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Agent skill

Vectorbt

by agiprolabs in agiprolabs/claude-trading-skills

High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics

MITAuto-check passedBusiness, Finance & HR

Install Vectorbt

skills CLI
$ npx skills add agiprolabs/claude-trading-skills --skill vectorbt -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install agiprolabs/claude-trading-skills vectorbt --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/vectorbt .claude/skills/vectorbt && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
vectorbt
GitHub stars
410
Token cost
~2.6k tokens
SKILL.md length
589 words
Files
5 (incl. scripts, references)
Skills in repo
68
Repo updated
First seen
Licence
MIT

At a glance

High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics

  • Works in 9 steps: Signals — Boolean Entry/Exit Arrays → Portfolio — The Backtesting Engine → Metrics — Built-in Performance Analysis → …
  • Tasks that involve Trading and backtesting
  • SKILL.md covers Overview, Installation, Core Concepts and Basic Workflow, plus 5 more sections
  • Runs Python scripts from its folder; calls uv

What it does

Vectorbt is an agent skill from agiprolabs/claude-trading-skills. High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api_guide.md`, `references/optimization_guide.md` and `scripts/backtest_example.py`).

It sits in Business, Finance & HR, covering Trading and backtesting, OKRs and executive reporting and DataFrames. It works with pandas and NumPy. The repository describes itself as: 68 trading, DeFi, and quantitative finance Agent Skills. Works with Claude Code, Cursor, Codex, Gemini CLI, and 30+ other tools. The licence is MIT.

When your agent uses it

  • Tasks that involve Trading and backtesting
  • Tasks that involve OKRs and executive reporting
  • Tasks that involve DataFrames

Example prompts

  • “/vectorbt”

Requirements

  • Python 3

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Signals — Boolean Entry/Exit Arrays
  2. Portfolio — The Backtesting Engine
  3. Metrics — Built-in Performance Analysis
  4. Parameter Optimization — Grid Search in Seconds
  5. Load OHLCV Data
  6. Compute Indicators
  7. Generate Entry/Exit Signals
  8. Run Backtest
  9. Analyze Results

What it can do on your machine

Read from SKILL.md and the folder at commit 981e1d7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Vectorbt loads about 2.6k tokens when it runs, and up to ~6.7k if it reads all its reference files. Until then it costs about 32 tokens; SKILL.md has 589 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~32
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from agiprolabs/claude-trading-skills at commit 981e1d7, republished under its MIT licence (© agiprolabs). 589 words, ~2,563 tokens.

Download SKILL.mdSave it as .claude/skills/vectorbt/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
vectorbt
description
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics

Vectorized Backtesting with vectorbt

Overview

vectorbt is a Python library for vectorized backtesting — running strategy simulations using NumPy/pandas array operations instead of bar-by-bar loops. This makes it 100–1000x faster than event-driven frameworks (backtrader, zipline), enabling parameter optimization across thousands of combinations in seconds.

Key strengths:

  • Blazing speed via NumPy vectorization
  • Built-in parameter grid search and optimization
  • 50+ built-in performance metrics (Sharpe, Sortino, Calmar, max drawdown, profit factor)
  • Rich plotting (equity curves, drawdowns, trade markers, heatmaps)
  • Native pandas integration — your data stays in DataFrames throughout

Installation

bash
uv pip install vectorbt pandas numpy

vectorbt pulls in pandas, NumPy, and Plotly automatically. For technical indicators, also install pandas-ta:

bash
uv pip install vectorbt pandas-ta

Core Concepts

1. Signals — Boolean Entry/Exit Arrays

Strategies in vectorbt are expressed as boolean pandas Series (or arrays) indicating where to enter and exit positions:

python
import vectorbt as vbt
import pandas as pd

# Entry: buy when fast EMA crosses above slow EMA
entries = fast_ema > slow_ema
# Exit: sell when fast EMA crosses below slow EMA
exits = fast_ema < slow_ema

vectorbt resolves conflicting signals automatically (you can't enter while already in a position).

