vectorbt-expert

name vectorbt-expert description VectorBT backtesting expert. Use when user asks to backtest strategies, create entry/exit signals, analyze portfolio performance, optimize parameters, fetch historical data, use VectorBT/vectorbt, compare strategies, position sizing, equity curves, drawdown charts, or trade analysis. Also triggers for openalgo.ta helpers (exrem, crossover, crossunder, flip, donchian, supertrend). user-invocable false 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. Whole shares : Always set min_size=1, size_granularity=1 for equities. 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 : .2 f} %" , f" {pf.sharpe_ratio(): .2 f} " , f" {pf.sortino_ratio(): .2 f} " , f" {pf.max_drawdown() * 100 : .2 f} %" , f" {pf.trades.win_rate() * 100 : .1 f} %" , f" {pf.trades.count()} " , f" {pf.trades.profit_factor(): .2 f} " , ], f"Benchmark ( {BENCHMARK_SYMBOL} )" : [ f" {pf_bench.total_return() * 100 : .2 f} %" , f" {pf_bench.sharpe_ratio(): .2 f} " , f" {pf_bench.sortino_ratio(): .2 f} " , f" {pf_bench.max_drawdown() * 100 : .2 f} %" , "-" , "-" , "-" , ], }, 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 : .2 f} % vs NIFTY {pf_bench.total_return() * 100 : .2 f} %" ) print ( f"* Max Drawdown: {pf.max_drawdown() * 100 : .2 f} %" ) print ( f" -> On Rs {INIT_CASH:,} , worst temporary loss = Rs { abs (pf.max_drawdown()) * INIT_CASH:, .0 f} " ) # --- Plot --- fig = pf.plot(subplots=[ 'value' , 'underwater' , 'cum_returns' ], template= "plotly_dark" ) fig.show() # --- Export ---