Quant Analyst

Professional quantitative trading system for cryptocurrency - backtesting, paper trading, live trading, and strategy optimization

Security Scan

The skill's high-level purpose (crypto quant trading) matches most instructions, but the package contains unrelated instructions (a separate video-generator SKILL file), it does not declare environment variables that its runtime examples require, and it instructs cloning and running external GitHub projects—these inconsistencies warrant caution.

Purpose & Capability

The primary SKILL.md and SKILL-ZH.md describe a quantitative trading tool and instruct cloning https://github.com/ZhenRobotics/openclaw-quant and installing Python deps—this is coherent with the stated purpose. However, SKILL-EN.md is for a completely different 'video-generator' skill (different repo, different env var: OPENAI_API_KEY) bundled into the same package. That file is unrelated to quant-analyst and suggests a packaging/misconfiguration or intentional bundling of unrelated capabilities.

Instruction Scope

The runtime instructions tell the agent/user to git clone external repositories and run project scripts (pip install -r requirements.txt, run python -m openclaw_quant, and in the English file run ~/openclaw-video/generate-for-openclaw.sh). Those commands will fetch and execute third-party code on the user's machine. The SKILL.md examples reference reading env vars (BINANCE_API_KEY/SECRET and use of os.getenv) even though the declared skill metadata lists no required env vars. The presence of unrelated instructions (video-generator) increases risk that the agent may run commands outside the intended domain.

Install Mechanism

No formal install spec is declared (instruction-only), which is lower baseline risk for the platform, but the instructions require cloning GitHub repos and running pip/npm installs. The repos referenced are hosted on GitHub (reasonable), but cloning and running install scripts pulls arbitrary code from an external source into the user's environment and may execute scripts that are not pre-vetted. The package contains two different repo targets (openclaw-quant and openclaw-video).

Credentials

Declared metadata lists no required env vars, but the instructions and examples explicitly advise setting BINANCE_API_KEY and BINANCE_API_SECRET (for live trading) and SKILL-EN.md requests OPENAI_API_KEY. This mismatch is a red flag: the skill will operate with credentials but does not declare them, so a user or automated installer may not be warned. Live trading also implies use of exchange API keys—users must ensure keys are limited (no withdrawal) and prefer testnet keys for initial use.

Persistence & Privilege

The skill does not request always:true, does not declare special config paths, and is install-free at platform level. It can be invoked autonomously (platform default) and contains AUTO-TRIGGER keywords, which is expected for agent-facing skills. There is no evidence the skill attempts to modify other skills or system-wide settings.

Scan Findings in Context

[no_regex_findings] expected: The static regex scanner had no findings because this skill is instruction-only and contains no executable code files for the scanner to analyze. That absence is not evidence of safety—review of the remote repositories the instructions clone is required.

What to consider before installing

Proceed cautiously. The quant-analyst instructions themselves are consistent with a trading tool, but: - There is an unrelated SKILL-EN.md (video-generator) bundled in the same package — ask the publisher why unrelated skill docs are included. - The skill's metadata declares no required env vars but the instructions expect BINANCE_API_KEY/SECRET (and the English file expects OPENAI_API_KEY). Treat this as a mismatch and assume the skill will ask for credentials. - Before running any git clone / pip install or executing scripts from the repos, manually inspect the GitHub repositories (openclaw-quant and openclaw-video) and review scripts such as generate-for-openclaw.sh and any startup hooks. Check recent commits, contributors, and issues. - Never supply exchange API keys with withdrawal permissions. Use read-only or testnet API keys and start with paper trading only. - Run installations and live tests in an isolated environment (VM or container) until you verify the code. - Prefer skills with a trustworthy homepage or known maintainer. If you can't verify the upstream repositories or get a clear explanation for the bundled video doc, consider not installing.

Quantitative Trading Skill

Professional quantitative trading system designed for cryptocurrency markets, featuring backtesting, paper trading, live trading, and advanced strategy optimization.

