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

• 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.
• 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.
• 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.
• Always clean signals with ta.exrem() after generating raw buy/sell signals. Always .fillna(False) before exrem.
• Market-specific fees: India (indian-market-costs), US (us-market-costs), Crypto (crypto-market-costs). Auto-select based on user's market.
• Default benchmarks: India=NIFTY via OpenAlgo, US=S&P 500 (^GSPC), Crypto=Bitcoin (BTC-USD). See data-fetching Market Selection Guide.
• Always produce a Strategy vs Benchmark comparison table after every backtest.
• Always explain the backtest report in plain language so even normal traders understand risk and strength.
• 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/:

| Rule File | Topic |

|-----------|-------|

| data-fetching | OpenAlgo (India), yfinance (US), CCXT (Crypto), custom providers, .env setup |

| simulation-modes | from_signals, from_orders, from_holding, direction types |

| position-sizing | Amount/Value/Percent/TargetPercent sizing |

| indicators-signals | OpenAlgo ta indicator reference (default), TA-Lib opt-in, signal generation |

| openalgo-ta-helpers | Complete OpenAlgo ta catalog (100+ indicators): exrem, crossover, Supertrend, Donchian, Ichimoku, MAs |

| stop-loss-take-profit | Fixed SL, TP, trailing stop |

| parameter-optimization | Broadcasting and loop-based optimization |

| performance-analysis | Stats, metrics, benchmark comparison, CAGR |

| plotting | Candlestick (category x-axis), VectorBT plots, custom Plotly |

| indian-market-costs | Indian market fee model by segment |

| us-market-costs | US market fee model (stocks, options, futures) |

| crypto-market-costs | Crypto fee model (spot, USDT-M, COIN-M futures) |

| futures-backtesting | Lot sizes (SEBI revised Dec 2025), value sizing |

| long-short-trading | Simultaneous long/short, direction comparison |

| duckdb-data | DuckDB direct loading, Historify format, auto-detect, resampling, multi-symbol |

| csv-data-resampling | Loading CSV, resampling with Indian market alignment |

| walk-forward | Walk-forward analysis, WFE ratio |

| robustness-testing | Monte Carlo, noise test, parameter sensitivity, delay test |

| pitfalls | Common mistakes and checklist before going live |

| strategy-catalog | Strategy reference with code snippets |

| openstatz-tearsheet | OpenStatz interactive offline dashboard, metrics, Monte Carlo (replaces QuantStats) |

Strategy Templates (in rules/assets/)

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

| Template | Path | Description |

|----------|------|-------------|

| EMA Crossover | assets/ema_crossover/backtest.py | EMA 10/20 crossover |

| RSI | assets/rsi/backtest.py | RSI(14) oversold/overbought |

| Donchian | assets/donchian/backtest.py | Donchian channel breakout |

| Supertrend | assets/supertrend/backtest.py | Supertrend with intraday sessions |

| MACD | assets/macd/backtest.py | MACD signal-candle breakout |

| SDA2 | assets/sda2/backtest.py | SDA2 trend following |

| Momentum | assets/momentum/backtest.py | Double momentum (MOM + MOM-of-MOM) |

| Dual Momentum | assets/dual_momentum/backtest.py | Quarterly ETF rotation |

| Buy & Hold | assets/buy_hold/backtest.py | Static multi-asset allocation |

| RSI Accumulation | assets/rsi_accumulation/backtest.py | Weekly RSI slab-wise accumulation |

| Walk-Forward | assets/walk_forward/template.py | Walk-forward analysis template |

| Realistic Costs | assets/realistic_costs/template.py | Transaction cost impact comparison |

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:
    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 (OpenAlgo ta - default indicator library) ---
ema_fast = ta.ema(close, 10)
ema_slow = ta.ema(close, 20)

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 vectorbt as vbt

try:
    # Default: OpenAlgo ta for both indicators and signal cleaning
    from openalgo import ta
    exrem = ta.exrem
    ema = ta.ema
except ImportError:
    # Fallback ONLY when the openalgo package itself is not installed
    # (standalone DuckDB with no OpenAlgo). Use TA-Lib for indicators
    # and this inline exrem() replacement for signal cleaning.
    import talib as tl

    def ema(data, period):
        return pd.Series(tl.EMA(data.values, timeperiod=period), index=data.index)

    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, but use the ema()/exrem() resolved above) ---

If the user explicitly asks for TA-Lib, skip the try/except above and import talib as tl directly instead - the exrem fallback is only for when openalgo itself is unavailable.