Strategy Lab 02 - Oscillation / residual-reversion strategy

from pathlib import Path

import pandas as pd
from IPython.display import Image, display
from quant_trading.data import load_sample_goog_ohlcv
from quant_trading.decomposition_features import walkforward_price_volume_features
from quant_trading.strategy_lab import (
    TrendFollowingConfig,
    OscillationReversionConfig,
    backtest_signal_set,
    execution_price_panel,
    decomposition_trend_following_signals,
    decomposition_oscillation_reversion_signals,
    plot_signal_analysis,
    stats_table,
)
from quant_trading.strategy_baselines import (
    buy_and_hold_weights,
    dual_moving_average_weights,
    bollinger_mean_reversion_weights,
)
from quant_trading.strategy_lab import backtest_target_weights_next_bar

CHART_DIR = Path("examples/quant_trading/reports/strategy_lab/charts")

ohlcv = load_sample_goog_ohlcv(trim_start="2014-01-01")
symbol = "GOOG"
close = ohlcv["Close"].rename(symbol).to_frame()
volume = ohlcv["Volume"].rename(symbol).to_frame()
execution_prices = execution_price_panel(ohlcv, field="Open", next_bar=True)
execution_prices.columns = [symbol]

features = walkforward_price_volume_features(
    close, volume, method="STL", period=126, train_window=504, step=5, z_window=63
)

signal = decomposition_oscillation_reversion_signals(
    close,
    features,
    config=OscillationReversionConfig(
        entry_residual_z=1.75,
        exit_residual_z=0.15,
        max_abs_trend_strength=0.35,
        require_cycle_turn=True,
        allow_short=True,
    ),
    name="detime_STL_oscillation_reversion",
)

bt = backtest_signal_set(
    close, signal, execution_prices=execution_prices, fee_bps=5, slippage_bps=2, periods_per_year=252
)

baselines = {
    "buy_hold": buy_and_hold_weights(close),
    "classic_bollinger_20_2": bollinger_mean_reversion_weights(close, window=20, entry_z=2.0, allow_short=True),
}
results = {signal.name: bt}
for name, weights in baselines.items():
    results[name] = backtest_target_weights_next_bar(
        close, weights, execution_prices=execution_prices, fee_bps=5, slippage_bps=2, periods_per_year=252, name=name
    )

stats_table(results)

# residual_z is the actual traded deviation.  Negative values mean price is below trend+cycle.
pd.concat({
    "close": close[symbol],
    "fair_value": signal.diagnostics["fair_value"][symbol],
    "residual_z": signal.diagnostics["residual_z"][symbol],
    "weak_trend_regime": signal.diagnostics["weak_trend_regime"][symbol],
    "target_weight": signal.target_weights[symbol],
}, axis=1).tail(12)

bt.orders.tail(12)

bt.trades.tail(12)

out = CHART_DIR / "notebook_02_oscillation_reversion.png"
plot_signal_analysis(ohlcv, signal, bt, asset=symbol, output_path=out, title="DeTime STL oscillation / residual reversion strategy")
display(Image(filename=str(out)))
out.as_posix()

image/png

text/plain

'examples/quant_trading/reports/strategy_lab/charts/notebook_02_oscillation_reversion.png'