2. Portfolio — The Backtesting Engine

vbt.Portfolio.from_signals() is the primary backtesting function. It takes price data and entry/exit signals, simulates trades, and computes performance:

python
pf = vbt.Portfolio.from_signals(
    close=close_prices,
    entries=entries,
    exits=exits,
    init_cash=10_000,
    fees=0.003,       # 0.3% per trade
    slippage=0.005,   # 0.5% slippage
    freq="1h",        # hourly data
)
3. Metrics — Built-in Performance Analysis
python
# Full stats summary
print(pf.stats())

# Individual metrics
print(f"Total Return: {pf.total_return():.2%}")
print(f"Sharpe Ratio: {pf.sharpe_ratio():.3f}")
print(f"Max Drawdown: {pf.max_drawdown():.2%}")
print(f"Win Rate:     {pf.trades.win_rate():.2%}")
4. Parameter Optimization — Grid Search in Seconds

Pass arrays instead of scalars to test many parameter combos simultaneously:

python
import numpy as np

fast_periods = np.arange(5, 25, 2)   # 10 values
slow_periods = np.arange(20, 60, 5)  # 8 values

fast_ma = vbt.MA.run(close, fast_periods, short_name="fast")
slow_ma = vbt.MA.run(close, slow_periods, short_name="slow")

# This creates 80 parameter combinations automatically
entries = fast_ma.ma_crossed_above(slow_ma)
exits = fast_ma.ma_crossed_below(slow_ma)

Basic Workflow

Step 1: Load OHLCV Data
python
import pandas as pd

# From CSV
df = pd.read_csv("ohlcv.csv", parse_dates=["timestamp"], index_col="timestamp")
close = df["close"]

# From Yahoo Finance (traditional markets)
btc = vbt.YFData.download("BTC-USD", start="2023-01-01", end="2025-01-01")
close = btc.get("Close")

For Solana tokens, fetch data via the birdeye-api skill and load into a DataFrame.

Step 2: Compute Indicators
python
import pandas_ta as ta

# Using pandas-ta (see pandas-ta skill)
df.ta.ema(length=12, append=True)
df.ta.ema(length=26, append=True)
df.ta.rsi(length=14, append=True)
df.ta.bbands(length=20, std=2, append=True)

# Or using vectorbt built-ins
rsi = vbt.RSI.run(close, window=14)
bbands = vbt.BBANDS.run(close, window=20, alpha=2)
Step 3: Generate Entry/Exit Signals
python
# EMA crossover
entries = df["EMA_12"] > df["EMA_26"]
exits = df["EMA_12"] < df["EMA_26"]

# RSI mean reversion
entries = rsi.rsi_below(30)
exits = rsi.rsi_above(70)
Step 4: Run Backtest
python
pf = vbt.Portfolio.from_signals(
    close=close,
    entries=entries,
    exits=exits,
    init_cash=10_000,
    fees=0.003,
    slippage=0.005,
    size=0.95,               # use 95% of available cash
    size_type="percent",
    freq="1h",
)
Step 5: Analyze Results
python
# Summary statistics
print(pf.stats())

# Trade-level analysis
trades = pf.trades.records_readable
print(f"\nTrade count: {len(trades)}")
print(f"Avg holding period: {trades['Duration'].mean()}")

# Equity curve
pf.plot().show()

# Drawdown chart
pf.drawdowns.plot().show()

Key Portfolio Parameters

ParameterDescriptionExample
closePrice series (pd.Series or DataFrame)df["close"]
entriesBoolean entry signalsfast > slow
exitsBoolean exit signalsfast < slow
init_cashStarting capital10_000
feesFee per trade (fraction)0.003 (0.3%)
slippageSlippage per trade (fraction)0.005 (0.5%)
sizePosition size0.95
size_typeHow to interpret size"percent", "amount", "value"
freqData frequency"1h", "4h", "1d"
directionTrade direction"both", "longonly", "shortonly"
accumulateAllow adding to positionsFalse
sl_stopStop-loss level (fraction)0.05 (5%)
tp_stopTake-profit level (fraction)0.10 (10%)

