77 lines
4.7 KiB
Markdown
77 lines
4.7 KiB
Markdown
# PRD: VectorBT Migration & CCXT Integration
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## 1. Introduction
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The goal of this project is to refactor the current backtesting infrastructure to a professional-grade stack using **VectorBT** for high-performance backtesting and **CCXT** for robust historical data acquisition. The system will support rapid prototyping of "many simple strategies," parameter optimization (Grid Search), and stability testing (Walk-Forward Analysis).
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## 2. Goals
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- **Replace Custom Backtester:** Retire the existing loop-based backtesting logic in favor of vectorized operations using `vectorbt`.
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- **Automate Data Collection:** Implement a `ccxt` based downloader to fetch and cache OHLCV data from OKX (and other exchanges) automatically.
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- **Enable Optimization:** Built-in support for Grid Search to find optimal strategy parameters.
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- **Validation:** Implement Walk-Forward Analysis (WFA) to validate strategy robustness and prevent overfitting.
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- **Standardized Reporting:** Generate consistent outputs: Console summaries, CSV logs, and VectorBT interactive plots.
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## 3. User Stories
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- **Data Acquisition:** "As a user, I want to run a command `download_data --pair BTC/USDT --exchange okx` and have the system fetch historical 1-minute candles and save them to `data/ccxt/okx/BTC-USDT/1m.csv`."
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- **Strategy Dev:** "As a researcher, I want to define a new strategy by simply writing a class/function that defines entry/exit signals, without worrying about the backtesting loop."
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- **Optimization:** "As a researcher, I want to say 'Optimize RSI period between 10 and 20' and get a heatmap of results."
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- **Validation:** "As a researcher, I want to verify if my 'best' parameters work on unseen data using Walk-Forward Analysis."
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- **Analysis:** "As a user, I want to see an equity curve and key metrics (Sharpe, Drawdown) immediately after a test run."
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## 4. Functional Requirements
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### 4.1 Data Module (`data_manager`)
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- **Exchange Interface:** Use `ccxt` to connect to exchanges (initially OKX).
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- **Fetching Logic:** Fetch OHLCV data in chunks to handle rate limits and long histories.
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- **Storage:** Save data to standardized paths: `data/ccxt/{exchange}/{pair}_{timeframe}.csv`.
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- **Loading:** Utility to load saved CSVs into a Pandas DataFrame compatible with `vectorbt`.
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### 4.2 Strategy Interface (`strategies/`)
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- **Base Protocol:** Define a standard structure for strategies. A strategy should return/define:
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- Indicator calculations (Vectorized).
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- Entry signals (Boolean Series).
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- Exit signals (Boolean Series).
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- **Parameterization:** Strategies must accept dynamic parameters to support Grid Search.
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### 4.3 Backtest Engine (`engine.py`)
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- **Simulation:** Use `vectorbt.Portfolio.from_signals` (or similar) for fast simulation.
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- **Cost Model:** Support configurable fees (maker/taker) and slippage estimates.
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- **Grid Search:** Utilize `vectorbt`'s parameter broadcasting to run many variations simultaneously.
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- **Walk-Forward Analysis:**
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- Implement a splitting mechanism (e.g., `vectorbt.Splitter`) to divide data into In-Sample (Train) and Out-of-Sample (Test) sets.
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- Execute optimization on Train, validate on Test.
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### 4.4 Reporting (`reporting.py`)
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- **Console:** Print key metrics: Total Return, Sharpe Ratio, Max Drawdown, Win Rate, Count of Trades.
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- **Files:** Save detailed trade logs and metrics summaries to `backtest_logs/`.
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- **Visuals:** Generate and save/show `vectorbt` plots (Equity curve, Drawdowns).
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## 5. Non-Goals
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- Real-time live trading execution (this is strictly for research/backtesting).
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- Complex Machine Learning models (initially focusing on indicator-based logic).
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- High-frequency tick-level backtesting (1-minute granularity is the target).
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## 6. Technical Architecture Proposal
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```text
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project_root/
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├── data/
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│ └── ccxt/ # New data storage structure
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├── strategies/ # Strategy definitions
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│ ├── __init__.py
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│ ├── base.py # Abstract Base Class
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│ └── ma_cross.py # Example strategy
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├── engine/
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│ ├── data_loader.py # CCXT wrapper
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│ ├── backtester.py # VBT runner
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│ └── optimizer.py # Grid Search & WFA logic
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├── main.py # CLI entry point
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└── pyproject.toml
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```
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## 7. Success Metrics
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- Can download 1 year of 1m BTC/USDT data from OKX in < 2 minutes.
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- Can run a 100-parameter grid search on 1 year of 1m data in < 10 seconds.
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- Walk-forward analysis produces a clear "Robustness Score" or visual comparison of Train vs Test performance.
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## 8. Open Questions
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- Do we need to handle funding rates for perp futures in the PnL calculation immediately? (Assumed NO for V1, stick to spot/simple futures price action).
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