import datetime, time import pandas as pd import numpy as np import datetime import matplotlib.pyplot as plt import seaborn as sns from pandas_datareader import data as pdr from collections import deque import os import alpaca_trade_api as api from math import floor ################################### import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import os import yfinance as yfin from googlefinance.client import get_price_data, get_prices_data, get_prices_time_data ###################################################### # Create an Account with Alpaca and input your keys below alpaca = api.REST('AK5AU7BPPHU7OL8BTZ34', '', 'https://api.alpaca.markets') ###################################################### while True: for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename)) param = { 'q': ".DJI", # Stock symbol (ex: "AAPL") 'i': "86400", # Interval size in seconds ("86400" = 1 day intervals) 'x': "INDEXDJX", # Stock exchange symbol on which stock is traded (ex: "NASD") 'p': "1Y" # Period (Ex: "1Y" = 1 year) } df = get_price_data(param) print(df) params = [ # Dow Jones { 'q': ".DJI", 'x': "INDEXDJX", }, # NYSE COMPOSITE (DJ) { 'q': "NYA", 'x': "INDEXNYSEGIS", }, # S&P 500 { 'q': ".INX", 'x': "INDEXSP", } ] period = "1Y" df = get_prices_data(params, period) print(df) params = [ # Dow Jones { 'q': ".DJI", 'x': "INDEXDJX", }, # NYSE COMPOSITE (DJ) { 'q': "NYA", 'x': "INDEXNYSEGIS", }, # S&P 500 { 'q': ".INX", 'x': "INDEXSP", } ] period = "1Y" interval = 60*30 # 30 minutes df = get_prices_time_data(params, period, interval) print(df) df from pandas_datareader import data as pdr from collections import deque class PSAR: def __init__(self, init_af=0.02, max_af=0.2, af_step=0.02): self.max_af = max_af self.init_af = init_af self.af = init_af self.af_step = af_step self.extreme_point = None self.high_price_trend = [] self.low_price_trend = [] self.high_price_window = deque(maxlen=2) self.low_price_window = deque(maxlen=2) # Lists to track results self.psar_list = [] self.af_list = [] self.ep_list = [] self.high_list = [] self.low_list = [] self.trend_list = [] self._num_days = 0 def calcPSAR(self, high, low): if self._num_days >= 3: psar = self._calcPSAR() else: psar = self._initPSARVals(high, low) psar = self._updateCurrentVals(psar, high, low) self._num_days += 1 return psar def _initPSARVals(self, high, low): if len(self.low_price_window) <= 1: self.trend = None self.extreme_point = high return None if self.high_price_window[0] < self.high_price_window[1]: self.trend = 1 psar = min(self.low_price_window) self.extreme_point = max(self.high_price_window) else: self.trend = 0 psar = max(self.high_price_window) self.extreme_point = min(self.low_price_window) return psar def _calcPSAR(self): prev_psar = self.psar_list[-1] if self.trend == 1: # Up psar = prev_psar + self.af * (self.extreme_point - prev_psar) psar = min(psar, min(self.low_price_window)) else: psar = prev_psar - self.af * (prev_psar - self.extreme_point) psar = max(psar, max(self.high_price_window)) return