import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn

df = pd.read_csv(r'your path\VSH3\VSH3Data.txt', sep=";", header=None)
df.columns = ["positionID","candleID","Side","openProfit","ADX[0]","ADX[1]","ATR[0]","ATR[1]","Std[0]","Std[1]",
            "MA[0]","MA[1]","Will[0]","Will[1]","High[0]","High[1]","Low[0]","Low[1]","Close[0]","Close[1]"]

trades = pd.read_csv(r'your path\VSH3\VSH3Trades.txt', sep=";", header=None)
trades.columns = ["positionID","profit"]

df = pd.merge(df, trades, on='positionID', how='outer')
#Delete this part of the code if your format is period-based
for i in df.columns[3:]:
  df[str(i)] = df[str(i)].str.replace(',','.')
  df[str(i)] = df[str(i)].astype(float)

#ADX analysis
ADXbins = np.linspace(0,100,11, dtype = np.float32)
df['target[0]'] = df['ADX[0]']
df['target[1]'] = df['ADX[1]']
ADXname = 'ADX'
ADXlabels = ['<10','<20','<30','<40','<50','<60','<70','<80','<90','<100']
createChart(df, ADXlabels, ADXname, ADXbins)

#Will analysis
Willbins = np.linspace(-100,0,11, dtype = np.float32)
df['target[0]'] = df['Will[0]']
df['target[1]'] = df['Will[1]']
Willname = 'Will'
Willlabels = ['<-90','<-80','<-70','<-60','<-50','<-40','<-30','<-20','<-10','<0']
createChart(df, Willlabels, Willname, Willbins)

#Range in ATR analysis
RanAbins = np.linspace(0,10,11, dtype = np.float32)
df['target[0]'] = (df['High[0]'] - df['Low[0]'])/ df['ATR[0]']
df['target[1]'] = (df['High[1]'] - df['Low[1]']) / df['ATR[1]']
RanAname = 'Range in ATR'
RanAlabels = ['<1','<2','<3','<4','<5','<6','<7','<8','<9','<10']
createChart(df, RanAlabels, RanAname, RanAbins)

#Relative ATR analysis
RAbins = np.linspace(0,1,11, dtype = np.float32)
df['target[0]'] = df['ATR[0]'] / df['Close[0]'] * 100
df['target[1]'] = df['ATR[1]'] / df['Close[1]'] * 100
RAname = 'Relative in ATR'
RAlabels = ['<.1%','<.2%','<.3%','<.4%','<.5%','<.6%','<.7%','<.8%','<.9%','<1%']
createChart(df, RAlabels, RAname, RAbins)

#Distance from High analysis
DisHbins = np.linspace(0,1,11, dtype = np.float32)
df['target[0]'] = (df['High[0]'] - df['Close[0]']) / df['ATR[0]']
df['target[1]'] = (df['High[1]'] - df['Close[1]']) / df['ATR[1]']
DisHname = 'Distance from High in ATR'
DisHlabels = ['<.1','<.2','<.3','<.4','<.5','<.6','<.7','<.8','<.9','<1']
createChart(df, DisHlabels, DisHname, DisHbins)

#Distance from Low analysis
DisLbins = np.linspace(0,1,11, dtype = np.float32)
df['target[0]'] = (df['Close[0]'] - df['Low[0]']) / df['ATR[0]']
df['target[1]'] = (df['Close[1]'] - df['Low[1]']) / df['ATR[1]']
DisLname = 'Distance from Low in ATR'
DisLlabels = ['<.1','<.2','<.3','<.4','<.5','<.6','<.7','<.8','<.9','<1']
createChart(df, DisLlabels, DisLname, DisLbins)

#Distance from SMA analysis
SMAbins = np.linspace(-5,5,11, dtype = np.float32)
df['target[0]'] = (df['Close[0]'] - df['SMA[0]']) / df['ATR[0]']
df['target[1]'] = (df['Close[1]'] - df['SMA[1]']) / df['ATR[1]']
SMAname = 'Distance from SMA in ATR'
SMAlabels = ['<-4','<-3','<-2','<-1','<0','<1','<2','<3','<4','<5']
createChart(df, SMAlabels, SMAname, SMAbins)

#DON'T CHANGE ANYTHING BELOW
def createChart(df, labels, name, bins):
    df1 = df[df['candleID'] == 0][['Side','target[0]','target[1]','profit']]
    df1['[0]bins'] = pd.cut(df1.iloc[:,1],bins)
    df1['[1]bins'] = pd.cut(df1.iloc[:,2],bins)
    long0 = df1[df1['Side']=='Long'].groupby('[0]bins')['profit'].agg(['sum', 'count'])
    long1 = df1[df1['Side']=='Long'].groupby('[1]bins')['profit'].agg(['sum', 'count'])
    short0 = df1[df1['Side']=='Short'].groupby('[0]bins')['profit'].agg(['sum', 'count'])
    short1 = df1[df1['Side']=='Short'].groupby('[1]bins')['profit'].agg(['sum', 'count'])

    plt.subplot(221)
    plt.bar(np.arange(len(long0['sum'])), long0['sum'], tick_label = labels)
    plt.gca().set_title(f'Long trades {name}[0]')
    for i, v in enumerate(long0['sum']):
        plt.gca().text(i-0.15, v, str(long0['count'].iloc[i]), color='black', fontweight='bold')

    plt.subplot(222)
    plt.bar(np.arange(len(long1['sum'])), long1['sum'], tick_label = labels)
    plt.gca().set_title(f'Long trades {name}[1]')
    for i, v in enumerate(long1['sum']):
        plt.gca().text(i-0.15, v, str(long1['count'].iloc[i]), color='black', fontweight='bold')

    plt.subplot(223)
    plt.bar(np.arange(len(short0['sum'])), short0['sum'], tick_label = labels)
    plt.gca().set_title(f'Short trades {name}[0]')
    for i, v in enumerate(short0['sum']):
        plt.gca().text(i-0.15, v, str(short0['count'].iloc[i]), color='black', fontweight='bold')

    plt.subplot(224)
    plt.bar(np.arange(len(short1['sum'])), short1['sum'], tick_label = labels)
    plt.gca().set_title(f'Short trades {name}[1]')
    for i, v in enumerate(short1['sum']):
        plt.gca().text(i-0.15, v, str(short1['count'].iloc[i]), color='black', fontweight='bold')

    plt.gcf().set_size_inches(20, 10)
    plt.savefig(fname = r'C:\Users\Christopher\Desktop\df for studies\Volatility Squeeze Hedge\VSH3\{}.png'.format(name))
    plt.close()