Quantitative Research & Development (QRD) Skill

Required Information:

1. Research Objective: What hypothesis or edge are we testing?
2. Asset Universe: Which instruments/markets? (equities, crypto, FX, futures, multi-asset)
3. Strategy Type: Alpha generation, risk management, execution, portfolio construction?
4. Time Horizon: Holding period and rebalancing frequency?
5. Data Availability: What data sources exist? What needs acquisition?
6. Constraints: Regulatory, capital, liquidity, technology limitations?
7. Success Criteria: What metrics define success? Sharpe, drawdown, turnover bounds?
8. Existing Infrastructure: Current tech stack, backtesting frameworks, production systems?

Optional Clarifications (ask if ambiguous):

• Benchmark comparisons required?
• Transaction cost assumptions?
• Risk budget or factor exposure limits?
• Live trading considerations?

Phase 2: Document Generation

Generate tasks/qrd-[research-name].md following the template structure below.

Document Template

# QRD: [Research Name]

**Version:** 1.0
**Author:** [Author]
**Date:** [Date]
**Status:** Draft | In Review | Approved | In Progress | Complete

## Executive Summary

[2-3 sentences: What we're building, why it matters, expected outcome]

---

## 1. Research Hypothesis

### 1.1 Core Hypothesis
[Clear, falsifiable statement of the alpha source or research question]

### 1.2 Economic Rationale
[Why should this work? Behavioral, structural, or informational edge?]

### 1.3 Null Hypothesis
[What would disprove this? Specific conditions for rejection]

### 1.4 Prior Research
[Relevant academic papers, internal research, or market observations]

---

## 2. Universe & Scope

### 2.1 Asset Universe
- **Instruments:** [e.g., S&P 500 constituents, top 100 crypto by market cap]
- **Filters:** [Liquidity minimums, market cap thresholds, sector exclusions]
- **Universe Size:** [Expected N instruments after filtering]

### 2.2 Time Scope
- **Backtest Period:** [Start date] to [End date]
- **In-Sample:** [Date range for model development]
- **Out-of-Sample:** [Date range for validation]
- **Walk-Forward:** [If applicable, specify window parameters]

### 2.3 Frequency
- **Signal Generation:** [Daily, hourly, tick-level]
- **Rebalancing:** [Frequency and timing, e.g., daily close, weekly Monday open]
- **Holding Period:** [Expected average holding period]

---

## 3. Data Requirements

### 3.1 Primary Data Sources
| Data Type | Source | Frequency | History Required | Format |
|-----------|--------|-----------|------------------|--------|
| [Price]   | [Source] | [Freq]  | [Years]          | [CSV/API/DB] |
| [Volume]  | [Source] | [Freq]  | [Years]          | [CSV/API/DB] |

### 3.2 Alternative Data (if applicable)
| Data Type | Source | Frequency | History Required | Format |
|-----------|--------|-----------|------------------|--------|
| [Sentiment] | [Source] | [Freq] | [Years]        | [Format] |

### 3.3 Data Quality Requirements
- **Missing Data Handling:** [Forward fill, interpolation, exclusion rules]
- **Corporate Actions:** [Adjustment methodology for splits, dividends]
- **Survivorship Bias:** [Point-in-time universe construction approach]
- **Look-Ahead Bias Prevention:** [Data availability timestamps, publication lags]

### 3.4 Benchmark Data
- **Primary Benchmark:** [e.g., SPY, BTC, custom equal-weight]
- **Risk-Free Rate:** [Source and instrument]

---

## 4. Signal/Factor Specification

### 4.1 Signal Definition

Signal_t = f(Data_{t-1}, Parameters)

[Mathematical formulation of the signal]

### 4.2 Signal Components
| Component | Formula | Intuition |
|-----------|---------|-----------|
| [Raw Signal] | [Formula] | [Why this captures alpha] |
| [Normalization] | [Z-score, rank, etc.] | [Purpose of transformation] |

### 4.3 Parameters
| Parameter | Default | Range | Optimization Method |
|-----------|---------|-------|---------------------|
| [Lookback] | [N days] | [Min-Max] | [Grid search, Bayesian] |

### 4.4 Signal Processing
- **Winsorization:** [Percentile bounds if applicable]
- **Smoothing:** [EMA, SMA parameters if applicable]
- **Combination Method:** [If multi-factor: equal weight, IC-weighted, ML]

---

## 5. Portfolio Construction

### 5.1 Position Sizing
- **Method:** [Equal weight, volatility parity, signal-proportional, optimization]
- **Gross Exposure:** [Target gross exposure as % of capital]
- **Net Exposure:** [Long/short bias constraints]