Installation

Step 1: Install the Skill

clawhub install quant-analyst

Step 2: Clone & Setup the Project

# Clone repository
git clone https://github.com/ZhenRobotics/openclaw-quant.git ~/openclaw-quant
cd ~/openclaw-quant

# Install dependencies
pip install -r requirements.txt

# Set API keys (optional for backtesting)
export BINANCE_API_KEY="your-key"
export BINANCE_API_SECRET="your-secret"

Step 3: Verify Installation

cd ~/openclaw-quant
python -m openclaw_quant --help

---

Core Features

• Backtesting Engine: Test strategies on historical data with realistic simulation
• Paper Trading: Real-time simulation with live market data
• Live Trading: Automated trading on real exchanges (Binance, OKX, etc.)
• Strategy Optimization: Parameter optimization using Bayesian methods
• Technical Indicators: 50+ built-in indicators (MA, RSI, MACD, Bollinger, etc.)
• Risk Management: Position sizing, stop-loss, take-profit
• Performance Analytics: Sharpe ratio, max drawdown, win rate, profit factor
• Multi-Exchange Support: Via ccxt library (100+ exchanges)

---

When to Use This Skill

AUTO-TRIGGER when user's message contains:

• Keywords: backtest, trading strategy, quant, cryptocurrency trading, optimize strategy
• Requests like: "test this trading strategy", "backtest MA cross", "paper trade bitcoin"
• Strategy descriptions: "buy when RSI < 30, sell when RSI > 70"
• Performance questions: "what's the sharpe ratio", "calculate max drawdown"

TRIGGER EXAMPLES:

• "Backtest a moving average crossover strategy on Bitcoin"
• "Optimize RSI parameters for the last 6 months"
• "Start paper trading with my momentum strategy"
• "What's the win rate of this strategy?"

DO NOT USE when:

• Only portfolio tracking (use portfolio-tracker skill)
• Only price alerts (use price-alert skill)
• General crypto news or information

---

Quick Start Examples

Example 1: Simple Moving Average Strategy

from openclaw_quant import Strategy, Backtest

class MAStrategy(Strategy):
    # Parameters (can be optimized)
    fast_period = 10
    slow_period = 30

    def init(self):
        # Vectorized indicator calculation
        self.fast_ma = self.I(SMA, self.data.Close, self.fast_period)
        self.slow_ma = self.I(SMA, self.data.Close, self.slow_period)

    def next(self):
        # Event-driven logic
        if self.fast_ma[-1] > self.slow_ma[-1]:
            if not self.position:
                self.buy()
        else:
            if self.position:
                self.sell()

# Backtest
bt = Backtest(MAStrategy, data, cash=10000, commission=0.001)
result = bt.run()
print(result)
result.plot()

Example 2: RSI Mean Reversion

class RSIStrategy(Strategy):
    rsi_period = 14
    oversold = 30
    overbought = 70

    def init(self):
        self.rsi = self.I(RSI, self.data.Close, self.rsi_period)

    def next(self):
        if self.rsi[-1] < self.oversold:
            if not self.position:
                self.buy()
        elif self.rsi[-1] > self.overbought:
            if self.position:
                self.sell()

bt = Backtest(RSIStrategy, data, cash=10000)
result = bt.run()

Example 3: Parameter Optimization

# Optimize parameters automatically
result = bt.optimize(
    fast_period=range(5, 20, 2),
    slow_period=range(20, 60, 5),
    maximize='sharpe_ratio'  # or 'total_return', 'profit_factor'
)

print(f"Best parameters: {result.best_params}")
print(f"Sharpe Ratio: {result.sharpe_ratio:.2f}")

Example 4: Paper Trading

from openclaw_quant import LiveTrading

# Paper trading with real-time data
live = LiveTrading(
    strategy=MAStrategy,
    exchange='binance',
    symbol='BTC/USDT',
    paper=True  # Simulation mode
)

live.run()

Example 5: Live Trading

# Real trading (use with caution!)
live = LiveTrading(
    strategy=MAStrategy,
    exchange='binance',
    symbol='BTC/USDT',
    paper=False,  # Real mode
    api_key=os.getenv('BINANCE_API_KEY'),
    api_secret=os.getenv('BINANCE_API_SECRET')
)

live.run()

---

Agent Usage Guide

Command-Line Interface

# Backtest a strategy
openclaw-quant backtest --strategy ma_cross --symbol BTCUSDT --days 365

# Optimize parameters
openclaw-quant optimize --strategy rsi --symbol ETHUSDT --metric sharpe_ratio

# Paper trading
openclaw-quant paper --strategy ma_cross --symbol BTCUSDT

# Live trading
openclaw-quant live --strategy ma_cross --symbol BTCUSDT --confirm

# View results
openclaw-quant results --backtest-id abc123

Natural Language (via OpenClaw Agent)

Agent can understand requests like:

• "Backtest a moving average crossover on Bitcoin for the last year"
• "Optimize RSI parameters to maximize Sharpe ratio"
• "Start paper trading with 10k USDT using my momentum strategy"
• "Show me the performance metrics of the last backtest"
• "What's the max drawdown of my live trading account?"

