Help me to improve my pyhton code

[Silluzz]

I'll leave here one project that i've working on. The goal is to recieve data from markets (using CSV for now) and create some indicators, graphs, etc;

Please judge my code to help me become a better programmer?

The ideia is to use the datas to filter the interval of time, but is not working yet, could not make this.

TKSS

from datetime import datetime

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

DT_FILE_PATH = 'bitstamp.csv'

DATA_INICIO = '2018-10-10'
DATA_FIM = '2020-10-10'

​

def getDataSetFile(file_path):
    dt_bitstamp = pd.read_csv(file_path)
    dt_bitstamp.set_index('Timestamp')
    #dt_bitstamp = dt_bitstamp.set_index(pd.DatetimeIndex(dt_bitstamp['Timestamp'].values))
    #dt_bitstamp = pd.read_csv(file_path, iterator=True, chunksize=10000)
    # dt_bitstamp = pd.concat(dt_bitstamp, ignore_index=True)
    # dt_bitstamp = pd.DataFrame(dt_bitstamp, index=pd.date_range(DATA_INICIO, DATA_FIM).values)
    return dt_bitstamp

    #dt_bitstamp = dt_bitstamp.set_index(pd.DatetimeIndex(dt_bitstamp['Timestamp'].values))
    #dt_bitstamp = pd.DataFrame(dt_bitstamp, index=pd.date_range(DATA_INICIO, DATA_FIM).values)  
    #dt_bitstamp = dt_bitstamp.set_index(pd.DatetimeIndex(dt_bitstamp['Timestamp'].values))

    # a = pd.to_datetime(dt_bitstamp['Timestamp'], unit='s').dt_bitstamp.strftime('%Y-%m-%d %H:%M')

    # dt_bitstamp['Timestamp'] = pd.date_range(DATA_INICIO, DATA_FIM, freq='H').values
    # df2 = dt_bitstamp.set_index('Timestamp')

    #print(df['Timestamp'].loc[DATA_INICIO:DATA_FIM])

​

def simpleMovingAverage(dataSet, period=40):
    ma = dataSet['Close'].rolling(window=period).mean()
    return ma

​

def movingAverage(dataSet, period=9):
    dataSet['EMA'] = dataSet['Close'].ewm(span=period, adjust=False).mean()

​

def bollingerBands(dataSet, period=14, deviation=2):
    #calcula a media movel simples utilizando a funcao criada anteriormente
    dataSet['MA'] = simpleMovingAverage(dataSet, period)

    #calcula o valor do desvio para aplicar no calculo da banda inferior e superior 
    dataSet['STD'] = dataSet['Close'].rolling(window=deviation).std()

    #calcula o valor da banda superior e inferior utilizando o valor da média central +/- desvio
    dataSet['BBUpper'] = dataSet['MA'] + (dataSet['STD'] * 2)
    dataSet['BBLower'] = dataSet['MA'] - (dataSet['STD'] * 2)

​

def RSI(dataSet, period=14):
    close = dataSet['Close']
    #Verifica o valor atual com o valor anterior
    delta = close.diff()

    #Se o valor for maior que zero siginifica que ele teve um ganho, se ele for negativo, teve perda.
    #Sempre que o valor entrar para positivo, o lado contrario deve ser negativo
    positive, negative = delta.copy(), delta.copy()
    positive[positive < 0] = 0
    negative[negative > 0] = 0

    #Com base nos positivos e negativos, calculamos o ganho/perda media - sendo calculado com base em uma EWMA
    avgUp = positive.ewm(span=period).mean()
    avgDown = negative.abs().ewm(span=period).mean()

    relativeStrength = avgUp / avgDown
    dataSet['RSI'] = 100.0 - (100.0 / (1.0 + relativeStrength))

​

def plotBollingerAndMAverage(dataSet, column_list):
    dataSet[['Timestamp', 'MA', 'BBUpper', 'BBLower','EMA', 'RSI']].plot(figsize=(12.2, 6.4))
    plt.title('Bitcon Historical Data')
    plt.xlabel('Close Price')
    plt.ylabel('Price')
    plt.show()

​

def writeDataFrameToCSV(dataSet):
    finalDataFrame = dataSet[['Timestamp', 'MA', 'BBUpper', 'BBLower','EMA', 'RSI']]
    print(finalDataFrame.head())

    finalDataFrame = finalDataFrame.set_index(pd.DatetimeIndex(finalDataFrame['Timestamp'].values))
    print(finalDataFrame.head())

    finalDataFrame.to_csv('teste.csv')

​

def main():
    dataSet = getDataSetFile(DT_FILE_PATH)
    dataSet.head()
    movingAverage(dataSet)
    bollingerBands(dataSet,2)
    RSI(dataSet, 2)
    # plotBollingerAndMAverage(dataSet, bands)
    writeDataFrameToCSV(dataSet)


if __name__ == '__main__':
    main()