Trading Algorithmically

Algo Trading

Build algorithmic trading strategies with backtesting, technical indicators, and fundamental data

Build production-ready trading systems with proper backtesting, risk management, and data integration.

Process

1. Review the task requirements.
2. Apply the skill's methodology.
3. Validate the output against the defined criteria.

Step 1: Data Acquisition

import yfinance as yf

# Single asset
df = yf.download("SPY", start="2020-01-01", end="2024-01-01")
df.columns = [c.lower() for c in df.columns]

# Multiple assets
data = yf.download(["SPY", "QQQ", "IWM"], start="2020-01-01")

Step 2: Calculate Technical Indicators

import pandas_ta as ta

# Add indicators
df['sma_20'] = ta.sma(df['close'], length=20)
df['rsi'] = ta.rsi(df['close'], length=14)
df['macd'] = ta.macd(df['close'])['MACD_12_26_9']
df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)
df['bbands'] = ta.bbands(df['close'], length=20, std=2)

Step 3: Generate Signals

# Example: MA Crossover
fast_ma = df['close'].rolling(10).mean()
slow_ma = df['close'].rolling(50).mean()

signals = pd.Series(0, index=df.index)
signals[fast_ma > slow_ma] = 1   # Long
signals[fast_ma < slow_ma] = -1  # Short

Step 4: Backtest

VectorBT (fast, vectorized):

import vectorbt as vbt

entries = signals.diff() == 1
exits = signals.diff() == -1

portfolio = vbt.Portfolio.from_signals(
    df['close'],
    entries,
    exits,
    init_cash=100_000,
    fees=0.001
)
print(portfolio.stats())

Backtrader (object-oriented):

import backtrader as bt

class MyStrategy(bt.Strategy):
    def __init__(self):
        self.sma = bt.indicators.SMA(period=20)

    def next(self):
        if self.data.close > self.sma:
            self.buy()
        elif self.data.close < self.sma:
            self.close()

Step 5: Fetch Fundamental Data

ticker = yf.Ticker("AAPL")

# Financial statements
income_stmt = ticker.income_stmt
balance_sheet = ticker.balance_sheet
cash_flow = ticker.cashflow

# Key ratios from info
info = ticker.info
pe_ratio = info.get('trailingPE')
roe = info.get('returnOnEquity')
debt_to_equity = info.get('debtToEquity')

Step 6: Risk Metrics

from knowledge.quantitative-finance import sharpe_ratio, max_drawdown

returns = df['close'].pct_change()
sharpe = np.sqrt(252) * returns.mean() / returns.std()
max_dd = (df['close'] / df['close'].expanding().max() - 1).min()

What Gets Created

| File | Purpose | ||| | strategies/ | Strategy implementations | | indicators/ | Custom indicator calculations | | data/ | Data fetching and caching | | backtest/ | Backtesting engine and reports | | risk/ | Position sizing and risk management |

Strategy Categories

Key Technical Indicators

Fundamental Data Sources

| Source | Data Type | Cost | |--|--|| | yfinance | OHLCV, Financials | Free | | Alpha Vantage | OHLCV, Fundamentals | Free tier | | SEC EDGAR | Filings | Free | | Polygon.io | Real-time | $29+/mo |

Backtesting Best Practices

1. Walk-forward validation - Prevent overfitting
2. Include transaction costs - Realistic P&L
3. Out-of-sample testing - Validate on unseen data
4. Monte Carlo simulation - Assess robustness
5. Parameter stability - Avoid curve fitting

Anti-Patterns to Avoid

| Anti-Pattern | Problem | Solution | |--||-| | Curve fitting | Fails live | Walk-forward validation | | Survivorship bias | Overstated returns | Point-in-time data | | Look-ahead bias | Unrealistic backtest | Strict data alignment | | Ignoring costs | Unprofitable live | Include slippage + commissions |

Fallback Procedures

Related Artifacts

• Knowledge: {directories.knowledge}/trading-patterns.json
• Knowledge: {directories.knowledge}/quantitative-finance.json
• Knowledge: {directories.knowledge}/risk-management.json
• Templates: {directories.templates}/trading/
• Blueprint: {directories.blueprints}/quantitative-trading/

When to Use

This skill should be used when strict adherence to the defined process is required.

Prerequisites

• Basic understanding of the agent factory context.
• Access to the necessary tools and resources.

Best Practices

• Always follow the established guidelines.
• Document any deviations or exceptions.
• Regularly review and update the skill documentation.