psar def _updateCurrentVals(self, psar, high, low): if self.trend == 1: self.high_price_trend.append(high) elif self.trend == 0: self.low_price_trend.append(low) psar = self._trendReversal(psar, high, low) self.psar_list.append(psar) self.af_list.append(self.af) self.ep_list.append(self.extreme_point) self.high_list.append(high) self.low_list.append(low) self.high_price_window.append(high) self.low_price_window.append(low) self.trend_list.append(self.trend) return psar def _trendReversal(self, psar, high, low): # Checks for reversals reversal = False if self.trend == 1 and psar > low: self.trend = 0 psar = max(self.high_price_trend) self.extreme_point = low reversal = True elif self.trend == 0 and psar < high: self.trend = 1 psar = min(self.low_price_trend) self.extreme_point = high reversal = True if reversal: self.af = self.init_af self.high_price_trend.clear() self.low_price_trend.clear() else: if high > self.extreme_point and self.trend == 1: self.af = min(self.af + self.af_step, self.max_af) self.extreme_point = high elif low < self.extreme_point and self.trend == 0: self.af = min(self.af + self.af_step, self.max_af) self.extreme_point = low return psar yfin.pdr_override() # Stock_List = ['TQQQ','SQQQ','NRGU','NUGT','FNGU','FAS','LABU','SOXL','CURE','TMF','IYR'] Stock_List= ['BTC-USD','LTC-USD','ETH-USD' ] Stock_Name = Stock_List[0] import pytz aapl = pdr.get_data_yahoo(Stock_Name, start=datetime.datetime.now(pytz.timezone('UTC')) + datetime.timedelta(-59), end=datetime.datetime.now(pytz.timezone('UTC')), interval="5m") length = len(aapl) CS_ = [] Cash_ = [] Stock_Change = [0]*length Stock_ = [[0]*length for _ in Stock_List] Stock_Exchange = [] Stocks_Change = [] Stock_Names = [] Monthly = [] Weekly = [] for i in range(len(Stock_List)): Stock_Name = Stock_List[i] yfObj = yfin.Ticker(Stock_Name) data = yfObj.history(start='2022-1-1', end=datetime.date.today()) indic = PSAR() aapl = pdr.get_data_yahoo(Stock_Name, start=datetime.datetime.now(pytz.timezone('UTC')) + datetime.timedelta(-59), end=datetime.datetime.now(pytz.timezone('UTC')), interval="5m") length = len(aapl) aaplv = aapl.iloc[0:length,4] aaplc = aapl.iloc[0:length,3] aapll = aapl.iloc[0:length,2] aaplh = aapl.iloc[0:length,1] aaplo = aapl.iloc[0:length,0] aaplv = aaplv.values.tolist() aaplo = aaplo.values.tolist() aaplc = aaplc.values.tolist () aapll = aapll.values.tolist() aaplh = aaplh.values.tolist () # print(len(aaplc)) # aaplo aapl['Log returns'] = np.log(aapl['Close']/aapl['Close'].shift()) aapl['Log returns'].std() volatility = aapl['Log returns'].std()*252**.5 minneg = [] maxpos = [] avgpos = 0 avgneg = 0 OBV = 0 N = 0 SMMA = 0 OBVindicator = 0 PeriodHigh = 0 PeriodLow = 0 diff_ = [] length_time = 14 for i in range(length_time): try: N = ((aaplc[i]-aapll[i])-(aaplh[i]-aaplc[i]))/(aaplh[i]-aapll[i]) except: try: N = ((aaplc[i-1]-aapll[i-1])-(aaplh[i-1]-aaplc[i-1]))/(aaplh[i-1]-aapll[i-1]) except: N = 0.5 SMMA += aaplc[i] if aaplo[i]-aaplc[i] > 0: pos = aaplo[i]-aaplc[i] avgpos += pos OBVpre = OBV try: OBV += (aaplv[i]/aaplv[i-1])/(i+1) except: try: OBV += (aaplv[i-1]/aaplv[i-2])/i except: OBV += (aaplv[i-2]/aaplv[i-3])/i else: neg = aaplo[i]-aaplc[i] avgneg += neg OBVpre = OBV try: OBV -= (aaplv[i]/aaplv[i-1]) except: try: OBV -= (aaplv[i-1]/aaplv[i-2]) except: OBV -= (aaplv[i-2]/aaplv[i-3]) maxpos.append(aaplc[i]) OBVindicator = abs(OBVpre)-abs(OBV) minneg = min(maxpos) maxposv = max(maxpos) for i in range(len(maxpos)): if minneg == maxpos[i]: PeriodHigh = i if maxpos == maxpos[i]: PeriodLow = i AroonUp = (-(PeriodHigh - length_time)/30)*100 AroonDown = ((PeriodLow - length_time)/30)*100 Aroon = (AroonUp + AroonDown) # maxposv = max(maxpos) avgneg = avgneg/length_time avgpos = avgpos/length_time # print(avgneg) # print(avgpos) SMMA = SMMA/length_time K = ((aaplc[length_time] - minneg)/(maxposv - minneg)) *100 RSI = 100 - (100/(1+(avgpos/(-avgneg)))) # print(K) # print(RSI) # print(OBV) # print(N) # print(SMMA) # print(AroonUp) # print(AroonDown) #Chaikin Indicator import numpy as np import matplotlib.pyplot as plt import matplotlib import collections account_dict = collections.defaultdict(list) account_dict1 = collections.defaultdict(list) value = 0 Cash = 50000 Stock = 50000 timestamp = 182 Rule_1_output = 0 Rule_2_output = 0 Rule_3_output = 0 Rule_4_output = 0 Rule_1S_output = 0 Rule_2S_output = 0 Rule_3S_output = 0 Rule_4S_output = 0 #Max Weight = 5 weight1 = 1 weight2 = 1 weight3 = 1 weight4 = 1 weight5 = 1 weight6 = 1 diff = 0 diffW = 0 diffM = 0 # weight1_ = [] # weight2_ = [] # weight3_ = [] # weight4_ = [] Value = 0.75 Buy = [] Sell = [] stock = aaplc day = [] diff_ = [] diff_W_ = [] Track_Change = [] Stock = [] K_ = [] RSI_ = [] OBV_ = [] N_ = [] SMMA_ = [] Aroon_ = [] MACD_ = [] K__ = [] OBV__ = [] RSI__ = [] N__ = [] MACD__ = [] def Indicator(value1, value2): minneg = [] maxpos = [] avgpos = 0 avgneg = 0 OBV = 0 N = 0 SMMA = 0 OBVindicator = 0 Williams = 0 EMA = 0 EMA12 = 0 EMA26 = 0 MACD = 0 Signal_Line = 0 for i in range((value1-value2),value1): Signal_Line = 0 #i = 1831 ################################# for l in range((value1-9),value1): for j in range((value1-12),value1): EMA += aaplc[j] # print(EMA) EMA12 = EMA/12 EMA12 += ((aaplc[j] - EMA12) * (2/12)) + EMA12 EMA = 0 for k in range((value1-26),value1): EMA += aaplc[k] EMA26 = EMA/26 EMA26 += ((aaplc[k] - EMA26) * (2/26)) + EMA26 MACD = EMA12 - EMA26 Signal_Line += MACD Signal_Line = Signal_Line/9 # print() # print(MACD) # print(Signal_Line) if Signal_Line < MACD: MACD = 1 if Signal_Line > MACD: MACD = 0 else: MACD = 1 # print(MACD) ################################# PeriodHigh = 0 PeriodLow = 0 try: N = ((aaplc[i]-aapll[i])-(aaplh[i]-aaplc[i]))/(aaplh[i]-aapll[i]) except: try: N = ((aaplc[i-1]-aapll[i-1])-(aaplh[i-1]-aaplc[i-1]))/(aaplh[i-1]-aapll[i-1]) except: try: N = ((aaplc[i-2]-aapll[i-2])-(aaplh[i-2]-aaplc[i-2]))/(aaplh[i-2]-aapll[i-2]) except: N = 0.5 SMMA += aaplc[i] if aaplo[i]-aaplc[i] > 0: pos = aaplo[i]-aaplc[i] avgpos += pos OBVpre = OBV try: OBV += (aaplv[i]/aaplv[i-1])/(i+1) except: OBV += (aaplv[i-1]/aaplv[i-2]) else: neg = aaplo[i]-aaplc[i] avgneg += neg OBVpre = OBV try: OBV -= (aaplv[i]/aaplv[i-1]) except: try: OBV -= (aaplv[i-1]/aaplv[i-2]) except: OBV -= (aaplv[i-2]/aaplv[i-3]) maxpos.append(aaplc[i]) OBVindicator = abs(OBVpre)-abs(OBV) minneg = min(maxpos) maxposv = max(maxpos) for i in range(len(maxpos)): if minneg == maxpos[i]: PeriodHigh = i if maxpos == maxpos[i]: PeriodLow = i # Williams = (PeriodHigh-aaplc[i])/(PeriodHigh-PeriodLow) AroonUp = (-(PeriodHigh - length_time)/30)*100 AroonDown = ((PeriodLow - length_time)/30)*100 Aroon = (AroonUp + AroonDown) avgneg = avgneg/length_time avgpos = avgpos/length_time # print(avgneg) # print(avgpos) SMMA = SMMA/length_time try: K = ((aaplc[length_time] - minneg)/(maxposv - minneg)) except: K = 6 try: RSI = 100 - (100/(1+(avgpos/-avgneg))) except: RSI = 50 # print(RSI) # K_.append(K) # RSI_.append(RSI) # OBV_.append(OBV) # N_.append(N) # SMMA_.append(SMMA) return (SMMA, OBV, N, RSI, K, Aroon, MACD, value1, value2) ################################################################## defaultbuy = [30,9,-0.5,-9,-50] defaultsell = [70,-20,0.5,-7,50] difference = [] Weight_List = [] difference_W = [] Weight_List_W = [] difference_M = [] Weight_List_M = [] def Output(): def Rule_1(RSI): if RSI < defaultbuy[0]: Rule_1_output = 1 # print('Rule1') else: return (weight1, 0) if Rule_1_output == True: return (weight1, Rule_1_output) else: return (weight1, Rule_1_output) def Rule_2(stock): if K < defaultbuy[1]: Rule_2_output = 1 # print('Rule2') else: return (weight2, 0) if Rule_2_output == True: return (weight2, Rule_2_output) def Rule_3(stock): if N < defaultbuy[2]: Rule_3_output = 1 # print('Rule3') else: return (weight3, 0) if Rule_3_output == True: return (weight3, Rule_3_output) def Rule_4(stock): if OBV < defaultbuy[3]: Rule_4_output = 1 # print('Rule4') else: return (weight4, 0) if Rule_4_output == True: return (weight4, Rule_4_output) def Rule_5(stock): if Aroon < -50: Rule_5_output = 1 # print('Rule4') else: return (weight5, 0) if Rule_5_output == True: return (weight5, Rule_5_output) def Rule_6(stock): if MACD < 0.5: Rule_6_output = 1 # print('Rule4') else: return (weight6, 0) if Rule_6_output == True: return (weight6, Rule_6_output) def Rules(weight1, weight2, weight3, weight4, weight6, Rule_1_output, Rule_2_output, Rule_3_output, Rule_4_output, Rule_6_output): if (Rule_1_output*weight1 + Rule_2_output*weight2 + Rule_3_output*weight3 + Rule_4_output*weight4 + Rule_6_output*weight6)/(weight1 + weight2 + weight3 + weight4 + weight6) > Value: return Buy.append(1) else: return Buy.append(0) def Rule_1_S(RSI): if RSI > defaultsell[0]: Rule_1S_output = 1 # print('Rule1') else: return (weight1, 0) if Rule_1S_output == True: return (weight1, Rule_1S_output) else: return (weight1, Rule_1S_output) def Rule_2_S(stock): if K > defaultsell[1]: Rule_2S_output = 1 # print('Rule2') else: return (weight2, 0) if Rule_2S_output == True: return (weight2, Rule_2S_output) def Rule_3_S(stock): if N > defaultsell[2]: Rule_3S_output = 1 # print('Rule3') else: