#To do:
#-Strategy refine
#-Buy time in RL not correct
#-Load Backlog from file
#-GUI
#-Plotting in same window
#x
#
#Trading bot
import pip
import yfinance as yf
import numpy as np
import pandas as pd
import ta
import math
from math import sqrt
import matplotlib.pyplot as plt
import pandas_datareader as web
import pandas as pd
from scipy.signal import savgol_filter
from scipy import optimize
from sklearn import linear_model
from datetime import datetime
import sys
from ta.volatility import BollingerBands
import degiroapi
from degiroapi.product import Product
from degiroapi.order import Order
from degiroapi.utils import pretty_json
import schedule
import time
from tkinter import *
pd.set_option('display.max_columns', None)
class Stocks:
Ticker = ['TWTR']
TickerSeries = ['AAPL','AMD','SPCE','TWTR','NFLX','TSLA','PFE','ETSY', 'SURF', 'WATT', 'MOXC', 'PCG', 'SQ', 'RH', 'SBUX', 'COIN']
def init():
#Initilize variables
global Budget
Budget = 1000
global Support_counter
Support_counter = 0
def Timer():
global Hours
global now
now = datetime.now()
Time = now.hour - datetime.strptime('15:30:00', '%H:%M:%S').hour
Hours = str(Time) + 'h'
return (Hours, now)
def Get_data(stock,range):
#Get stock data
global Stock_data
Stock_data = yf.download(tickers=stock,period=range,interval='1m')
#Get rid of unnecesary columns
Stock_data = Stock_data.drop('Open',1)
Stock_data = Stock_data.drop('High',1)
Stock_data = Stock_data.drop('Low',1)
Stock_data = Stock_data.drop('Close',1)
DataSet = pd.DataFrame()
DataSet = Stock_data.copy()
return (DataSet)
def Get_data_range(stock,From,To):
#Get stock data
global Stock_data
Stock_data = yf.download(tickers=stock,start=From,stop=To,interval='1m')
#Get rid of unnecesary columns
Stock_data = Stock_data.drop('Open',1)
Stock_data = Stock_data.drop('High',1)
Stock_data = Stock_data.drop('Low',1)
Stock_data = Stock_data.drop('Close',1)
DataSet = pd.DataFrame()
DataSet = Stock_data.copy()
return (DataSet)
def Data_init():
global DataSet
DataSet = pd.DataFrame()
DataSet['Price'] = Stock_data['Adj Close'].round(4)
DataSet['Price'].round(4)
DataSet['Support'] = np.nan
DataSet['Resistant'] = np.nan
DataSet['Buy_Signal_Price'] = np.nan
DataSet['Sell_Signal_Price'] = np.nan
DataSet['Gain'] = np.nan
DataSet['SuppLine'] = np.nan
DataSet['ResLine'] = np.nan
return (DataSet)
def Trading_History():
global History
History = pd.DataFrame()
History['Buy Time'] = ''
History['Stock'] = ''
History['Buy Price'] = ''
History['Amount'] = ''
History['Sold'] = ''
History['Sell Time'] = ''
History['Sell Price'] = ''
History['Gain'] = ''
return ()
def Indicators(a,b,c,d):
#Make SMA
DataSet['SMA5'] = DataSet['Price'].rolling(window = a).mean()
DataSet['SMA30'] = DataSet['Price'].rolling(window = b).mean()
DataSet['SMA45'] = DataSet['Price'].rolling(window = c).mean()
DataSet['EMA360'] = DataSet['Price'].ewm(span = d).mean()
return (DataSet)
def S_R(DataSet):
Local_Max = np.nan
Local_Min = np.nan
Have_Min = 0
Have_Max = 0
for i in range (len(DataSet)):
#Support
if DataSet['Price'].iloc[i] < DataSet['Price'].iloc[i-1]:
