bootstrapindex 0.0.1

Returns block bootstrap indexes for walk-forward analysis (expanding or sliding window)

bootstrap-index

1. The aim of this package is to produce indexes of dataset used for walk forward optimization.
2. Walk Forward Analysis does optimization on a training set; test on a period after the set and then rolls it all forward and repeats the process. We have multiple out-of-sample periods and look at these results combined.
3. To faciliate Walk Forward Analysis, the package produces start and end of bootstrap indexes within each training set data chunk.

Examples

Initiating class

url="https://github.com/jironghuang/trend_following/raw/main/quantopian_data/futures_incl_2016.csv"
s=requests.get(url).content
data=pd.read_csv(io.StringIO(s.decode('utf-8')))    
data['Date'] = pd.to_datetime(data['Date'], format='%Y-%m-%d')
data.set_index('Date', inplace=True)    

bootstrap = bootstrapindex(data, window='sliding', 
                            num_samples_per_period=10, 
                            min_sample_size=300, 
                            prop_block_bootstrap=0.25, 
                            days_block=252, 
                            starting_index = 5
                            )   

Creating in-sample and out-of-sample index

url="https://github.com/jironghuang/trend_following/raw/main/quantopian_data/futures_incl_2016.csv"
s=requests.get(url).content
data=pd.read_csv(io.StringIO(s.decode('utf-8')))    
data['Date'] = pd.to_datetime(data['Date'], format='%Y-%m-%d')
data.set_index('Date', inplace=True)           
bootstrap = bootstrapindex(data, window='sliding', 
                            num_samples_per_period=10, 
                            min_sample_size=300, 
                            prop_block_bootstrap=0.25, 
                            days_block=252, 
                            starting_index = 5
                            )        
bootstrap = bootstrap_index(data)
bootstrap.create_window_index()
Out[93]: 
[[[0, 251], [252, 503]],
 [[0, 503], [504, 755]],     
...            

Producing block boostrap indexes from a data chunk

url="https://github.com/jironghuang/trend_following/raw/main/quantopian_data/futures_incl_2016.csv"
s=requests.get(url).content
data=pd.read_csv(io.StringIO(s.decode('utf-8')))    
data['Date'] = pd.to_datetime(data['Date'], format='%Y-%m-%d')
data.set_index('Date', inplace=True)       
bootstrap = bootstrapindex(data, window='sliding', 
                            num_samples_per_period=10, 
                            min_sample_size=300, 
                            prop_block_bootstrap=0.25, 
                            days_block=252, 
                            starting_index = 5
                            )        
bootstrap.extract_block_bootstrap_periods(sample_size = 100, start_sample_index = 50, end_sample_index = 500)
Out[143]: 
{'start_index': array([247, 118,  78, 171, 170, 368, 343, 215, 166, 287]),
 'end_index': array([372, 243, 203, 296, 295, 493, 468, 340, 291, 412])} 

Producing block boostrap indexes from all training set data chunks

url="https://github.com/jironghuang/trend_following/raw/main/quantopian_data/futures_incl_2016.csv"
s=requests.get(url).content
data=pd.read_csv(io.StringIO(s.decode('utf-8')))    
data['Date'] = pd.to_datetime(data['Date'], format='%Y-%m-%d')
data.set_index('Date', inplace=True)    

bootstrap = bootstrapindex(data, window='sliding', 
                            num_samples_per_period=10, 
                            min_sample_size=300, 
                            prop_block_bootstrap=0.25, 
                            days_block=252, 
                            starting_index = 5
                            )
bootstrap.create_dictionary_window_n_bootstrap_index()
bootstrap.expanding_windows_w_bootstrap_info
Out[132]: 
{1: {'in_sample_index': [5, 256],
  'out_sample_index': [257, 508],
  'bootstrap_index': {'start_index': array([3, 1, 0, 1, 1, 4, 4, 2, 1, 3]),
   'end_index': array([252, 250, 249, 250, 250, 253, 253, 251, 250, 252])}},
 2: {'in_sample_index': [257, 508],
  'out_sample_index': [509, 760],
  'bootstrap_index': {'start_index': array([ 98,  34,  14,  60,  60, 159, 203,  82,  58, 118]),    
...