return (weight3, 0) if Rule_3S_output == True: return (weight3, Rule_3S_output) def Rule_4_S(stock): if OBV > defaultsell[3]: Rule_4S_output = 1 # print('Rule4') else: return (weight4, 0) if Rule_4S_output == True: return (weight4, Rule_4S_output) def Rule_6_S(stock): if MACD > 0.5: Rule_6S_output = 1 # print('Rule4') else: return (weight6, 0) if Rule_6S_output == True: return (weight6, Rule_6S_output) def Rules_S(weight1, weight2, weight3, weight4, weight6, Rule_1S_output, Rule_2S_output, Rule_3S_output, Rule_4S_output, Rule_6S_output): if (Rule_1S_output*weight1 + Rule_2S_output*weight2 + Rule_3S_output*weight3 + Rule_4S_output*weight4 + Rule_6S_output*weight6)/(weight1 + weight2 + weight3 + weight4 + weight6) > Value: return Sell.append(1) else: return Sell.append(0) Rule_1_output = Rule_1(RSI)[1] Rule_2_output = Rule_2(K)[1] Rule_3_output = Rule_3(N)[1] Rule_4_output = Rule_4(OBV)[1] Rule_6_output = Rule_6(MACD)[1] Rules(weight1, weight2, weight3, weight4, weight6, Rule_1_output, Rule_2_output, Rule_3_output, Rule_4_output, Rule_6_output) Rule_1S_output = Rule_1_S(RSI)[1] Rule_2S_output = Rule_2_S(K)[1] Rule_3S_output = Rule_3_S(N)[1] Rule_4S_output = Rule_4_S(OBV)[1] Rule_6S_output = Rule_6_S(MACD)[1] Rules_S(weight1, weight2, weight3, weight4, weight6, Rule_1S_output, Rule_2S_output, Rule_3S_output, Rule_4S_output, Rule_6S_output) Weight_Value = 3+(((((RSI/100)+ Rule_1_output*weight1) + (((K+20)/20) + Rule_2_output*weight2) + (((N+1)/2)+Rule_3_output*weight3) + Rule_4_output*weight4 + Rule_6_output*weight6)/(weight1 + weight2 + weight3 + weight4 + weight6)) + ((((RSI/100)+Rule_1S_output*weight1) + (((K+20)/20)+Rule_2S_output*weight2) + (((N+1)/2)+Rule_3S_output*weight3) + Rule_4S_output*weight4 + Rule_6S_output*weight6)/(weight1 + weight2 + weight3 + weight4 + weight6)))/2 Weight_List.append(Weight_Value) Weight_Value_W = 3+(((((RSI/100)+ Rule_1_output*weight1) + (((K+20)/20) + Rule_2_output*weight2) + (((N+1)/2)+Rule_3_output*weight3) + Rule_4_output*weight4 + Rule_6_output*weight6)/(weight1 + weight2 + weight3 + weight4 + weight6)) + ((((RSI/100)+Rule_1S_output*weight1) + (((K+20)/20)+Rule_2S_output*weight2) + (((N+1)/2)+Rule_3S_output*weight3) + Rule_4S_output*weight4 + Rule_6S_output*weight6)/(weight1 + weight2 + weight3 + weight4 + weight6)))/2 Weight_List_W.append(Weight_Value) Weight_Value_M = 3+(((((RSI/100)+ Rule_1_output*weight1) + (((K+20)/20) + Rule_2_output*weight2) + (((N+1)/2)+Rule_3_output*weight3) + Rule_4_output*weight4 + Rule_6_output*weight6)/(weight1 + weight2 + weight3 + weight4 + weight6)) + ((((RSI/100)+Rule_1S_output*weight1) + (((K+20)/20)+Rule_2S_output*weight2) + (((N+1)/2)+Rule_3S_output*weight3) + Rule_4S_output*weight4 + Rule_6S_output*weight6)/(weight1 + weight2 + weight3 + weight4 + weight6)))/2 Weight_List_M.append(Weight_Value) def diff(): try: w1 = 0.998 w2 = 1.002 diff = 0 bulls = 0 bears = 0 for i in range(len(Weight_List)): if ((Weight_List[len(Weight_List)-1]) * 0.998) <= Weight_List[i] <= ((Weight_List[len(Weight_List)-1]) * 