Local_Min = DataSet['Price'].iloc[i]
DataSet['Resistant'].iloc[i-1] = Local_Max
Local_Max = np.nan
#Resistant
if DataSet['Price'].iloc[i] > DataSet['Price'].iloc[i-1]:
Local_Max = DataSet['Price'].iloc[i]
DataSet['Support'].iloc[i-1] = Local_Min
Local_Min = np.nan
return (DataSet)
def LinearR (DataSet):
DataSet['ID'] = np.arange(len(DataSet))
LinearSuppData = pd.DataFrame()
LinearSuppData = DataSet.copy()
LinearResData = pd.DataFrame()
LinearResData = DataSet.copy()
LinearSuppData = LinearSuppData[LinearSuppData['Support'].notna()]
LinearResData = LinearResData[LinearResData['Resistant'].notna()]
DataSet['SuppMVA'] = LinearSuppData['Support'].rolling(window = 3).mean()
DataSet['ResMVA'] = LinearResData['Resistant'].rolling(window = 3).mean()
LinearSuppData.index = pd.to_numeric(pd.to_datetime(LinearSuppData.index))
LinearResData.index = pd.to_numeric(pd.to_datetime(LinearResData.index))
y_fit_Supp = LinearSuppData['Support'].to_numpy()
x_fit_Supp = LinearSuppData['ID'].to_numpy().reshape(-1,1)
y_fit_Res = LinearResData['Resistant'].to_numpy()
x_fit_Res = LinearResData['ID'].to_numpy().reshape(-1,1)
RegSuppLine = linear_model.LinearRegression()
RegSuppLine.fit(x_fit_Supp,y_fit_Supp)
RegResLine = linear_model.LinearRegression()
RegResLine.fit(x_fit_Res,y_fit_Res)
for i in range (len(DataSet)):
DataSet['SuppLine'] = (DataSet['ID'] * RegSuppLine.coef_) + RegSuppLine.intercept_
DataSet['ResLine'] = (DataSet['ID'] * RegResLine.coef_) + RegResLine.intercept_
return(DataSet)
def BollingerBands(m,l,h,DataSet):
BBSet = pd.DataFrame()
BBSet = DataSet
DataSet['BB_MAV'] = DataSet['Price'].rolling(window = m).mean()
DataSet['BB_SD'] = DataSet['Price'].rolling(window = m).std()
DataSet['BB_LO'] = DataSet['BB_MAV'] - (BBSet['BB_SD']*l)
DataSet['BB_HI'] = DataSet['BB_MAV'] + (BBSet['BB_SD']*h)
return(DataSet)
def Buy_Sell(DataSet, Stock):
global Amount
global History
print (History)
for i in range (len(DataSet)):
#Buy conditions
if (DataSet['Price'].iloc[i] <= DataSet['BB_LO'].iloc[i]) and (DataSet['Price'].iloc[i] < DataSet['SuppLine'].iloc[i] and DataSet['EMA360'].iloc[i] > DataSet['Price'].iloc[i]):
try:
if (not History['Stock'].iloc[History.loc[History['Sold'] == False].index[0]] == Stock):
(DataSet['Buy_Signal_Price'].iloc[i]) = (DataSet['Price'].iloc[i])
Amount = math.floor(Budget/(DataSet['Buy_Signal_Price'].iloc[i]))
History = History.append({'Buy Time': DataSet.index.time[i], 'Stock' : Stock, 'Buy Price' : round (DataSet['Buy_Signal_Price'][i], 4), 'Amount' : Amount, 'Sold' : False}, ignore_index=True)
#print('BUY!', Amount, 'of', Stocks.Ticker, 'for', DataSet['Buy_Signal_Price'][i])
except IndexError:
(DataSet['Buy_Signal_Price'].iloc[i]) = (DataSet['Price'].iloc[i])
Amount = math.floor(Budget/(DataSet['Buy_Signal_Price'].iloc[i]))
History = History.append({'Buy Time': DataSet.index.time[i], 'Stock' : Stock, 'Buy Price' : round (DataSet['Buy_Signal_Price'][i], 4), 'Amount' : Amount, 'Sold' : False}, ignore_index=True)
#print('BUY!', Amount, 'of', Stocks.Ticker, 'for', DataSet['Buy_Signal_Price'][i])
#Sell conditions
if (not History['Sold'].all()):
if (DataSet['Price'].iloc[i] >= DataSet['BB_HI'].iloc[i]) and (DataSet['Price'].iloc[i] > DataSet['ResLine'].iloc[i]) and (DataSet['EMA360'].iloc[i] < DataSet['Price'].iloc[i]) or ((DataSet['Price'][i]) <= (History['Buy Price'].iloc[History.loc[History['Sold'] == False].index[0]])*0.995):
if (History['Stock'].iloc[History.loc[History['Sold'] == False].index[0]] == Stock):
DataSet['Sell_Signal_Price'].iloc[i] = DataSet['Price'].iloc[i]
for h in range (len(History)):
if (History['Stock'].iloc[h] == Stock and History['Sold'].iloc[h] == False):
History['Sold'].iloc[h] = True
History['Sell Time'].iloc[h] = DataSet.index.time[i]
History['Sell Price'].iloc[h] = round (DataSet['Sell_Signal_Price'][i], 4)
History['Gain'].iloc[h] = (History['Sell Price'].iloc[h] - History['Buy Price'].iloc[h])*Amount
#print('SELL!', Amount, 'of', Stocks.Ticker, 'for', DataSet['Sell_Signal_Price'][i])
print(History)
return(DataSet, History)
def Buy_Sell_RL(DataSet, Stock):
global Amount
global Bought
global History
#Buy conditions
print((realprice[0]['data']['lastPrice'], '<=', DataSet['BB_LO'][-1]), (DataSet['Price'][-1], '<' , DataSet['SuppLine'][-1]), DataSet['EMA360'][-1],'>',DataSet['Price'][-1])
print((realprice[0]['data']['lastPrice'], '>=', DataSet['BB_HI'][-1]), (DataSet['Price'][-1], '>', DataSet['ResLine'][-1]))
if (realprice[0]['data']['lastPrice'] <= DataSet['BB_LO'][-1]) and (realprice[0]['data']['lastPrice'] < DataSet['SuppLine'][-1]) and (DataSet['EMA360'][-1] > realprice[0]['data']['lastPrice']):
try:
if (not History['Stock'].iloc[History.loc[History['Sold'] == False].index[0]] == Stock):
Amount = math.floor(Budget/(DataSet['Price'][-1]))
degiro.buyorder(Order.Type.LIMIT, Product(products[0]).id, 1, Amount, round (realprice[0]['data']['lastPrice'], 2))
#Loging
print('BUY!', Amount, 'of', Stock, 'for', realprice[0]['data']['lastPrice'])
History = History.append({'Buy Time': DataSet.index.time[-1], 'Stock' : Stock, 'Buy Price' : round (realprice[0]['data']['lastPrice'], 4), 'Amount' : Amount, 'Sold' : False}, ignore_index=True)
except IndexError:
Amount = math.floor(Budget/(DataSet['Price'][-1]))
degiro.buyorder(Order.Type.LIMIT, Product(products[0]).id, 1, Amount, round (realprice[0]['data']['lastPrice'], 2))
#Loging
print('BUY!', Amount, 'of', Stock, 'for', DataSet['Buy_Signal_Price'].tail())
History = History.append({'Buy Time': DataSet.index.time[-1], 'Stock' : Stock, 'Buy Price' : round (realprice[0]['data']['lastPrice'], 4), 'Amount' : Amount, 'Sold' : False}, ignore_index=True)
#Sell conditions
if (not History['Sold'].all()):
if (realprice[0]['data']['lastPrice'] >= DataSet['BB_HI'][-1]) and (realprice[0]['data']['lastPrice'] > DataSet['ResLine'][-1]) and (DataSet['EMA360'][-1] < realprice[0]['data']['lastPrice']) or ((realprice[0]['data']['lastPrice']) <= (History['Buy Price'].iloc[History.loc[History['Sold'] == False].index[0]])*0.998):
if (History['Stock'].iloc[History.loc[History['Sold'] == False].index[0]] == Stock):