Performance Metrics

Returns
  • total_return() — cumulative return over the period
  • annualized_return() — annualized compound return
  • daily_returns() — Series of daily returns
Risk
  • max_drawdown() — maximum peak-to-trough decline
  • annualized_volatility() — annualized standard deviation of returns
  • value_at_risk() — VaR at specified confidence level
Risk-Adjusted
  • sharpe_ratio() — excess return per unit volatility
  • sortino_ratio() — excess return per unit downside deviation
  • calmar_ratio() — annualized return / max drawdown
  • omega_ratio() — probability-weighted gain/loss ratio
Show full SKILL.md (225 more words)Show less
Trade Statistics
  • trades.win_rate() — fraction of profitable trades
  • trades.profit_factor() — gross profit / gross loss
  • trades.expectancy() — average P&L per trade
  • trades.avg_winning_trade() — mean profit on winners
  • trades.avg_losing_trade() — mean loss on losers
  • trades.count() — total number of completed trades

Parameter Optimization

python
fast_windows = [5, 8, 12, 15, 20]
slow_windows = [20, 26, 30, 40, 50]

# Run all 25 combos at once
fast_ma = vbt.MA.run(close, fast_windows, short_name="fast")
slow_ma = vbt.MA.run(close, slow_windows, short_name="slow")

entries = fast_ma.ma_crossed_above(slow_ma)
exits = fast_ma.ma_crossed_below(slow_ma)

pf = vbt.Portfolio.from_signals(close, entries, exits, fees=0.003)

# Find best params by Sharpe
sharpe = pf.sharpe_ratio()
best_idx = sharpe.idxmax()
print(f"Best params: {best_idx}, Sharpe: {sharpe[best_idx]:.3f}")
Walk-Forward Validation

Always validate optimized parameters on out-of-sample data:

python
# Split: 70% train, 30% test
split_idx = int(len(close) * 0.7)
train_close = close.iloc[:split_idx]
test_close = close.iloc[split_idx:]

# Optimize on training data
# ... (run grid search on train_close)

# Validate best params on test data
# ... (run single backtest on test_close with best params)

See references/optimization_guide.md for detailed walk-forward methodology and overfitting prevention.

Crypto-Specific Considerations

24/7 Markets

Crypto markets never close. Use hourly or minute-based frequencies, not business-day frequencies:

python
# Correct for crypto
pf = vbt.Portfolio.from_signals(close, entries, exits, freq="1h")

# Wrong — business days assume market closures
# pf = vbt.Portfolio.from_signals(close, entries, exits, freq="1B")
Realistic Fees

DEX swaps on Solana typically cost 0.25–1% including AMM fees. CEX spot fees are 0.05–0.1%.

python
# Solana DEX (conservative)
pf = vbt.Portfolio.from_signals(close, entries, exits, fees=0.005)

# CEX spot
pf = vbt.Portfolio.from_signals(close, entries, exits, fees=0.001)
Slippage

Low-liquidity tokens can have 1–5% slippage. Always model this:

python
# High-liquidity (SOL, ETH): 0.1–0.5%
pf = vbt.Portfolio.from_signals(close, entries, exits, slippage=0.003)

# Low-liquidity memecoins: 1–3%
pf = vbt.Portfolio.from_signals(close, entries, exits, slippage=0.02)
Short History

Many tokens have less than 1 year of data. Be cautious about annualizing metrics from short samples.

Common Strategy Patterns

EMA Crossover
python
fast = vbt.MA.run(close, 12, short_name="fast")
slow = vbt.MA.run(close, 26, short_name="slow")
entries = fast.ma_crossed_above(slow)
exits = fast.ma_crossed_below(slow)
RSI Mean Reversion
python
rsi = vbt.RSI.run(close, 14)
entries = rsi.rsi_crossed_below(30)
exits = rsi.rsi_crossed_above(70)
Bollinger Band Breakout
python
bb = vbt.BBANDS.run(close, window=20, alpha=2)
entries = close > bb.upper
exits = close < bb.lower
Stop-Loss and Take-Profit
python
pf = vbt.Portfolio.from_signals(
    close, entries, exits,
    sl_stop=0.05,    # 5% stop-loss
    tp_stop=0.10,    # 10% take-profit
)
  • pandas-ta — Technical indicator computation (feeds vectorbt signals)
  • birdeye-api — Fetch Solana token OHLCV data for backtesting
  • trading-visualization — Advanced chart generation for backtest results
  • portfolio-analytics — Deeper portfolio-level risk/return analysis
  • position-sizing — Optimal position sizing methodology
  • risk-management — Portfolio-level risk guardrails
  • regime-detection — Market regime awareness for adaptive strategies

