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  • License: GNU General Public License v3 (GPLv3) (GPL-3.0-or-later)
  • Author: FinLab
  • Requires: Python >=3.8
  • Provides-Extra: cli , qlib , docs , dev , brokers
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Project description

FinLab

瞬間的閃念之中，揭示過去十年2000支股票的歷史數據，這就是finlab package！不僅如此，它與pandas無縫整合，讓你在策略創建的旅程中，如同進行一場華麗的交響樂演奏。其語法之簡潔，讓你撰寫策略時有如神助。

你只需要撰寫人類懂得邏輯，而程式會自動融合各種頻率的歷史數據來選股，這不僅是程式設計上的巧思，更是對簡潔的極致追求。當你以為這已經是頂峰，它的詳細回測結果，又將你帶入更深一層的分析維度，每一筆數據都讓你的決策更加精準。 在幾秒鐘之內，2000支股票的回測，這速度，這效率，它不僅是一個 package，這是一個交易者的夢想加速器！這是finlab，一個為熱血操盤手量身打造的回測神器！

功能

  • 📊 快速存取龐大資料集：單一指令即可取得2000支股票過去十年的歷史數據。
  • 🐼 Pandas整合：利用熟悉且功能強大的pandas函式庫，輕鬆設計交易策略。
  • 🔍 用戶友好的語法✍️：採用簡潔直觀編碼語法。
  • 🕒 多頻率數據處理：自動整合管理不同時間頻率的歷史數據。
  • 🔬 全面的回測分析：透過詳細的回測報告，獲得深入的洞察。
  • 🚀 高速計算：得益於 Cython 優化的性能，幾秒鐘內即可執行2000支股票的回測。
  • 🤖 機器學習：結合 qlib 研發機器學習策略。

相關連結

簡易教學

下載資料

輸入以下程式碼，即可下載資料。可以查詢有哪些歷史資料可以下載。

from finlab import data

data.get('price:收盤價')
date 0015 0050 0051 0052 0053
2007-04-23 9.54 57.85 32.83 38.4 nan
2007-04-24 9.54 58.1 32.99 38.65 nan
2007-04-25 9.52 57.6 32.8 38.59 nan
2007-04-26 9.59 57.7 32.8 38.6 nan
2007-04-27 9.55 57.5 32.72 38.4 nan

撰寫策略

可以用非常簡單的 Pandas 語法來撰寫策略邏輯，以創新高的策略來說，可以用以下的寫法：

from finlab import data

close = data.get('price:收盤價')

# 創三百個交易日新高
position = close >= close.rolling(300).max()
position
date 0015 0050 0051 0052 0053
2007-04-23 00:00:00 False False False False False
2007-04-24 00:00:00 False False False False False
2007-04-25 00:00:00 False False False False False
2007-04-26 00:00:00 False False False True False
2007-04-27 00:00:00 False False False False False

這邊的 position 是一個 False/True 的查詢表，當數值為 True ，代表該股票在當天有創新高，而數字 False 則代表沒有創新高。由於創新高的股票很少，上面的範例中，只有少數股票的數值會是 True。

假設我們希望每個月底，搜尋上表中數值為 True 的股票並且買入持有一個月，可以用以下的語法：

回測績效

from finlab import backtest

report = backtest.sim(position, resample='M')
report.display()

image

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  • License: GNU General Public License v3 (GPLv3) (GPL-3.0-or-later)
  • Author: FinLab
  • Requires: Python >=3.8
  • Provides-Extra: cli , qlib , docs , dev , brokers
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