degiro.sellorder(Order.Type.LIMIT, Product(products[0]).id, 1, Amount, round (realprice[0]['data']['lastPrice'], 2))
print('SELL!', Amount, 'of', Stock, 'for', realprice[0]['data']['lastPrice'])
for h in range (len(History)):
if (History['Stock'].iloc[h] == Stock and History['Sold'].iloc[h] == False):
History['Sold'].iloc[h] = True
History['Sell Time'].iloc[h] = DataSet.index.time[i]
History['Sell Price'].iloc[h] = round (realprice[0]['data']['lastPrice'], 4)
History['Gain'].iloc[h] = (History['Sell Price'].iloc[h] - History['Buy Price'].iloc[h])*Amount
return(DataSet, History)
def Degiro_Cash():
global degiro
degiro = degiroapi.DeGiro()
degiro.login("MoszneTrader", "B!z@nek77")
cashfunds = degiro.getdata(degiroapi.Data.Type.CASHFUNDS)
for data in cashfunds:
print(data)
return
def Degiro(Stock):
global products
global realprice
products = degiro.search_products(Stock)
realprice = degiro.real_time_price(Product(products[0]).id, degiroapi.Interval.Type.One_Day)
print(Product(products[0]).symbol,realprice[0]['data']['lastPrice'])
#if np.isnan(DataSet['Buy_Signal_Price'].iloc[-1]) == False:
#degiro.buyorder(Order.Type.LIMIT, Product(products[0]).id, 1, Amount, realprice[0]['data']['lastPrice'])
#print('BUY!', Amount, 'of', Stocks.Ticker, 'for', DataSet['Buy_Signal_Price'].tail())
#if np.isnan(DataSet['Sell_Signal_Price'].iloc[-1]) == False:
#degiro.sellorder(Order.Type.LIMIT, Product(products[0]).id, 1, Amount, realprice[0]['data']['lastPrice'])
#print('SELL!', Amount, 'of', Stocks.Ticker, 'for', DataSet['Sell_Signal_Price'].tail())
#Ploting options
def Plot():
#Ranges
x = DataSet.index
y = DataSet['Price']
plt.figure(figsize=(16, 10))
#plt.ylim([DataSet['Price'].min(),DataSet['Price'].max()])
plt.plot(x,y, label = 'Price')
#plt.plot(DataSet['SMA5'], label= 'SMA5')
#plt.plot(DataSet['SMA30'], label= 'SMA30')
#plt.plot(SMA45['Adj Close'], label= 'SMA45')
plt.plot(DataSet['EMA360'], label = 'EMA360')
plt.plot(DataSet['Buy_Signal_Price'], label = 'Buy', marker ='^', color = 'green', ms = 10)
plt.plot(DataSet['Sell_Signal_Price'], label = 'Sell', marker ='v', color = 'red', ms = 10)
plt.plot(DataSet['BB_MAV'], label = 'BolingerBand MAV', linestyle = '--', color = 'blue')
plt.plot(DataSet['BB_HI'], label = 'BolingerBand High', linestyle = '--', color = 'green')
plt.plot(DataSet['BB_LO'], label = 'BolingerBand Low', linestyle = '--', color = 'red')
#plt.fill_between(BBSet['BB_HI'], BBSet['BB_LO'], alpha = 0.1)
plt.plot(DataSet['SuppLine'], label = 'Support', color = 'green')
plt.plot(DataSet['ResLine'], label = 'Resistant', color = 'red')
#plt.scatter(DataSet.index,DataSet['Support'], color = 'green', label = 'Support', s = 4)
#plt.scatter(DataSet.index,DataSet['Resistant'], color = 'red', label = 'Resistant', s = 4)
plt.plot(DataSet['SuppMVA'], label = 'SupportMVA', color = 'green', linestyle = ':')
plt.plot(DataSet['ResMVA'], label = 'ResistanMVA', color = 'red', linestyle = ':')
plt.title(Stocks.Ticker)
plt.ylabel('Price $')
plt.xlabel('Time')
plt.legend(loc='upper left')
plt.show()
def GUI():
return
