--- name: bybit-orderbook-backtester-98a418884a02 description: > Download, process, and backtest ByBit derivatives historical order book data. Use this skill when the user wants to: (1) download historical order book snapshots from ByBit's derivatives history-data page using Selenium automation, (2) process/unzip ob500 JSONL files and filter to depth 50, (3) run any of 10 order-book-based trading strategies (Order Book Imbalance, Breakout, False Breakout, Scalping, Momentum, Reversal, Spoofing Detection, Optimal Execution, Market Making, Latency Arbitrage) against the data, or (4) generate full backtest performance reports with PnL, Sharpe ratio, win rate, max drawdown, and strategy comparison. Triggers on: "bybit order book", "order book backtest", "download bybit data", "ob500", "order book imbalance", "spoofing detection strategy", "market making backtest", "crypto order book", "depth of book backtest", "bybit historical data". --- # ByBit Order Book Backtester End-to-end pipeline: download → process → backtest → report. ## Dependencies ```bash pip install undetected-chromedriver selenium pandas numpy pyarrow --break-system-packages ``` Chrome/Chromium must be installed for Selenium. ## Workflow The pipeline has 3 stages. Run them sequentially, or skip to later stages if data is already available. ### Stage 1: Download Order Book Data Prompt the user for: - **Symbol** (default: BTCUSDT) - **Date range** (default: last 30 days) Run `scripts/download_orderbook.py`: ```bash python scripts/download_orderbook.py \ --symbol BTCUSDT \ --start 2024-06-01 --end 2024-06-30 \ --output ./data/raw ``` Key details: - Downloads from `https://www.bybit.com/derivatives/en/history-data` - Automatically chunks into 7-day windows (ByBit's limit) - Uses `undetected-chromedriver` for Cloudflare bypass - Outputs: ZIP files in `./data/raw/` named `{date}_{symbol}_ob500.data.zip` - For data format details: see `references/bybit_data_format.md` **If Selenium fails** (Cloudflare blocks, UI changes): Instruct the user to manually download from the ByBit page and place ZIPs in `./data/raw/`. ### Stage 2: Process & Filter to Depth 50 Run `scripts/process_orderbook.py`: ```bash python scripts/process_orderbook.py \ --input ./data/raw \ --output ./data/processed \ --depth 50 \ --sample-interval 1s ``` What it does: - Reads JSONL from ZIPs (each line = full 500-level L2 snapshot) - Filters to top 50 bid/ask levels - Computes derived features: mid_price, spread, volume_imbalance, microprice - Optionally downsamples (e.g., `1s`, `5s`, `1min`) — recommended for faster backtests - Outputs: Parquet files in `./data/processed/` **Without downsampling**: ~860K snapshots/day, ~300 MB Parquet per day per symbol. **With 1s downsampling**: ~86K snapshots/day, ~5 MB per day — much more practical. ### Stage 3: Backtest Strategies Run `scripts/backtest.py`: ```bash # Run all 10 strategies python scripts/backtest.py \ --input ./data/processed/BTCUSDT_ob50.parquet \ --output ./reports # Run specific strategies python scripts/backtest.py \ --input ./data/processed/BTCUSDT_ob50.parquet \ --strategies imbalance,breakout,market_making \ --output ./reports # Quick test with limited rows python scripts/backtest.py \ --input ./data/processed/BTCUSDT_ob50.parquet \ --max-rows 100000 \ --output ./reports ``` Strategy keys: `imbalance`, `breakout`, `false_breakout`, `scalping`, `momentum`, `reversal`, `spoofing`, `optimal_execution`, `market_making`, `latency_arb` Outputs in `./reports/`: - `{SYMBOL}_backtest_report.json` — Full results with equity curves - `{SYMBOL}_backtest_report.md` — Comparison table and detailed metrics Report metrics per strategy: total trades, winners/losers, win rate, cumulative PnL, Sharpe ratio, max drawdown (absolute and %), avg PnL per trade, avg hold time, profit factor, best/worst trade, equity curve. For strategy logic and tunable parameters: see `references/strategies.md` ## Customization To modify strategy parameters, edit the `__init__` method of any strategy class in `scripts/backtest.py`. Each strategy's `self.params` dict contains all tunables. To add a new strategy: 1. Subclass `Strategy` in `scripts/backtest.py` 2. Implement `on_snapshot(self, row, idx, df)` with entry/exit logic 3. Register in `STRATEGY_MAP` ## Troubleshooting **Selenium can't load ByBit page**: ByBit uses Cloudflare. Ensure `undetected-chromedriver` is up to date. Try `--no-headless` to debug visually. Fall back to manual download. **Out of memory on processing**: Use `--sample-interval 1s` or larger. Process one day at a time. **No trades generated**: Strategy thresholds may be too tight for the data period. Relax parameters (lower thresholds, shorter lookbacks) in `references/strategies.md`. --- Source attribution: kbarbel640-del Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/davidm413/bybit-order-book/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 clawdbot Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: kbarbel640-del Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/davidm413/bybit-order-book/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Finance & Accounting, Marketing, Product Management Roles: Data Analyst, Financial Analyst, Growth Marketer, ML / AI Engineer, Product Manager Specialties: API, Backend Tools: Numpy, Pandas, Pyarrow, Selenium Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 67eabb252c98b87a0c96352dcf404abdd6c0b368b73b9f240f855d369a2c2127