Strategy Map: From Classic Indicators to Decomposition-Aware Signals

The revised tutorial does not only improve Bollinger bands. It rewrites several classic strategy families by asking what each indicator was trying to estimate.

Strategy family	Classical version	DeTime rewrite	Status
Feature layer	raw close and raw volume	walk-forward price + volume decomposition	implemented in Tutorial 01
Dual moving average	fast SMA > slow SMA on price	fast/slow average on extracted trend, gated by residual and volume	implemented in Tutorial 02
MACD	fast EMA - slow EMA on price	MACD on extracted trend, gated by cycle, residual and volume	implemented in Tutorial 02
Multi-MA alignment	several raw moving averages aligned	trend state + residual stress + participation filter	implemented in Tutorial 02
Trend pullback	buy dip in an uptrend	trend intact + residual cheap + volume confirmation	implemented in Tutorial 02
RSI / Bollinger / price z-score	overbought/oversold on raw price	residual z-score, residual RSI and residual bands with cycle timing	implemented in Tutorial 03
Turtle / Donchian	breakout above prior high	breakout + trend + cycle + residual-overextension + volume confirmation	implemented in Tutorial 04
Pairs trading	spread z-score	decompose spread; trade residual only when spread trend is stable and pair volume/news state is acceptable	implemented in Tutorial 05
Rotation	momentum rank	cross-sectional trend/cycle/residual/volume score with volatility targeting	implemented in Tutorial 06

Implemented examples

from quant_trading.strategy_baselines import make_classic_baseline_weight_grid
from quant_trading.strategy_detime import make_detime_trend_weight_grid

classic_trend = make_classic_baseline_weight_grid(prices)
detime_trend = make_detime_trend_weight_grid(prices, features)

from quant_trading.strategy_mean_reversion import (
    make_classic_mean_reversion_weight_grid,
    make_detime_mean_reversion_weight_grid,
)

classic_reversion = make_classic_mean_reversion_weight_grid(prices)
detime_reversion = make_detime_mean_reversion_weight_grid(prices, features)

from quant_trading.strategy_breakout import (
    make_classic_breakout_weight_grid,
    make_detime_breakout_weight_grid,
)

classic_breakout = make_classic_breakout_weight_grid(ohlcv)
detime_breakout = make_detime_breakout_weight_grid(ohlcv, features)

Executable notebooks:

• examples/notebooks/quant_trading/02_decomposition_aware_moving_average_macd.ipynb
• examples/notebooks/quant_trading/03_residual_mean_reversion_rsi_bollinger.ipynb
• examples/notebooks/quant_trading/04_turtle_donchian_breakout_volume_confirmation.ipynb

from quant_trading.strategy_pairs import (
    walkforward_pair_spread_features,
    make_classic_pair_weight_grid,
    make_detime_pair_weight_grid,
)

pairs = [("KO", "PEP"), ("XOM", "CVX")]
spread_features, spread_panel, beta_panel, pair_specs = walkforward_pair_spread_features(prices, pairs)
classic_pairs = make_classic_pair_weight_grid(prices, pair_specs)
detime_pairs = make_detime_pair_weight_grid(
    prices, pair_specs, spread_features, spread_panel=spread_panel, beta_panel=beta_panel
)

from quant_trading.strategy_rotation import (
    make_classic_rotation_weight_grid,
    make_detime_rotation_weight_grid,
)

classic_rotation = make_classic_rotation_weight_grid(prices)
detime_rotation = make_detime_rotation_weight_grid(prices, features)

Additional executable notebooks:

• examples/notebooks/quant_trading/05_pairs_spread_decomposition_stat_arb.ipynb
• examples/notebooks/quant_trading/06_cross_sectional_rotation_portfolio.ipynb