Предварительное описание торговых стратегий и план проверки
Статья Machine Learning for Trading
Сводка
В этом обзоре главы описан процесс определения торговых стратегий на основе гипотез до проведения бэктеста. Документированные допущения о данных и неизменяемая конфигурация связываются с исследовательским анализом, событийными исследованиями и структурированным паспортом стратегии. Предлагаемый паспорт фиксирует проверяемую гипотезу, план реализации, требования к осуществимости и план валидации, помогая превратить результаты исследования в спецификации, которые можно проверять и версионировать.
В материале рассматриваются примеры импульсной стратегии на ETF, возврата к среднему по ставке финансирования криптовалют, внутридневной микроструктуры рынка и данных об исторических факторах. Особое внимание уделяется практикам с учётом доступности данных на момент времени: макроданные с лагом, скользящие статистики, объединение по времени с будущей доходностью и правила паузы для событийных исследований. Среди статистических примеров — тесты средней доходности с поправкой Ньюи — Уэста и оценки неопределённости коэффициента Шарпа. Это обзор архитектуры и кода, а не новое эмпирическое исследование: оценки качества и рекомендации принадлежат автору, а описанные результаты предварительного анализа не доказывают доходность в реальной торговле или вне выборки.
Ключевые идеи
- Стратегия должна начинаться с проверяемой гипотезы и документированных допущений о данных.
- Структурированный паспорт стратегии может фиксировать реализацию, осуществимость и требования к валидации в версионируемом виде.
- Объединение по времени с допусками безопаснее при нерегулярных наблюдениях, чем предположение о фиксированных сдвигах.
- Лаги с учётом доступности данных и скользящие расчёты помогают уменьшить смещение заглядывания вперёд при исследовательском анализе.
- Предварительный анализ и проверка качества кода не доказывают эффективность вне выборки.
Теги
Полный текст
# Chapter 5: Strategy Definition - Codebase Analysis
# Chapter 5: Strategy Definition - Codebase Analysis
**Generated**: 2026-01-25
**Analysis Type**: Deep Architecture & Pattern Review
---
## Executive Summary
Chapter 5 is a **mature, well-architected** educational codebase demonstrating strategy pre-specification. The code quality is high with consistent patterns, strong documentation, and proper separation of concerns. The architecture follows a **hypothesis-driven exploration** pattern rather than a production trading system pattern.
**Overall Quality**: ★★★★☆ (4/5) - Publication-ready with minor improvements possible
---
## Architecture Overview
```
06_strategy_definition/
├── chapter/ # Book manuscript (section-based workflow)
│ ├── draft.md # Auto-generated from sections
│ ├── summary.md # Chapter scope definition
│ ├── bibliography.md # Academic references
│ └── sections/ # 10 section files (ground truth)
│
├── code/ # 5 Jupytext notebooks (~4,500 LOC)
│ ├── 01_etf_momentum.py # ETF correlation, regime, rotation (858 lines)
│ ├── 02_crypto_premium.py # Funding rate mean reversion (792 lines)
│ ├── 03_algoseek_intraday.py # Microstructure exploration (817 lines)
│ ├── 04_aqr_factor_performance.py # Century of factor evidence (1,395 lines)
│ └── 05_strategy_term_sheet_template.py # Python dataclasses (694 lines)
│
├── term_sheets/ # 8 completed strategy specifications
├── catalog/ # 5 notebook metadata JSON files
├── figures/ # Book figures (AI-generated + notebook)
└── reviews/ # Code review artifacts
```
### Pattern: Hypothesis-Driven Exploration
The codebase follows a clear pedagogical pattern:
```
1. Data Contract (documented assumptions)
↓
2. Configuration Dataclass (frozen, versioned)
↓
3. Exploratory Analysis (correlation, distribution, regime)
↓
4. Event Study (with cooldown, forward returns)
↓
5. Key Priors Summary (for Term Sheet)
```
---
## Code Quality Assessment
### Strengths
| Aspect | Score | Evidence |
|--------|-------|----------|
| **Documentation** | ★★★★★ | Every notebook has purpose, data contract, assumptions, limitations |
| **Type Safety** | ★★★★☆ | Frozen dataclasses, type hints in key functions |
| **Polars Usage** | ★★★★★ | Proper lazy API, `.over()` windows, no pandas except boundaries |
| **Time Handling** | ★★★★★ | Time-based joins (not shifts), timezone-aware, tolerance parameters |
| **Point-in-Time** | ★★★★☆ | Rolling z-scores, macro lag handling, clear look-ahead warnings |
### Patterns Observed
#### 1. Configuration Dataclasses (Excellent)
```python
@dataclass(frozen=True)
class EtfExplorationConfig:
"""Configuration for ETF momentum exploration (Chapter 2)."""
