Vectorbt Expert

VectorBT Backtesting Expert Skill

Environment

• Python with vectorbt, pandas, numpy, plotly
• Data sources: OpenAlgo (Indian markets), DuckDB (direct database), yfinance (US/Global), CCXT (Crypto), custom providers
• DuckDB support: supports both custom DuckDB and OpenAlgo Historify format
• API keys loaded from single root .env via python-dotenv + find_dotenv() — never hardcode keys
• Technical indicators: OpenAlgo ta (DEFAULT - from openalgo import ta, 100+ indicators covering trend/momentum/volatility/volume/oscillators/statistical/hybrid). Use TA-Lib only if the user explicitly asks for TA-Lib/talib. NEVER use VectorBT built-in indicators either way.
• Specialty indicators (no TA-Lib equivalent, always openalgo.ta): Supertrend, Donchian, Ichimoku, HMA, KAMA, ALMA, ZLEMA, VWMA
• Signal cleaning: openalgo.ta for exrem, crossover, crossunder, flip (always, regardless of indicator library)
• Fee model: Indian market standard (STT + statutory charges + Rs 20/order)
• Benchmark: NIFTY 50 via OpenAlgo (NSE_INDEX) by default
• Charts: Plotly with template="plotly_dark"
• Environment variables loaded from single .env at project root via find_dotenv() (walks up from script dir)
• Scripts go in backtesting/{strategy_name}/ directories (created on-demand, not pre-created)
• Never use icons/emojis in code or logger output

Critical Rules

1. Default to OpenAlgo ta (from openalgo import ta) for ALL technical indicators (EMA, SMA, RSI, MACD, BBANDS, ATR, ADX, STDDEV, MOM, and 90+ more). Only use TA-Lib if the user explicitly requests "talib"/"TA-Lib" in their prompt. NEVER use vbt.MA.run(), vbt.RSI.run(), or any VectorBT built-in indicator with either library.
2. Always use OpenAlgo ta for indicators not in TA-Lib at all: Supertrend, Donchian, Ichimoku, HMA, KAMA, ALMA, ZLEMA, VWMA - these have no TA-Lib equivalent, so they're openalgo.ta even in a TA-Lib-opt-in script.
3. Use OpenAlgo ta for signal utilities: ta.exrem(), ta.crossover(), ta.crossunder(), ta.flip(). If openalgo.ta is not importable (standalone DuckDB), use inline exrem() fallback. See duckdb-data.
4. Always clean signals with ta.exrem() after generating raw buy/sell signals. Always .fillna(False) before exrem.
5. Market-specific fees: India (indian-market-costs), US (us-market-costs), Crypto (crypto-market-costs). Auto-select based on user's market.
6. Default benchmarks: India=NIFTY via OpenAlgo, US=S&P 500 (^GSPC), Crypto=Bitcoin (BTC-USD). See data-fetching Market Selection Guide.
7. Always produce a Strategy vs Benchmark comparison table after every backtest.
8. Always explain the backtest report in plain language so even normal traders understand risk and strength.
9. Plotly candlestick charts must use xaxis type="category" to avoid weekend gaps.
10. Whole shares: Always set min_size=1, size_granularity=1 for equities.
11. DuckDB data loading: When user provides a DuckDB path, load data directly using duckdb.connect() with read_only=True. Auto-detect format: OpenAlgo Historify (table market_data, epoch timestamps) vs custom (table ohlcv, date+time columns). See duckdb-data.

Modular Rule Files

Detailed reference for each topic is in rules/:

Strategy Templates (in rules/assets/)

Production-ready scripts with realistic fees, NIFTY benchmark, comparison table, and plain-language report:

Quick Template: Standard Backtest Script

import os
from datetime import datetime, timedelta
from pathlib import Path

import numpy as np
import pandas as pd
import vectorbt as vbt
from dotenv import find_dotenv, load_dotenv
from openalgo import api, ta

# --- Config ---
script_dir = Path(__file__).resolve().parent
load_dotenv(find_dotenv(), override=False)

SYMBOL = "SBIN"
EXCHANGE = "NSE"
INTERVAL = "D"
INIT_CASH = 1_000_000
FEES = 0.00111              # Indian delivery equity (STT + statutory)
FIXED_FEES = 20             # Rs 20 per order
ALLOCATION = 0.75
BENCHMARK_SYMBOL = "NIFTY"
BENCHMARK_EXCHANGE = "NSE_INDEX"

# --- Fetch Data ---
client = api(
    api_key=os.getenv("OPENALGO_API_KEY"),
    host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"),
)

end_date = datetime.now().date()
start_date = end_date - timedelta(days=365 * 3)

df = client.history(
    symbol=SYMBOL, exchange=EXCHANGE, interval=INTERVAL,
    start_date=start_date.strftime("%Y-%m-%d"),
    end_date=end_date.strftime("%Y-%m-%d"),
)
if "timestamp" in df.columns: