
import pandas as pd
import numpy as np
from binance.client import Client
client = Client()


from datetime import datetime

def getdailydata(symbol="BTCUSD", interval='1d', start_time=datetime(2019, 12, 31), end_time=datetime(2020, 12, 31)):
    frame = pd.DataFrame(client.get_historical_klines(
                symbol,
                interval,
                start_time.strftime("%-d %b, %Y"),
                end_time.strftime("%-d %b, %Y")))

    if len(frame) > 0:
        frame = frame.iloc[:,:5]
        frame.columns = ['DateTime', 'Open', 'High', 'Low', 'Close']
        frame = frame.astype('float')
        frame['DateTime'] = pd.to_datetime(frame['DateTime'], unit='ms')
        return frame


from datetime import date

today = date.today()

price_df = getdailydata('ETHBTC', '1d', datetime(2021,1,1), today)


price_df['Change'] = price_df['Close'] - price_df['Open']
price_df['ChangeR'] = price_df['Change'] / price_df['Open']
price_df['ChangeP'] = price_df['ChangeR'] * 100

for index, row in price_df.iterrows():
    change = price_df.loc[index, 'Change']
    if index == 0 and change > 0:
        price_df.loc[index, 'ConsecutivePositive'] = 1
        price_df.loc[index, 'ConsecutiveNegative'] = 0
    elif index == 0 and change < 0:
        price_df.loc[index, 'ConsecutiveNegative'] = 1
        price_df.loc[index, 'ConsecutivePositive'] = 0
    elif change > 0:
        price_df.loc[index, 'ConsecutivePositive'] = price_df.loc[index - 1,'ConsecutivePositive'] + 1
        price_df.loc[index, 'ConsecutiveNegative'] = 0
    elif change < 0:
        price_df.loc[index, 'ConsecutiveNegative'] = price_df.loc[index - 1,'ConsecutiveNegative'] + 1
        price_df.loc[index, 'ConsecutivePositive'] = 0


price_df['CummulativeProfit'] = price_df['Change'].cumsum()


price_df


price_df.plot(x='DateTime', y=['ConsecutiveNegative'], grid=True, figsize=(20,5))

<AxesSubplot: xlabel='DateTime'>


price_df.plot(x='DateTime', y=['ConsecutivePositive'], grid=True, figsize=(20,5))

<AxesSubplot: xlabel='DateTime'>


price_df_non_zero = price_df.replace(0, np.NaN)
price_df_non_zero.hist('ConsecutivePositive', bins=50, figsize=(15,5))

array([[<AxesSubplot: title={'center': 'ConsecutivePositive'}>]],
      dtype=object)


price_df_non_zero['ConsecutivePositive'].mean()

1.8789808917197452


price_df_non_zero.hist('ConsecutiveNegative', bins=50, figsize=(15,5))

array([[<AxesSubplot: title={'center': 'ConsecutiveNegative'}>]],
      dtype=object)


price_df_non_zero['ConsecutiveNegative'].mean()

1.7938461538461539


import matplotlib.pyplot as plt
price_df.plot(x='DateTime', y=['ChangeP'], grid=True, figsize=(20,5))

<AxesSubplot: xlabel='DateTime'>


price_df['ChangeP'].median()

-0.05214572616448347


price_df['ChangeP'].mean()

0.20077621787429273


price_df['ChangeP'].max()

23.1817804006317


price_df['ChangeP'].min()

-15.517635407937835


price_df['ChangeP'].gt(0).sum()

314


price_df['ChangeP'].lt(0).sum()

325


price_df.boxplot('ChangeP',vert=False, showmeans=True, figsize=(20,2))

<AxesSubplot: >


price_df.hist('ChangeP', bins=50, figsize=(15,5))

array([[<AxesSubplot: title={'center': 'ChangeP'}>]], dtype=object)


from scipy import stats
stats.normaltest(price_df["ChangeP"])

NormaltestResult(statistic=173.94505933937833, pvalue=1.6916491204946663e-38)

	DateTime	Open	High	Low	Close	Change	ChangeR	ChangeP	ConsecutiveNegative	ConsecutivePositive	CummulativeProfit
0	2021-01-01	0.025464	0.025551	0.024800	0.024850	-0.000614	-0.024112	-2.411247	1.0	0.0	-0.000614
1	2021-01-02	0.024851	0.024955	0.023540	0.024062	-0.000789	-0.031749	-3.174923	2.0	0.0	-0.001403
2	2021-01-03	0.024062	0.030500	0.023083	0.029640	0.005578	0.231818	23.181780	0.0	1.0	0.004175
3	2021-01-04	0.029639	0.035000	0.028929	0.032551	0.002912	0.098249	9.824893	0.0	2.0	0.007087
4	2021-01-05	0.032552	0.034555	0.031094	0.032393	-0.000159	-0.004884	-0.488449	1.0	0.0	0.006928
...	...	...	...	...	...	...	...	...	...	...	...
634	2022-09-27	0.069503	0.069972	0.068201	0.069609	0.000106	0.001525	0.152511	0.0	2.0	0.043781
635	2022-09-28	0.069610	0.069869	0.067550	0.068884	-0.000726	-0.010430	-1.042954	1.0	0.0	0.043055
636	2022-09-29	0.068884	0.069187	0.068077	0.068188	-0.000696	-0.010104	-1.010394	2.0	0.0	0.042359
637	2022-09-30	0.068189	0.069088	0.067598	0.068412	0.000223	0.003270	0.327032	0.0	1.0	0.042582
638	2022-10-01	0.068413	0.069002	0.067825	0.068044	-0.000369	-0.005394	-0.539371	1.0	0.0	0.042213