### 5.2 Constraints
| Constraint | Value | Hard/Soft |
|------------|-------|-----------|
| Max Position Size | [%] | [Hard/Soft] |
| Sector Exposure | [%] | [Hard/Soft] |
| Turnover Limit | [% per period] | [Hard/Soft] |
| Min Holding Period | [Days] | [Hard/Soft] |

### 5.3 Rebalancing Logic
- **Trigger:** [Calendar-based, threshold-based, or hybrid]
- **Execution:** [Close prices, TWAP, VWAP assumptions]

---

## 6. Risk Management

### 6.1 Risk Metrics to Monitor
| Metric | Threshold | Action if Breached |
|--------|-----------|-------------------|
| Drawdown | [%] | [Reduce exposure, halt trading] |
| VaR (95%) | [$/%] | [Action] |
| Volatility | [Ann. %] | [Vol targeting adjustment] |

### 6.2 Factor Exposures
[List factor exposures to monitor: market beta, size, value, momentum, etc.]

### 6.3 Stress Testing Scenarios
| Scenario | Description | Expected Impact |
|----------|-------------|-----------------|
| [2008 Crisis] | [Conditions] | [Estimated loss] |
| [COVID Crash] | [Conditions] | [Estimated loss] |

---

## 7. Transaction Costs

### 7.1 Cost Model
| Cost Component | Estimate | Source/Methodology |
|----------------|----------|-------------------|
| Commission | [bps] | [Broker quotes, historical] |
| Spread | [bps] | [Historical bid-ask data] |
| Market Impact | [Formula] | [Square-root model, etc.] |
| Slippage | [bps] | [Historical execution data] |

### 7.2 Cost Sensitivity Analysis
[Define scenarios: optimistic, base case, pessimistic cost assumptions]

---

## 8. Performance Metrics

### 8.1 Primary Metrics
| Metric | Target | Minimum Acceptable |
|--------|--------|-------------------|
| Sharpe Ratio | [X] | [Y] |
| Sortino Ratio | [X] | [Y] |
| Max Drawdown | [%] | [%] |
| CAGR | [%] | [%] |

### 8.2 Secondary Metrics
- Calmar Ratio
- Win Rate
- Profit Factor
- Average Win / Average Loss
- Time in Drawdown

### 8.3 Statistical Significance
- **T-statistic Threshold:** [Minimum t-stat for significance]
- **Bootstrap Confidence Intervals:** [Methodology]
- **Multiple Testing Adjustment:** [Bonferroni, FDR if testing multiple signals]

---

## 9. Implementation Specification

### 9.1 Technology Stack
- **Language:** [Python, R, etc.]
- **Backtesting Framework:** [Custom, Zipline, Backtrader, vectorbt]
- **Data Storage:** [PostgreSQL, Parquet, HDF5]
- **Orchestration:** [Airflow, Prefect, cron]

### 9.2 Code Structure

project/ ├── data/ │ ├── raw/ # Unprocessed data │ └── processed/ # Clean, analysis-ready data ├── src/ │ ├── data/ # Data loaders and cleaners │ ├── signals/ # Signal generation │ ├── portfolio/ # Position sizing and optimization │ ├── backtest/ # Backtesting engine │ ├── risk/ # Risk calculations │ └── utils/ # Common utilities ├── notebooks/ # Research notebooks ├── tests/ # Unit and integration tests └── configs/ # Parameter configurations

### 9.3 Testing Requirements
- Unit tests for all signal calculations
- Integration tests for backtest pipeline
- Reconciliation tests against known benchmarks

---

## 10. Acceptance Criteria

### 10.1 Research Phase Complete When:
- [ ] Signal generates statistically significant returns in-sample
- [ ] Signal remains significant out-of-sample
- [ ] Performance survives realistic transaction costs
- [ ] Risk metrics within defined bounds
- [ ] No evidence of look-ahead or survivorship bias
- [ ] Code reviewed and tested
- [ ] Documentation complete

### 10.2 Go/No-Go Decision Framework
| Outcome | Criteria | Next Action |
|---------|----------|-------------|
| Proceed to Production | [All primary metrics met] | [Production deployment plan] |
| Iterate | [Partial success] | [Specific improvements to test] |
| Reject | [Hypothesis falsified] | [Document learnings, archive] |

---

## 11. Out of Scope

[Explicitly list what is NOT included to prevent scope creep]
- [Item 1]
- [Item 2]