Files

References
  • references/api_guide.md — Complete vectorbt API reference for Portfolio, indicators, plotting, and data loading
  • references/optimization_guide.md — Grid search, walk-forward validation, overfitting prevention, and optimization best practices
Scripts
  • scripts/backtest_example.py — Three-strategy backtest comparison using synthetic data (EMA crossover, RSI mean reversion, Bollinger breakout)
  • scripts/parameter_sweep.py — EMA crossover parameter grid search with walk-forward validation

© agiprolabs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in skills/vectorbt of agiprolabs/claude-trading-skills.

  • SKILL.md
  • references/api_guide.md
  • references/optimization_guide.md
  • scripts/backtest_example.py
  • scripts/parameter_sweep.py

Open the folder on GitHubat commit 981e1d7

Compare with similar skills

Vectorbt next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Vectorbt compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Vectorbt this skillagiprolabs/claude-trading-skills410—~2.6kAutomated safety check: PassMIT
Candlestick Pattern SignalsHKUDS/Vibe-Trading35k—~468Automated safety check: PassMIT
Tushare Datazillionare/zillionare3182 repos~2.3kAutomated safety check: PassNone
Quant Blog Writingzillionare/zillionare318—~895Automated safety check: PassNone
Quant Analystmajiayu000/claude-skill-registry6661 repos~964Automated safety check: PassMIT
Elliott Wave Signal EngineHKUDS/Vibe-Trading35k—~482Automated safety check: PassMIT

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Works with

Categories

Questions about Vectorbt

What does Vectorbt do?

High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics. Vectorbt is an agent skill from agiprolabs/claude-trading-skills.

When should I use Vectorbt?

Vectorbt fits situations like: tasks that involve Trading and backtesting; tasks that involve OKRs and executive reporting; tasks that involve DataFrames.

How do I install Vectorbt in Claude Code?

Run `npx skills add agiprolabs/claude-trading-skills --skill vectorbt -a claude-code`. Or copy the skill folder (skills/vectorbt in agiprolabs/claude-trading-skills) into .claude/skills/vectorbt in your project. Claude Code loads it when a task matches its description.

How do I install Vectorbt in Codex?

Run `npx skills add agiprolabs/claude-trading-skills --skill vectorbt -a codex`. Or copy the skill folder (skills/vectorbt in agiprolabs/claude-trading-skills) into .agents/skills/vectorbt in your project. Codex loads it when a task matches its description.

Can I use Vectorbt in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agiprolabs/claude-trading-skills --skill vectorbt -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vectorbt, .gemini/skills/vectorbt, .github/skills/vectorbt and .opencode/skills/vectorbt in your project.

What does Vectorbt need to run?

Going by SKILL.md and its folder, Vectorbt needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Vectorbt access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Vectorbt safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Vectorbt use?

Vectorbt is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Vectorbt use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.1k tokens, read only when the agent opens those files.

What are the alternatives to Vectorbt?

Skills that share tags, products or a category with Vectorbt: Candlestick Pattern Signals (HKUDS/Vibe-Trading, 35k stars), Tushare Data (zillionare/zillionare, 318 stars), Quant Blog Writing (zillionare/zillionare, 318 stars) and Quant Analyst (majiayu000/claude-skill-registry, 666 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Vectorbt?

agiprolabs (a GitHub user) maintains it in agiprolabs/claude-trading-skills, which has 410 GitHub stars. The repository holds 68 skills in this directory. The repository was last updated on September 3, 2026.

Source: agiprolabs/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.