AdvancedStrategiesPython

Run this module

cd "Strategies - Trend Following"
python "trend_following.py"

Trend Following¶

Trend-following: ride momentum with discipline. Backbone of CTAs and managed-futures funds (AHL, Winton, Man, MLP). Profits from extended directional moves; pays for it during chop.

Functions¶

Function	Description
`donchian_channel(high, low, window)`	Rolling N-period high/low band
`donchian_breakout_signal(c, h, l, entry, exit)`	Turtle-style entry/exit
`ma_crossover_signal(prices, fast, slow)`	Fast/slow MA crossover
`tsmom_signal(prices, lookback)`	Time-Series Momentum (MOP 2012)
`atr(high, low, close, window)`	Wilder's Average True Range
`atr_position_size(cap, risk, atr, k)`	Vol-targeted size
`trend_strength(prices, window)`	Log-price OLS slope (annualized)

Donchian Breakout (Turtles)¶

entry_high = max(high[t-N : t-1])
exit_low   = min(low[t-M : t-1])
long when close > entry_high; flat when close < exit_low

Classic windows: N=20 / M=10 (system 1), N=55 / M=20 (system 2).

ATR Position Sizing¶

contracts = (capital * risk_per_trade) / (ATR * stop_multiple)

Risk a fixed dollar amount per trade — bigger ATR → smaller size.

Example¶

from trend_following import donchian_breakout_signal, atr, atr_position_size

pos = donchian_breakout_signal(close, high, low, 20, 10)
a = atr(high, low, close, 14)
size = atr_position_size(100_000, 0.01, a[-1], stop_atr_multiple=2.0)

Practical Notes¶

Trend systems lose ~60% of months but win on tail months.
Diversify across markets (equities, rates, FX, commodities) — single-market trend is fragile.
Use volatility targeting: bigger ATR → smaller size, keeps risk constant.
TSMOM 12-month lookback is robust across asset classes (Moskowitz et al. 2012).

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