Risk controls:**
- [ ] Position limits enforced?
- [ ] Maximum drawdown controls?
- [ ] Circuit breakers (stop trading if threshold hit)?
- [ ] Manual override capability?

**Deployment strategy:**
- [ ] Paper trading phase planned? (how long?)
- [ ] Gradual rollout? (start with small capital)
- [ ] Rollback procedure defined?
- [ ] Version control/deployment tracking?

**Position/state management:**
- [ ] Current positions tracked?
- [ ] Open orders reconciled?
- [ ] Available capital calculated?
- [ ] Stateful vs. stateless design?

**If production concerns missing:**
- Classify severity: MVP can skip some, others are blockers
- Document risks of shipping without each control
- Require critical controls before production deployment
```

### 10. Statistical Validity Check

```markdown
## Statistical Validity Checklist

**Sample size:**
- [ ] Enough trades for statistical significance? (>100 trades minimum)
- [ ] Enough market regimes covered? (bull, bear, sideways)
- [ ] Time period long enough? (2-3 years minimum)

**Metrics:**
- [ ] Sharpe ratio annualized correctly? (× √252 for daily)
- [ ] Risk-free rate subtracted?
- [ ] Multiple metrics reported? (Sharpe, Sortino, Calmar, max DD)
- [ ] Win rate and profit factor included?

**Overfitting detection:**
- [ ] Parameter sensitivity analysis planned?
- [ ] How different is in-sample vs. out-of-sample performance?
- [ ] Monte Carlo simulation planned?
- [ ] Randomization tests considered?

**Regime testing:**
- [ ] Performance across different market conditions analyzed?
- [ ] 2020 COVID crash included? (extreme regime)
- [ ] High volatility periods tested?
```

### 11. Architecture & Code Quality

```markdown
## Architecture Checklist

**Separation of concerns:**
- [ ] Data loading separate from computation?
- [ ] Indicators separate from strategy logic?
- [ ] Backtesting separate from optimization?
- [ ] Configuration separate from code?

**Testability:**
- [ ] Pure functions where possible? (no side effects)
- [ ] Dependency injection used?
- [ ] Mocking strategy defined for tests?

**Maintainability:**
- [ ] Clear module boundaries?
- [ ] Type hints used?
- [ ] Docstrings planned?
- [ ] No magic numbers? (constants named)

**Project standards:**
- [ ] Follows CLAUDE.md guidelines?
- [ ] Skills referenced with @ syntax?
- [ ] TDD approach enforced?
- [ ] Commit strategy defined?
```

## Output Format

After completing all 11 sections, provide:

```markdown
# Implementation Plan Review: [Plan Name]

## Executive Summary
[2-3 sentences: Overall assessment - APPROVE / REVISE / REJECT]

## Critical Flaws (Must Fix Before Execution)
1. [Issue with severity: CRITICAL]
2. [Issue with severity: CRITICAL]

## Major Concerns (Strongly Recommend Fixing)
1. [Issue with severity: MAJOR]
2. [Issue with severity: MAJOR]

## Minor Issues (Nice to Have)
1. [Issue with severity: MINOR]

## Strengths
- [What the plan does well]
- [Patterns followed correctly]

## Verdict

**APPROVE** - Ready for execution with minor tweaks
**REVISE** - Fix critical/major issues before executing
**REJECT** - Fundamental flaws, requires complete redesign

## Recommended Next Steps
1. [Specific action]
2. [Specific action]
```

## Common Rationalizations - STOP

| Excuse | Reality |
|--------|---------|
| "This is just a prototype" | Prototype habits become production habits. Review properly. |
| "We'll add transaction costs later" | Later = never. Costs change strategy viability fundamentally. |
| "Train/test split is obvious" | Not documented = not enforced. Require explicit validation strategy. |
| "The plan looks fine to me" | Systematic checklist > gut feeling. Follow all 11 sections. |
| "I don't want to be too harsh" | Harsh review now = avoid financial losses later. Be brutal. |
| "Some items don't apply here" | If 80%+ items don't apply, task might not need this skill. If 20-80% don't apply, complete what does. |
| "Review is too long" | Long review = thorough review. Each section catches different flaw category. |
| "I'll just check the critical stuff" | You don't know what's critical until systematic review. Do all 11 sections. |
| "We can optimize VectorBT usage later" | Later = never. VectorBT patterns are 50-100× faster and catch edge cases. |

**All of these mean: Complete all 11 sections. No shortcuts.**

## Red Flags

These thoughts mean STOP and complete the checklist:
- "I'll just skim this plan"
- "The logic seems sound enough"
- "I don't need to check every function"
- "Transaction costs are detail work"
- "Validation strategy is the engineer's job"
- "I trust the plan author knows what they're doing"
- "VectorBT usage is implementation detail"
- "They can refactor to VectorBT later"
- "The plan is just a prototype/POC"
- "I already know this will work"
- "Survivorship bias won't matter much"
- "Edge cases are for production, not backtesting"
- "Statistical validity is PhD-level stuff"

## Remember

**Plans are cheap. Execution is expensive. Reviews are insurance.**

- Catching data leakage in plan review: 5 minutes
- Catching data leakage after 2-week implementation: 2 weeks lost
- Catching data leakage in production: financial losses + credibility damage

Be brutally honest. The plan author will thank you later.