---

Performance Metrics

The system calculates comprehensive performance metrics:

Metric	Description
Total Return	Overall profit/loss percentage
Annualized Return	Return extrapolated to one year
Sharpe Ratio	Risk-adjusted return (higher is better)
Sortino Ratio	Downside risk-adjusted return
Max Drawdown	Largest peak-to-trough decline
Win Rate	Percentage of profitable trades
Profit Factor	Gross profit / Gross loss
Calmar Ratio	Return / Max drawdown
Average Win/Loss	Mean profit/loss per trade
Expectancy	Expected value per trade

---

Built-in Strategies

The system includes several ready-to-use strategies:

1. MA Cross: Moving average crossover
2. RSI Mean Reversion: Buy oversold, sell overbought
3. MACD Momentum: MACD line and signal crossover
4. Bollinger Bounce: Trade Bollinger band touches
5. Breakout: Support/resistance breakouts
6. Grid Trading: Buy low, sell high in range
7. DCA (Dollar Cost Average): Regular accumulation
8. Mean Reversion: Statistical arbitrage

---

Technical Indicators

50+ indicators available via self.I() method:

Trend Indicators:

• SMA, EMA, WMA, DEMA, TEMA
• MACD, ADX, Aroon, Supertrend

Momentum Indicators:

• RSI, Stochastic, CCI, Williams %R
• ROC (Rate of Change), Momentum

Volatility Indicators:

• Bollinger Bands, ATR, Keltner Channels
• Standard Deviation, Historical Volatility

Volume Indicators:

• OBV, Volume SMA, MFI, VWAP
• Accumulation/Distribution, CMF

---

Risk Management

Built-in risk management features:

class MyStrategy(Strategy):
    def init(self):
        # Set risk parameters
        self.risk_per_trade = 0.02  # 2% of capital
        self.stop_loss = 0.05       # 5% stop loss
        self.take_profit = 0.10     # 10% take profit

        self.ma = self.I(SMA, self.data.Close, 20)

    def next(self):
        if self.ma[-1] > self.data.Close[-1]:
            if not self.position:
                # Calculate position size based on risk
                size = self.calculate_position_size(
                    risk=self.risk_per_trade,
                    stop_loss=self.stop_loss
                )
                self.buy(size=size)
                self.set_stop_loss(self.stop_loss)
                self.set_take_profit(self.take_profit)

---

Data Sources

Supports multiple data sources:

1. Exchange APIs: Binance, OKX, Bybit, etc. (via ccxt)
2. CSV Files: Load historical data from files
3. Database: PostgreSQL, SQLite for caching
4. Real-time WebSocket: Live market data

# Example: Load data from Binance
from openclaw_quant import DataFetcher

fetcher = DataFetcher('binance')
data = fetcher.fetch_candles(
    symbol='BTC/USDT',
    timeframe='1h',
    since='2023-01-01',
    limit=1000
)

---

Configuration

Example configuration file (config.yaml):

backtest:
  initial_capital: 10000
  commission: 0.001  # 0.1%
  slippage: 0.0005   # 0.05%

strategy:
  name: ma_cross
  parameters:
    fast_period: 10
    slow_period: 30

exchange:
  name: binance
  testnet: false

risk:
  max_position_size: 0.1  # 10% of capital
  max_drawdown: 0.2       # Stop if 20% drawdown
  daily_loss_limit: 0.05  # Stop if 5% daily loss

notification:
  telegram:
    enabled: true
    bot_token: "your-token"
    chat_id: "your-chat-id"

