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
- 量化交易完整教學：什麼是量化交易、台股策略回測器與平台比較
- 程式文檔
- Jupyter Notebook 範例
- 回測儀表板
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()
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- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/6.2.0 CPython/3.13.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
7b9bd2e9fab248d286d0307b2b4f73f0f44089dfa20a8c7af2a2203b005592d6
|
|
| MD5 |
999d6a595da3671df28d01161e0eda93
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|
| BLAKE2b-256 |
a86b1bfd786cf3853eb0a64cdb32aaf7ae3668e88bb34df58ae3d5aa97346284
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