start_date_str: str = "2007-01-01"
trading_days_per_year: int = 252
momentum_formation_days: int = 126 # 6 months
skip_month_days: int = 21 # Skip most recent month
# ... etc
```
**Benefit**: Immutable, documented, single source of truth for notebook parameters.
#### 2. Time-Based Forward Returns (Production-Quality)
```python
def _compute_forward_returns_hourly(
df: pl.DataFrame,
*,
price_col: str,
horizons_hours: tuple[int, ...],
group_col: str,
ts_col: str,
tolerance_hours: int = 2,
) -> pl.DataFrame:
```
**Pattern**: Uses `join_asof` with tolerance instead of `.shift()` to handle gaps correctly.
#### 3. Event Study with Proper Cooldown
```python
def event_study_with_cooldown(
df: pl.DataFrame,
regime_col: str = "regime",
cooldown_hours: int = 24,
) -> pl.DataFrame:
```
**Pattern**: Stateful thinning that compares to last KEPT event, not previous event.
#### 4. Strategy Term Sheet as Python Dataclass
```python
@dataclass
class StrategyTermSheet:
name: str
version: str
status: Literal["Draft", "Review", "Approved"]
classification: Literal["Price-Based", "Fundamental", "Structural", "ML-Enhanced"]
hypothesis: FalsifiableHypothesis
blueprint: ImplementationBlueprint
feasibility: FeasibilityGate
validation: ValidationPlan
```
**Benefit**: Enforces structure, enables JSON/YAML export, supports version control.
---
## Component Inventory
### Notebooks (5)
| Notebook | Purpose | Data Sources | Lines | Quality |
|----------|---------|--------------|-------|---------|
| `01_etf_momentum` | Correlation, regime, rotation | ETF Universe, FRED | 858 | ★★★★★ |
| `02_crypto_premium` | Funding rate mean reversion | Binance Premium/OHLCV | 792 | ★★★★★ |
| `03_algoseek_intraday` | Microstructure exploration | AlgoSeek NASDAQ100 | 817 | ★★★★☆ |
| `04_aqr_factor_performance` | Century of factor evidence | AQR, Fama-French | 1,395 | ★★★★★ |
| `05_strategy_term_sheet_template` | Interactive Term Sheet | None | 694 | ★★★★★ |
### Term Sheets (8)
| Term Sheet | Classification | Status |
|------------|----------------|--------|
| `etf_momentum__rotational_momentum.md` | Price-Based | Complete |
| `crypto_premium__premium_funding_reversal.md` | Structural | Complete |
| `nasdaq100_reversal__intraday_orderflow_reversal.md` | Structural | Complete |
| `us_factors__cross_sectional_factor_momentum.md` | Price-Based | Complete |
| `futures_carry__term_structure_momentum.md` | Structural | Complete |
| `fx_momentum__cross_sectional_momentum.md` | Price-Based | Complete |
| `algoseek_sp500__options_volatility_alpha.md` | Structural | Complete |
| `etf_momentum.md` | Price-Based | Legacy (use etf_momentum version) |
### Chapter Sections (10)
```
00_preamble.md
01_the_strategy_specification_problem.md
02_a_map_of_strategies_and_edges.md
03_the_strategy_term_sheet.md
04_filling_the_term_sheet.md
05_minimum_specification_feasibility.md
06_validation_plan_and_failure_conditions.md
07_where_ideas_come_from.md
08_evidence_discipline_and_the_factor_zoo.md
09_key_takeaways.md
```
---
## Data Flow Architecture
```
┌─────────────────┐
│ Data Layer │
│ (utils, DATA_DIR)│
└────────┬────────┘
│
┌────────────────────┼────────────────────┐
│ │ │
▼ ▼ ▼
┌───────────────┐ ┌───────────────┐ ┌───────────────┐
│ ETF Universe │ │ Crypto Premium│ │ AlgoSeek │
│ + FRED │ │ + OHLCV │ │ Minute Bars │
└───────┬───────┘ └───────┬───────┘ └───────┬───────┘
│ │ │
▼ ▼ ▼
┌───────────────────────────────────────────────────┐
│ Exploration Notebooks │
│ (correlation, regime, event study, statistics) │
└───────────────────────┬───────────────────────────┘
│
▼
┌───────────────────────────────────────────────────┐
│ Strategy Term Sheets │
│ (hypothesis, blueprint, feasibility, validation)│
└───────────────────────────────────────────────────┘
│
▼
Downstream Chapters
(Ch7 features, Ch17 backtest)
```
---
## Statistical Methods Used
### Factor Analysis (04_aqr_factor_performance.py)
| Method | Implementation | Quality |
|--------|----------------|---------|
| Newey-West HAC t-stat | `calculate_mean_return_tstat()` | ★★★★★ |
| Lo (2002) Sharpe SE | `calculate_sharpe_stats()` | ★★★★★ |
| Max Drawdown | `calculate_max_drawdown()` | ★★★★☆ |
| Rolling Correlation | `pl.rolling_corr()` native | ★★★★★ |
**Notable**: Proper distinction between Harvey et al. (2016) mean-return t-stat (for factor discovery) and Lo (2002) Sharpe ratio significance (for performance).
