# xlib-quant
* 一个简单的量化研究框架，具备基本的数据获取、因子分析、机器学习、回测及结果分析功能。
* 框架具体思路与内容请参考公众号FinHack炼金术《从零开始卷量化(25)-脱离Qlib，手撸一个属于自己的量化投资框架！ 》
* **当且该框架并不稳定**，预计本系列文章全部更新完(预计65篇，5月底前更新完)后会出一个稳定版的分支。
# 公众号
# 框架整体思路
<img width="300" alt="image" src="https://user-images.githubusercontent.com/6196607/162599136-2a8286c8-b205-4f43-a894-61c771356920.png">
# 一把梭代码
import pandas as pd
from xlib import data
from xlib import factors
from xlib import model
from xlib.strategies import Top10Strategy
from xlib import backtest
df=data.get_all_index_data()
bench=data.get_index_data(index="000300",start_date="20200101",end_date="20220330",renew=False)
df=factors.getTA(df)
df['label']=df.groupby('symbol')['close'].shift(10)
df['label']=df['close']/df['label']
df_train,df_valid,df_pred=model.datasplit(df,train_end='2019-01-01',valid_end='2020-01-01')
model.lgbtrain(df_train,df_valid,label='label')
preds=model.lgbpred(df_pred,label='label')
preds['score']=preds['pred']
preds['rank']=preds.groupby('date')['pred'].rank()
preds['signal']=preds.apply(lambda x: 1 if x['rank']<=10 else 0 ,axis=1)
returns=backtest.test(preds,Top10Strategy)
backtest.analysis(returns,bench)
# 结果演示
<img width="600" alt="image" src="https://user-images.githubusercontent.com/6196607/162584205-8bb34525-4ff0-47c2-8b29-5674af881f29.png">
<img width="600" alt="image" src="https://user-images.githubusercontent.com/6196607/162584222-3ff3f10e-5a07-4621-a9c3-c903a0e8b34f.png">
一个简单的量化研究框架，具备基本的数据获取、因子分析、机器学习、回测及结果分析功能.zip
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一个简单的量化研究框架，具备基本的数据获取、因子分析、机器学习、回测及结果分析功能.zip （302个子文件） 
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