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Advances in Financial Machine Learning

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Python implementations of Machine Learning helper functions based on a book,

Advances in Financial Machine Learning
[1], written by
Marcos Lopez de Prado


Excute the following command

python install



  • Triple Barriers Labeling
  • CUSUM sampling ```Python from financeml.labeling import getbarrierlabels, cusumfilter from financeml.stats import getdaily_vol

vol = getdailyvol(close) trgt = vol timestamps = cusumfilter(close, vol) labels = getbarrierlabels(close, timestamps, trgt, sltp=[1, 1], numdays=1, minret=0, numthreads=16) print(

Return the following pandas.Series
python 2000-01-05 -1.0 2000-01-06 1.0 2000-01-10 -1.0 2000-01-11 1.0 2000-01-12 1.0 ``` * Future Returns for Regression


Parallel computing using

library. Here is the example of applying function to each element with parallelization. ```python import pandas as pd import numpy as np

def apply_func(x): return x ** 2

def func(df, timestamps, f): df_ = df.loc[timestamps] for idx, x in df.items(): df.loc[idx] = f(x) return df_

df = pd.Series(np.random.randn(10000)) from financeml.multiprocessing import mppandas_obj

results = mppandasobj(func, pdobj=('timestamps', df.index), numthreads=24, df=df, f=apply_func) print(results.head())

0 0.449278 1 1.411846 2 0.157630 3 4.949410 4 0.601459 dtype: float64 ```

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