def Tunning(Tickers):
Row = -1
TunningSet = pd.DataFrame()
TunningSet['Stock'] = Tickers
TunningSet['Transactions'] = np.nan
TunningSet['Biggest_Winrate'] = np.nan
TunningSet['Biggest_Gain'] = np.nan
TunningSet['Best_m'] = np.nan
TunningSet['Best_l'] = np.nan
TunningSet['Best_h'] = np.nan
TunningSet['Best_a'] = np.nan
TunningSet['Best_b'] = np.nan
TunningSet['Best_c'] = np.nan
TunningSet['Best_d'] = np.nan
for t in Tickers:
Best_m = 0
Best_l = 0
Best_h = 0
Best_d = 0
Biggest_Winrate = 0
Biggest_Gain = 0
Row = Row + 1
Wintrate = 0
TotalTrans = 0
Get_data(t,'6h')
Data_init()
DataCopy = DataSet.copy()
for d in range(60,360,30):
for m in range (16,22,2):
for l in range (100,300,50):
for h in range (100,300,50):
DataCopy2 = DataCopy
S_R(DataSet)
LinearR(DataSet)
Indicators(5,15,30,d)
BollingerBands(m,l/100,h/100,DataCopy)
print('Current Step:','m',m,'l',l,'h',h,'d',d)
Buy_Sell(DataCopy)
print ('Curent Ticker:', t)
print ('Curent Winrate:',Winrate)
print('Biggest Winrate:',Biggest_Winrate)
print('Gains:', GainSum)
#Plot()
if (Winrate > Biggest_Winrate):
TotalTrans = Total
Biggest_Winrate = Winrate
Biggest_Gain = GainSum
Best_m = m
Best_l = l
Best_h = h
Best_d = d
DataCopy = DataCopy2
TunningSet['Transactions'] = TotalTrans
TotalTrans = 0
TunningSet['Biggest_Winrate'].iloc[Row] = Biggest_Winrate
TunningSet['Biggest_Gain'].iloc[Row] = Biggest_Gain
TunningSet['Best_m'].iloc[Row] = Best_m
TunningSet['Best_l'].iloc[Row] = Best_l
TunningSet['Best_h'].iloc[Row] = Best_h
TunningSet['Best_d'].iloc[Row] = Best_d
#print('Best results:', Biggest_Winrate, Biggest_Gain)
#print('Best m l h:', Best_m, Best_l/100, Best_h/100)
print(TunningSet)
return(TunningSet)
def Manual(Ticker):
Get_data(Ticker,'4h')
Degiro_Cash()
Data_init()
Trading_History()
#Get_data_range(Stocks.Ticker,'2021-06-04 11:00:00-04:00','2021-06-4 13:00:00-04:00')
Indicators(5,15,30,360)
S_R(DataSet)
LinearR(DataSet)
BollingerBands(30,2.25,2.25,DataSet)
Buy_Sell(DataSet, Ticker)
Degiro(Ticker)
Plot()
now = datetime.now()
now = now.date()
History.to_csv(f'History_{now}.csv')
#print(DataSet)
return()
def Run(Tickers):
Trading_History()
History = pd.read_csv(f'History_RL{datetime.now().date()}.csv')
while datetime.now() > datetime.now().replace(hour=15, minute=30, second=0, microsecond=0) and datetime.now() < datetime.now().replace(hour=22, minute=0, second=0, microsecond=0):
print(datetime.now())
Degiro_Cash()
Timer();
for t in Tickers:
Get_data(t,'Hours')
Data_init()
#Get_data_range(Stocks.Ticker,'2021-06-04 11:00:00-04:00','2021-06-4 13:00:00-04:00')
Indicators(5,15,30,360)
S_R(DataSet)
LinearR(DataSet)
BollingerBands(30,2.25,2.25,DataSet)
Degiro(t)
Buy_Sell_RL(DataSet, t)
print(History)
current_date = datetime.now()
current_date = now.date()
History.to_csv(f'History_RL{current_date}.csv')
time.sleep(15)
else:
print('It is not time yet!')
return()
init()
GUI()
#Tunning(Stocks.TickerSeries)
#Manual('SPCE')
Run(Stocks.TickerSeries)
MainWindow = Tk()
MainWindow.title('Trading Bot')
F1 = Entry()
F1.pack()
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