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finlab.optimize

Strategy parameter optimization and combination testing module.

Use Cases

  • Test all combinations of multiple conditions: When you have multiple stock selection conditions (technical, fundamental, institutional), find the best intersection
  • Quickly find the best strategy combination: Automate testing of all possible combinations, identifying the best performer from dozens of strategies
  • Visually compare performance: Use heatmaps, bar charts, and line charts to quickly compare combinations

Quick Examples

from finlab import data
from finlab.optimize.combinations import sim_conditions

close = data.get("price:收盤價")
rev = data.get('monthly_revenue:當月營收')
營業利益成長率 = data.get('fundamental_features:營業利益成長率')

# Define 3 conditions
c1 = (close > close.average(20)) & (close > close.average(60))
c2 = 營業利益成長率 > 0
c3 = rev.average(3) / rev.average(12) > 1.1

exits = close < close.average(20)
conditions = {'c1': c1, 'c2': c2, 'c3': c3}

# Test all combinations (7 total: c1, c2, c3, c1&c2, c1&c3, c2&c3, c1&c2&c3)
report_collection = sim_conditions(
    conditions=conditions,
    hold_until={'exit': exits, 'stop_loss': 0.1},
    resample='M',
    position_limit=0.1,
    upload=False
)

# Visual comparison
report_collection.plot_stats('heatmap')  # Heatmap
report_collection.plot_stats('bar').show()  # Bar chart
report_collection.plot_creturns().show()  # Cumulative return line chart

Detailed Guide

See Strategy Parameter Optimization Guide for:

  • Why strategy optimization is needed
  • How to interpret 12 performance metrics
  • How to avoid overfitting
  • Optimization techniques with stop-loss/take-profit
  • Practical workflow recommendations

API Reference

finlab.optimize.combinations.sim_conditions 

sim_conditions(conditions, hold_until={}, *args, **kwargs)

取得回測報告集合

將選股條件排出所有的組合並進行回測，方便找出最好條件的交集結果。

PARAMETER DESCRIPTION
conditions

選股條件集合，key 為條件名稱，value 為條件變數，ex:{'c1':c1, 'c2':c2}

TYPE: dict

hold_until

設定訊號進出場語法糖參數，預設為不使用。ex:{'exit':exit, 'stop_loss':0.1}

TYPE: dict DEFAULT: {}

*args

finlab.backtest.sim() 回測參數設定。

TYPE: tuple DEFAULT: ()

**kwargs

finlab.backtest.sim() 回測參數設定。

TYPE: dict DEFAULT: {}

RETURNS DESCRIPTION
ReportCollection

回測數據報告

Examples:

from finlab import data
from finlab.backtest import sim
from finlab.optimize.combinations import sim_conditions

close = data.get("price:收盤價")
pe = data.get('price_earning_ratio:本益比')
rev=data.get('monthly_revenue:當月營收').index_str_to_date()
rev_ma3=rev.average(3)
rev_ma12=rev.average(12)

c1 = (close > close.average(20)) & (close > close.average(60))
c2 = (close == close.rolling(20).max())
c3 = pe < 15
c4 = rev_ma3/rev_ma12 > 1.1
c5 = rev/rev.shift(1) > 0.9
exits = close < close.average(20)
conditions = {'c1':c1, 'c2':c2, 'c3':c3, 'c4':c4, 'c5':c5}
report_collection = sim_conditions(conditions=conditions, hold_until={'exit':exits, 'stop_loss':0.1}, resample='M', position_limit=0.1, upload=False)
# 策略分組指標報告
# print(report_collection.stats)
report_collection.plot_creturns().show()
report_collection.plot_stats('bar').show()
report_collection.plot_stats('heatmap')
視覺化範例

ex1:

report_collection.plot_creturns().show()

繪製折線圖指標分群棒狀圖

bar

ex2:

report_collection.plot_stats('bar').show()

繪製指標分群棒狀圖

bar

ex3:

report_collection.plot_stats('heatmap')

繪製指標分級熱力圖，數值越大為排名越前面，avg_score為指標平均分數，分數越高為評價較正向的策略。

heatmap

Advanced Usage

  • Filter specific metrics: report_collection.plot_stats('bar', indicators=['daily_sharpe', 'win_ratio'])
  • Custom sorting: report_collection.plot_stats('heatmap', heatmap_sort_by='daily_sharpe')
  • Access specific combination: report = report_collection.reports['c1 & c3']

finlab.optimize.combinations.ReportCollection 

ReportCollection(reports)

回測組合比較報告

判斷策略組合數據優劣，從策略海中快速找到體質最強的策略。 也可以觀察在同條件下的策略疊加更多條件後會有什麼變化？ Args: reports (dict): 回測物件集合，ex:{'strategy1': finlab.backtest.sim(),'strategy2': finlab.backtest.sim()}

get_stats 

get_stats()

取得策略指標比較表

指標欄位說明：

  • 'daily_mean': 策略年化報酬
  • 'daily_sharpe': 策略年化夏普率
  • 'daily_sortino': 策略年化索提諾比率
  • 'max_drawdown': 策略報酬率最大回撤率(負向)
  • 'avg_drawdown': 策略平均回撤(負向)
  • 'ytd': 今年度策略報酬率
  • 'win_ratio': 每筆交易勝率
  • 'avg_return': 每筆交易平均獲利率
  • 'avg_mae': 每筆交易平均最大不利方向幅度(負向)
  • 'avg_bmfe': 最大不利方向發生前的"每筆交易平均最大有利方向幅度"，若數值越高，越有機會在停損之前操作停利。
  • 'avg_gmfe': 每筆交易平均最大有利方向幅度
  • 'avg_mdd': 每筆交易平均的最大回撤率(負向)
RETURNS DESCRIPTION
DataFrame

策略指標比較報表

plot_creturns 

plot_creturns()

繪製策略累積報酬率

比較策略淨值曲線變化

RETURNS DESCRIPTION
Figure

折線圖

Examples:

line

plot_stats 

plot_stats(mode='bar', heatmap_sort_by='avg_score', indicators=[])

策略指標比較報表視覺化

PARAMETER DESCRIPTION
mode

繪圖模式。'bar' - 指標分群棒狀圖。'heatmap' - 指標分級熱力圖。

TYPE: str DEFAULT: 'bar'

heatmap_sort_by

heatmap 降冪排序的決定欄位

TYPE: str or list of str DEFAULT: 'avg_score'

indicators

要顯示的特定指標欄位，預設為將指標全部顯示

TYPE: list DEFAULT: []

RETURNS DESCRIPTION
Figure

長條圖
DataFrame

熱力圖

Examples:

ex1:

繪製指標分群棒狀圖

bar

ex2:

繪製指標分級熱力圖。

'avg_score': 各指標加總後的平均分數，分數越高為整體評價較正向的策略。

預設以avg_score為排序，數值越大為排名越前面，分數越高為整體評價較優的策略。

heatmap

Performance Tips

When the number of conditions exceeds 6, there will be over 63 combinations, leading to long computation times. Recommendations:

  1. First backtest individual conditions, remove those with poor performance
  2. Test in batches (separate technical and fundamental)
  3. Use longer resample periods (e.g., 'Q')

Resources