---

## 12. References

- [Paper 1: Title, Authors, Link]
- [Paper 2: Title, Authors, Link]
- [Internal Document: Title, Location]

---

## User Stories

### [ ] US-001: Data Pipeline Setup
**As a** quant researcher
**I want** clean, point-in-time data loaded and validated
**So that** I can begin signal development without data quality issues

#### Acceptance Criteria
- [ ] Raw data ingested from specified sources
- [ ] Corporate actions properly adjusted
- [ ] Missing data handled per specification
- [ ] Universe filtered per criteria
- [ ] Data validation tests pass
- [ ] No look-ahead bias in data alignment

#### Technical Notes
[Specific implementation guidance]

---

### [ ] US-002: Signal Implementation
**As a** quant researcher
**I want** the signal calculated per specification
**So that** I can evaluate its predictive power

#### Acceptance Criteria
- [ ] Signal formula implemented correctly
- [ ] Parameters configurable
- [ ] Unit tests verify calculation accuracy
- [ ] Signal stored with proper timestamps
- [ ] Visualization of signal distribution available

#### Technical Notes
[Specific implementation guidance]

---

### [ ] US-003: Backtest Engine
**As a** quant researcher
**I want** a backtest simulating realistic trading
**So that** I can evaluate strategy performance

#### Acceptance Criteria
- [ ] Portfolio construction per specification
- [ ] Transaction costs applied correctly
- [ ] Rebalancing logic implemented
- [ ] Performance metrics calculated
- [ ] Results reproducible with same seed

#### Technical Notes
[Specific implementation guidance]

---

### [ ] US-004: Risk Analysis
**As a** quant researcher
**I want** comprehensive risk metrics and stress tests
**So that** I can understand strategy vulnerabilities

#### Acceptance Criteria
- [ ] All risk metrics from Section 6 calculated
- [ ] Stress test scenarios simulated
- [ ] Factor exposure analysis complete
- [ ] Risk dashboard/report generated

#### Technical Notes
[Specific implementation guidance]

---

### [ ] US-005: Performance Attribution
**As a** quant researcher
**I want** returns decomposed by source
**So that** I understand what drives performance

#### Acceptance Criteria
- [ ] Brinson attribution (if applicable)
- [ ] Factor attribution
- [ ] Sector/region attribution (if applicable)
- [ ] Attribution report generated

#### Technical Notes
[Specific implementation guidance]

---

### [ ] US-006: Statistical Validation
**As a** quant researcher
**I want** statistical tests of strategy robustness
**So that** I can be confident results aren't due to chance

#### Acceptance Criteria
- [ ] In-sample vs out-of-sample comparison
- [ ] T-statistics and p-values calculated
- [ ] Bootstrap confidence intervals generated
- [ ] Overfitting diagnostics (if applicable)
- [ ] Sensitivity to parameters documented

#### Technical Notes
[Specific implementation guidance]

---

### [ ] US-007: Documentation & Reporting
**As a** quant researcher
**I want** comprehensive research documentation
**So that** findings can be reviewed and replicated

#### Acceptance Criteria
- [ ] Research notebook with methodology
- [ ] Performance tearsheet generated
- [ ] Risk report complete
- [ ] Code documentation complete
- [ ] Go/No-Go recommendation documented

#### Technical Notes
[Specific implementation guidance]

JSON Conversion for Autonomous Execution

After QRD approval, convert to qrd.json for Ralph/autonomous loops:

{
  "projectName": "[Research Name]",
  "version": "1.0",
  "hypothesis": "[Core hypothesis statement]",
  "userStories": [
    {
      "id": "US-001",
      "title": "Data Pipeline Setup",
      "description": "Clean, point-in-time data loaded and validated",
      "acceptanceCriteria": [
        "Raw data ingested from specified sources",
        "Corporate actions properly adjusted",
        "Missing data handled per specification"
      ],
      "status": "pending",
      "passes": false
    }
  ],
  "metrics": {
    "targetSharpe": 1.5,
    "maxDrawdown": 0.15,
    "minTStat": 2.0
  }
}

Output Location

Save documents to: tasks/qrd-[research-name].md

Tips for Effective QRDs

1. Atomic Stories: Each user story should be completable in one agent context window
2. Testable Criteria: Every acceptance criterion must be verifiable programmatically
3. No Ambiguity: Parameters, thresholds, and formulas must be explicit
4. Bias Prevention: Explicitly address look-ahead, survivorship, and overfitting
5. Realistic Costs: Conservative transaction cost assumptions
6. Statistical Rigor: Multiple testing adjustment if scanning many signals