### Momentum Analysis (01_etf_momentum.py)
| Method | Implementation | Quality |
|--------|----------------|---------|
| Cross-sectional quintile ranking | `.rank("ordinal").over("timestamp")` | ★★★★★ |
| 6-1 Momentum (skip-month) | Vectorized with `.shift()` | ★★★★★ |
| Yield curve regime filter | FRED data with 2-bday lag | ★★★★★ |
| Rotation simulation | Month-by-month loop (pedagogical) | ★★★★☆ |
### Event Studies (02_crypto_premium.py, 03_algoseek_intraday.py)
| Method | Implementation | Quality |
|--------|----------------|---------|
| Rolling z-score | Point-in-time (168h window) | ★★★★★ |
| Event cooldown | Stateful per-group thinning | ★★★★★ |
| Forward returns | Time-based join with tolerance | ★★★★★ |
| Winsorization | Global quantiles (exploration only) | ★★★★☆ |
---
## Improvement Recommendations
### Priority 1: Minor Code Improvements
1. **Add `__all__` exports to notebooks** for potential module reuse:
```python
__all__ = ["EtfExplorationConfig", "calculate_momentum_score"]
```
2. **Extract common utilities** to shared module:
- `_compute_forward_returns_*` functions appear in multiple notebooks
- `_event_cooldown_filter` is duplicated
- Could live in `utils/exploration.py`
3. **Add catalog entry for `etf_momentum.md` legacy term sheet** or remove:
- Currently `etf_momentum.md` exists alongside `etf_momentum__rotational_momentum.md`
- Confusing which is canonical
### Priority 2: Documentation Enhancements
1. **Add notebook execution order** to README.md:
```markdown
## Execution Order
1. 01_etf_momentum.py (ETF data exploration)
2. 02_crypto_premium.py (crypto case study)
...
```
2. **Document term sheet naming convention**:
- Pattern: `{dataset}__{strategy_type}.md`
- Examples: `etf_momentum__rotational_momentum.md`, `crypto_premium__premium_funding_reversal.md`
### Priority 3: Testing Coverage
1. **Add unit tests for statistical functions**:
- `calculate_mean_return_tstat()` - verify against known values
- `calculate_sharpe_stats()` - test Lo (2002) SE calculation
- `_event_cooldown_filter()` - edge cases
2. **Add data contract validation tests**:
- Verify parquet schemas match documented contracts
- Check for expected columns and dtypes
---
## Risk Areas
### Low Risk (Acceptable)
| Area | Observation | Mitigation |
|------|-------------|------------|
| Pandas boundary | Matplotlib/scipy require pandas | Minimal, conversion at boundary only |
| Global winsorization | Uses full-sample quantiles | Documented as exploration-only |
| Loop in rotation sim | Month-by-month for clarity | Acceptable for pedagogy |
### Medium Risk (Monitor)
| Area | Observation | Mitigation |
|------|-------------|------------|
| Duplicate term sheets | `etf_momentum.md` vs `etf_momentum__*.md` | Clarify canonical version |
| Hardcoded magic numbers | Some thresholds in notebook body | Move to config dataclass |
| Missing type hints | Helper functions lack full typing | Add for production reuse |
---
## Dependency Analysis
### External Dependencies
```python
# Core
polars # DataFrames (primary)
numpy # Numerical operations
pandas # Boundary conversions only
plotly # Visualization
# Domain
scipy.stats # Statistical tests
IPython.display # Jupyter rendering
# ML4T
utils # DATA_DIR, paths
ml4t.data.providers # AQR, Fama-French
```
### Internal Dependencies
```
05_strategy_term_sheet_template.py
↓ exports
StrategyTermSheet (dataclass)
↓ used by
term_sheets/*.md (via to_markdown())
```
---
## Alignment with Book Standards
| Standard | Compliance | Notes |
|----------|------------|-------|
| Polars-first | ✅ Full | No pandas except visualization boundaries |
| Frozen config dataclass | ✅ Full | All notebooks use this pattern |
| Time-based joins | ✅ Full | No `.shift()` for forward returns |
| Data contracts documented | ✅ Full | Every notebook has contract table |
| Output to `figures/` only | ✅ Full | No ad-hoc file saves |
| No code in chapter text | ✅ Full | References notebooks by name |
| TEST mode support | ⚠️ Partial | Not all notebooks have explicit TEST guards |
---
## Summary
Chapter 5 demonstrates **exemplary educational code architecture**:
1. **Clear separation**: Data contracts → Config → Analysis → Summary
2. **Proper statistics**: Newey-West HAC, Lo Sharpe SE, time-based forward returns
3. **Point-in-time discipline**: Rolling z-scores, macro lag handling
4. **Versioned artifacts**: Term sheets as structured documents
**Recommended actions**:
- Extract common utilities to reduce duplication
- Clarify canonical term sheet naming
- Add unit tests for statistical functions
**Overall**: Ready for publication with minor cleanup.
---
*Analysis performed by Claude Code /development:analyze*Полный текст с указанием источника опубликован на условиях его лицензии. Лицензия: MIT
Это краткое изложение подготовлено исследовательским агентом Stratmill по оригиналу и не является его копией.