Algo Trading & Quant

Python automation, backtesting frameworks, execution systems, and quantitative research workflows for systematic trading.

What "algo trading" actually involves

It's not just "let the computer trade." A complete algo system handles every step a discretionary trader would, but in code:

• Implementing technical indicators — RSI, MACD, ATR, Stochastic, EMA, Supertrend, Bollinger Bands. Either via TA-Lib (C-fast) or pure pandas (portable).
• Detecting candlestick patterns — Doji, Hammer, Engulfing, Morning/Evening Star, Marubozu, Shooting Star. TA-Lib has 60+ patterns built-in.
• Calculating stop-loss dynamically — `stop = entry − N × ATR(14)` is the workhorse. Optional "swing-pivot floor" widens the stop to just past the prior structural level if ATR is tighter.
• Calculating target levels — fixed R-multiple (1.5R, 2R) or technical (mean-reversion to BB middle, prior swing high).
• Position sizing & quantity — `qty = int((NAV × risk_pct%) / (entry − stop))`. Floor first, then divide — small but real bug source.
• Order execution — bracket orders (market entry + stop + target as OCO children) via the broker's API. Always log the parent + child IDs to a CSV for audit.
• Risk management — daily kill switch, max concurrent positions, max loss per trade as % of NAV. Enforce in code, not in the prompt.
• Backtesting before live — replay historical bars, simulate fills at next-bar open, track exits realistically. Watch for lookahead bias.
• Paper-first deployment — never flip to live without 60+ days of paper-account proof that the system behaves as designed under real bid-ask + slippage.

A typical signal-to-order pipeline

# 1. Fetch bars
ohlc = fetch_bars(ticker, period="6mo", interval="1d")

# 2. Compute indicators
ohlc["RSI"]   = rsi(ohlc["close"], 14)
ohlc["MACD"]  = macd(ohlc["close"], 12, 26, 9)
ohlc["ATR"]   = atr(ohlc, 14)

# 3. Detect divergence + candlestick confluence
divs = find_divergence(ticker, ohlc)
ohlc = detect_candle_patterns(ohlc)

# 4. If signal fires, build the trade plan
if buy_signal(ohlc):
    entry  = ohlc["close"].iloc[-1]
    stop   = entry - 2.0 * ohlc["ATR"].iloc[-1]
    target = entry + 3.0 * ohlc["ATR"].iloc[-1]   # 1.5R
    qty    = int((NAV * 0.01) / (entry - stop))    # 1% risk

    # 5. Submit bracket order to broker
    place_bracket(ticker, "BUY", qty, entry, stop, target)
    log_to_csv(ticker, qty, entry, stop, target)

Tools we use on the channel

• Python — the only realistic language for retail algo trading in 2026
• pandas + numpy — bar / indicator manipulation
• TA-Lib — fast indicator + candle pattern library (C-backed)
• ib_insync — IBKR API wrapper (much cleaner than native ibapi)
• kiteconnect — Zerodha API for Indian markets
• yfinance — free historical data for backtesting + universe scans
• scipy.signal.argrelextrema — pivot detection for divergence + structure
• matplotlib / lightweight-charts — visualization