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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: TA-Lib (ALWAYS - never use VectorBT built-in indicators)
• Specialty indicators: openalgo.ta for Supertrend, Donchian, Ichimoku, HMA, KAMA, ALMA, ZLEMA, VWMA
• Signal cleaning: openalgo.ta for exrem, crossover, crossunder, flip
• 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. ALWAYS use TA-Lib for ALL technical indicators (EMA, SMA, RSI, MACD, BBANDS, ATR, ADX, STDDEV, MOM). NEVER use vbt.MA.run(), vbt.RSI.run(), or any VectorBT built-in indicator.
2. Use OpenAlgo ta for indicators NOT in TA-Lib: Supertrend, Donchian, Ichimoku, HMA, KAMA, ALMA, ZLEMA, VWMA.
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. with after generating raw buy/sell signals. Always before exrem.

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 talib as tl
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:
    df["timestamp"] = pd.to_datetime(df["timestamp"])
    df = df.set_index("timestamp")
else:
    df.index = pd.to_datetime(df.index)
df = df.sort_index()
if df.index.tz is not None:
    df.index = df.index.tz_convert(None)

close = df["close"]

# --- Strategy: EMA Crossover (TA-Lib) ---
ema_fast = pd.Series(tl.EMA(close.values, timeperiod=10), index=close.index)
ema_slow = pd.Series(tl.EMA(close.values, timeperiod=20), index=close.index)

buy_raw = (ema_fast > ema_slow) & (ema_fast.shift(1) <= ema_slow.shift(1))
sell_raw = (ema_fast < ema_slow) & (ema_fast.shift(1) >= ema_slow.shift(1))

entries = ta.exrem(buy_raw.fillna(False), sell_raw.fillna(False))
exits = ta.exrem(sell_raw.fillna(False), buy_raw.fillna(False))

# --- Backtest ---
pf = vbt.Portfolio.from_signals(
    close, entries, exits,
    init_cash=INIT_CASH, size=ALLOCATION, size_type="percent",
    fees=FEES, fixed_fees=FIXED_FEES, direction="longonly",
    min_size=1, size_granularity=1, freq="1D",
)

# --- Benchmark ---
df_bench = client.history(
    symbol=BENCHMARK_SYMBOL, exchange=BENCHMARK_EXCHANGE, interval=INTERVAL,
    start_date=start_date.strftime("%Y-%m-%d"),
    end_date=end_date.strftime("%Y-%m-%d"),
)
if "timestamp" in df_bench.columns:
    df_bench["timestamp"] = pd.to_datetime(df_bench["timestamp"])
    df_bench = df_bench.set_index("timestamp")
else:
    df_bench.index = pd.to_datetime(df_bench.index)
df_bench = df_bench.sort_index()
if df_bench.index.tz is not None:
    df_bench.index = df_bench.index.tz_convert(None)
bench_close = df_bench["close"].reindex(close.index).ffill().bfill()
pf_bench = vbt.Portfolio.from_holding(bench_close, init_cash=INIT_CASH, fees=FEES, freq="1D")

# --- Results ---
print(pf.stats())

# --- Strategy vs Benchmark ---
comparison = pd.DataFrame({
    "Strategy": [
        f"{pf.total_return() * 100:.2f}%", f"{pf.sharpe_ratio():.2f}",
        f"{pf.sortino_ratio():.2f}", f"{pf.max_drawdown() * 100:.2f}%",
        f"{pf.trades.win_rate() * 100:.1f}%", f"{pf.trades.count()}",
        f"{pf.trades.profit_factor():.2f}",
    ],
    f"Benchmark ({BENCHMARK_SYMBOL})": [
        f"{pf_bench.total_return() * 100:.2f}%", f"{pf_bench.sharpe_ratio():.2f}",
        f"{pf_bench.sortino_ratio():.2f}", f"{pf_bench.max_drawdown() * 100:.2f}%",
        "-", "-", "-",
    ],
}, index=["Total Return", "Sharpe Ratio", "Sortino Ratio", "Max Drawdown",
          "Win Rate", "Total Trades", "Profit Factor"])
print(comparison.to_string())

# --- Explain ---
print(f"* Total Return: {pf.total_return() * 100:.2f}% vs NIFTY {pf_bench.total_return() * 100:.2f}%")
print(f"* Max Drawdown: {pf.max_drawdown() * 100:.2f}%")
print(f"  -> On Rs {INIT_CASH:,}, worst temporary loss = Rs {abs(pf.max_drawdown()) * INIT_CASH:,.0f}")

# --- Plot ---
fig = pf.plot(subplots=['value', 'underwater', 'cum_returns'], template="plotly_dark")
fig.show()

# --- Export ---
pf.positions.records_readable.to_csv(script_dir / f"{SYMBOL}_trades.csv", index=False)

Quick Template: DuckDB Backtest Script

import datetime as dt
from pathlib import Path

import duckdb
import numpy as np
import pandas as pd
import talib as tl
import vectorbt as vbt

try:
    from openalgo import ta
    exrem = ta.exrem
except ImportError:
    def exrem(signal1, signal2):
        result = signal1.copy()
        active = False
        for i in range(len(signal1)):
            if active:
                result.iloc[i] = False
            if signal1.iloc[i] and not active:
                active = True
            if signal2.iloc[i]:
                active = False
        return result

# --- Config ---
SYMBOL = "SBIN"
DB_PATH = r"path/to/market_data.duckdb"
INIT_CASH = 1_000_000
FEES = 0.000225              # Intraday equity
FIXED_FEES = 20

# --- Load from DuckDB ---
con = duckdb.connect(DB_PATH, read_only=True)
df = con.execute("""
    SELECT date, time, open, high, low, close, volume
    FROM ohlcv WHERE symbol = ? ORDER BY date, time
""", [SYMBOL]).fetchdf()
con.close()

df["datetime"] = pd.to_datetime(df["date"].astype(str) + " " + df["time"].astype(str))
df = df.set_index("datetime").sort_index()
df = df.drop(columns=["date", "time"])

# --- Resample to 5min ---
df_5m = df.resample("5min", origin="start_day", offset="9h15min",
                     label="right", closed="right").agg({
    "open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"
}).dropna()
close = df_5m["close"]

# --- Strategy + Backtest (same as OpenAlgo template) ---

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533

Repository

marketcalls/vec…g-skills

GitHub Stars

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First Seen

Feb 25, 2026

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