ComeOnOliver Algorithmic Trading
Overview
Build, backtest, and deploy algorithmic trading strategies. Cover market data ingestion, strategy development, backtesting with realistic assumptions, risk management, and live execution.
Instructions
Architecture
Market Data → Data Pipeline → Strategy Engine → Risk Manager → Order Executor → Exchange
↑ ↓ ↓
└──────────── Performance Monitor ←────── Position Tracker ←────────┘
Data pipeline
# data_fetcher.py — Fetch historical OHLCV data
import ccxt
import yfinance as yf
def fetch_crypto_ohlcv(symbol: str, timeframe: str, since: str) -> list:
"""Fetch candles from Binance. Returns [timestamp, O, H, L, C, V]."""
exchange = ccxt.binance()
ohlcv = exchange.fetch_ohlcv(symbol, timeframe,
since=exchange.parse8601(since), limit=1000)
return ohlcv
def fetch_stock_data(ticker: str, period: str = '2y', interval: str = '1d'):
"""Fetch stock OHLCV from Yahoo Finance."""
return yf.download(ticker, period=period, interval=interval)
For real-time data, use WebSocket feeds (ccxt.pro):
async def stream_orderbook(symbol: str = 'BTC/USDT'):
exchange = ccxtpro.binance()
while True:
ob = await exchange.watch_order_book(symbol)
spread = (ob['asks'][0][0] - ob['bids'][0][0]) / ob['bids'][0][0] * 100
print(f"{symbol} Spread: {spread:.4f}%")
Strategy development
Common types: Momentum/trend following (MAs, RSI, MACD), mean reversion (z-scores, Bollinger Bands), statistical arbitrage (pairs trading, cross-exchange), market making (spread capture).
# strategy.py — Dual Moving Average Crossover with RSI filter
import pandas as pd
def calculate_signals(df: pd.DataFrame, fast=20, slow=50, rsi_period=14):
"""Generate trading signals from OHLCV data."""
df['sma_fast'] = df['close'].rolling(window=fast).mean()
df['sma_slow'] = df['close'].rolling(window=slow).mean()
delta = df['close'].diff()
gain = delta.where(delta > 0, 0).rolling(window=rsi_period).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=rsi_period).mean()
df['rsi'] = 100 - (100 / (1 + gain / loss))
df['signal'] = 0
df.loc[(df['sma_fast'] > df['sma_slow']) &
(df['sma_fast'].shift(1) <= df['sma_slow'].shift(1)) &
(df['rsi'] < 70), 'signal'] = 1 # Buy
df.loc[(df['sma_fast'] < df['sma_slow']) &
(df['sma_fast'].shift(1) >= df['sma_slow'].shift(1)) &
(df['rsi'] > 30), 'signal'] = -1 # Sell
return df
Backtesting
A backtest must model real conditions — fees, slippage, and stop-losses:
# backtester.py — Vectorized backtester with realistic assumptions
class BacktestConfig:
commission: float = 0.001 # 0.1% per trade
slippage: float = 0.0005 # 0.05% estimated
initial_capital: float = 10000
position_size: float = 0.1 # 10% of portfolio per trade
stop_loss: float = 0.02 # 2% stop-loss
take_profit: float = 0.06 # 6% take-profit (3:1 R/R)
Key metrics:
RETURNS: Total Return, Annualized Return, Alpha (vs buy-and-hold)
RISK: Max Drawdown (<15%), Sharpe Ratio (>1.0 good, >2.0 excellent), Sortino, Calmar
TRADING: Win Rate (>45% for trend, >55% for mean reversion), Profit Factor (>1.5)
Risk management
Position sizing: Fixed fractional (X% per trade) or Kelly Criterion (f = (bp - q) / b, use half-Kelly for safety).
# risk_manager.py — Enforce risk rules before every order
RISK_RULES = {
'max_position_pct': 0.10, # Max 10% per position
'max_portfolio_risk': 0.02, # Max 2% risk per trade
'max_daily_loss': 0.05, # Stop after 5% daily loss
'max_drawdown': 0.15, # Stop after 15% drawdown
'max_correlation': 0.7, # No correlated positions
}
Live execution
# executor.py — Live orders with safety checks
def place_order(exchange, symbol, side, amount, order_type='limit', price=None):
ticker = exchange.fetch_ticker(symbol)
if order_type == 'market':
spread = (ticker['ask'] - ticker['bid']) / ticker['bid'] * 100
if spread > 0.5:
raise ValueError(f"Spread too wide: {spread:.2f}%")
if order_type == 'limit' and price:
deviation = abs(price - ticker['last']) / ticker['last'] * 100
if deviation > 2.0:
raise ValueError(f"Limit price {deviation:.1f}% from market")
return exchange.create_order(symbol, order_type, side, amount, price)
Always paper trade first: Alpaca (built-in), Binance Testnet, Interactive Brokers (TWS). Run 1 month or 100 trades minimum before live capital.
Examples
Build a crypto momentum strategy
Build a momentum trading strategy for BTC/USDT on Binance. Use EMA crossover (12/26) with volume confirmation and RSI filter. Backtest on 2 years of hourly data with realistic fees (0.1% taker), calculate Sharpe ratio and max drawdown, and compare against buy-and-hold. Include stop-loss at 2% and take-profit at 6%.
Create a pairs trading system
Build a statistical arbitrage system that trades correlated stock pairs. Use cointegration testing to find pairs from the S&P 500, implement a z-score mean reversion strategy, and backtest with transaction costs. Include the pair selection process, entry/exit rules, and risk management.
Set up a live trading bot with risk management
Deploy a live trading bot on Alpaca for US stocks. It should run a simple momentum strategy on a universe of 20 liquid ETFs, execute via limit orders, enforce position size limits (max 10% per holding), and stop trading if daily loss exceeds 2%. Include monitoring, logging, and alerting via Telegram.
Guidelines
- Always backtest with realistic fees, slippage, and stop-losses — unrealistic backtests are worthless
- Never skip paper trading — run for at least 1 month or 100 trades before deploying live capital
- Use half-Kelly criterion for position sizing to add a safety margin
- Implement hard daily loss limits and max drawdown circuit breakers
- Check spread width before market orders — never market-order into thin books
- Compare every strategy against buy-and-hold; if it underperforms, it's not worth the complexity
- Log every order, signal, and risk check for post-mortem analysis