---

Project Structure

openclaw-quant/
├── src/
│   ├── openclaw_quant/
│   │   ├── __init__.py
│   │   ├── strategy.py         # Strategy base class
│   │   ├── backtest.py         # Backtest engine
│   │   ├── live.py             # Live trading engine
│   │   ├── broker.py           # Order execution
│   │   ├── data.py             # Data fetching
│   │   ├── indicators.py       # Technical indicators
│   │   ├── optimizer.py        # Parameter optimization
│   │   ├── metrics.py          # Performance metrics
│   │   └── risk.py             # Risk management
│   └── strategies/
│       ├── ma_cross.py
│       ├── rsi.py
│       └── ...
├── examples/
│   ├── backtest_example.py
│   ├── optimization_example.py
│   └── paper_trading_example.py
├── tests/
├── docs/
├── requirements.txt
└── README.md

---

Requirements

Python >= 3.9
pandas >= 2.0.0
numpy >= 1.24.0
ccxt >= 4.0.0
optuna >= 3.0.0
matplotlib >= 3.7.0
pydantic >= 2.0.0

---

Troubleshooting

Issue 1: API Connection Error

Error: ccxt.NetworkError or connection timeout

Solution:

# Check internet connection
# Verify API keys are correct
# Use testnet for testing:
exchange = ccxt.binance({'enableRateLimit': True, 'options': {'defaultType': 'future', 'testnet': True}})

Issue 2: Insufficient Data

Error: Not enough candles for strategy

Solution:

# Increase warmup period
bt = Backtest(strategy, data, warmup=100)  # Skip first 100 candles

# Or fetch more historical data
data = fetcher.fetch_candles(symbol='BTC/USDT', limit=5000)

Issue 3: Optimization Takes Too Long

Solution:

# Reduce search space
result = bt.optimize(
    fast_period=range(5, 20, 5),  # Larger step
    slow_period=range(20, 60, 10),
    max_tries=50  # Limit iterations
)

---

Safety Guidelines

Backtesting

• Always test on historical data first
• Use realistic commission and slippage
• Beware of overfitting
• Walk-forward validation recommended

Paper Trading

• Test for at least 1-2 weeks
• Monitor slippage and fills
• Check strategy behavior in different market conditions

Live Trading

• Start with small capital
• Set strict risk limits
• Monitor continuously for first week
• Have emergency stop mechanism
• Never risk more than you can afford to lose

---

Performance Tips

1. Vectorization: Use self.I() for indicators (computed once)
2. Data Caching: Cache historical data to avoid repeated API calls
3. Optimization: Use Bayesian optimization, not grid search
4. Parallel Backtesting: Test multiple symbols simultaneously
5. WebSocket: Use WebSocket for real-time data (faster than REST)

---

Documentation

• GitHub: https://github.com/ZhenRobotics/openclaw-quant
• Quick Start: ~/openclaw-quant/QUICKSTART.md
• API Reference: ~/openclaw-quant/docs/API.md
• Strategy Guide: ~/openclaw-quant/docs/STRATEGIES.md
• Full README: ~/openclaw-quant/README.md

---

Roadmap

Version 0.1.0 (Current)

• Basic backtesting engine
• Simple strategies (MA, RSI)
• Binance support

Version 0.2.0

• Paper trading
• Parameter optimization
• More indicators

Version 0.3.0

• Live trading
• Multi-exchange support
• Advanced risk management

Version 1.0.0

• Production-ready
• Web dashboard
• Strategy marketplace

---

Cost

• Development: Free and open source (MIT License)
• Data: Free (using exchange APIs)
• Trading Fees: Varies by exchange (typically 0.1%)
• API Costs: Free tier available on most exchanges

---

License

MIT License - Free for personal and commercial use

---

Support

• Issues: https://github.com/ZhenRobotics/openclaw-quant/issues
• Discussions: https://github.com/ZhenRobotics/openclaw-quant/discussions
• ClawHub: https://clawhub.ai/ZhenStaff/openclaw-quant

---

Agent Behavior Guidelines

When using this skill, agents should:

DO:

• Check if project is installed before use
• Warn about risks in live trading mode
• Validate strategy parameters
• Show clear performance metrics
• Explain optimization results

DON'T:

• Execute live trades without explicit confirmation
• Recommend specific trading strategies
• Guarantee profits or returns
• Ignore risk warnings
• Make financial advice

---

Status: Under Development (Alpha)

Author: @ZhenStaff

Last Updated: 2026-03-05