1.002): if i <= len(aaplc)-5: difference.append(((((aaplc[i+1] - aaplc[i])/(aaplc[i+1]))*100) + (((aaplc[i+2] - aaplc[i+1])/(aaplc[i+2]))*100) + (((aaplc[i+3] - aaplc[i+2])/(aaplc[i+3]))*100))/3) for i in range(len(difference)): diff += difference[i] if difference[i] > 0: bulls += 1 else: bears += 1 diff = ((diff/(len(difference)-1))) return (diff, bulls, bears) except: w1 = 0.98 w2 = 1.01 diff = 0 bulls = 0 bears = 0 for i in range(len(Weight_List)): if ((Weight_List[len(Weight_List)-1]) * 0.998) <= Weight_List[i] <= ((Weight_List[len(Weight_List)-1]) * 1.002): if i <= len(aaplc)-5: difference.append(((((aaplc[i+1] - aaplc[i])/(aaplc[i+1]))*100) + (((aaplc[i+2] - aaplc[i+1])/(aaplc[i+2]))*100) + (((aaplc[i+3] - aaplc[i+2])/(aaplc[i+3]))*100))/3) for i in range(len(difference)): diff += difference[i] if difference[i] > 0: bulls += 1 else: bears += 1 diff = ((diff/(len(difference)-1))) return (diff, bulls, bears) def diff_W(): try: w1 = 0.998 w2 = 1.002 diffW = 0 bull = 0 bear = 0 for i in range(len(Weight_List_W)): if ((Weight_List_W[len(Weight_List_W)-1]) * 0.998) <= Weight_List_W[i] <= ((Weight_List_W[len(Weight_List_W)-1]) * 1.002): if i <= len(aaplc)-24: difference_W.append(((aaplc[i+7]- aaplc[i])/(aaplc[i+7])*100)+ (((aaplc[i+14] - aaplc[i+7])/(aaplc[i+14]))*100) + (((aaplc[i+21] - aaplc[i+14])/(aaplc[i+21]))*100)/3) for i in range(len(difference_W)): diffW += difference_W[i] if differenceW[i] > 0: bull += 1 else: bear += 1 diffW = ((diffW/(len(difference_W)-1))) return (diffW, bull, bear) except: w1 = 0.98 w2 = 1.02 diffW = 0 bull = 0 bear = 0 for i in range(len(Weight_List_W)): if ((Weight_List_W[len(Weight_List_W)-1]) * 0.998) <= Weight_List_W[i] <= ((Weight_List_W[len(Weight_List_W)-1]) * 1.002): if i <= len(aaplc)-24: difference_W.append(((aaplc[i+7]- aaplc[i])/(aaplc[i+7])*100)+ (((aaplc[i+14] - aaplc[i+7])/(aaplc[i+14]))*100) + (((aaplc[i+21] - aaplc[i+14])/(aaplc[i+21]))*100)/3) for i in range(len(difference_W)): diffW += difference_W[i] if difference_W[i] > 0: bull += 1 else: bear += 1 diffW = ((diffW/(len(difference_W)-1))) return (diffW, bull, bear) def diff_M(): try: w1 = 0.998 w2 = 1.002 diffM = 0 bullss = 0 bearss = 0 for i in range(len(Weight_List_M)): if ((Weight_List_M[len(Weight_List_M)-1]) * 0.9) <= Weight_List_M[i] <= ((Weight_List_M[len(Weight_List_M)-1]) * 1.1): if i <= len(aaplc)-62: difference_M.append(((aaplc[i+30]- aaplc[i])/(aaplc[i+30])*100)+ (((aaplc[i+60] - aaplc[i+30])/(aaplc[i+60]))*100)/2) for i in range(len(difference_M)): diffM += difference_M[i] if differenceM[i] > 0: bullss += 1 else: bearss += 1 diffM = ((diffM/(len(difference_M)-1))) return (diffM, bullss, bearss) except: w1 = 0.98 w2 = 1.02 diffM = 0 bullss = 0 bearss = 0 for i in range(len(Weight_List_M)): if ((Weight_List_M[len(Weight_List_M)-1]) * 0.998) <= Weight_List_M[i] <= ((Weight_List_M[len(Weight_List_M)-1]) * 1.002): if i <= len(aaplc)-62: difference_M.append(((aaplc[i+30]- aaplc[i])/(aaplc[i+30])*100)+ (((aaplc[i+60] - aaplc[i+30])/(aaplc[i+60]))*100)/2) for i in range(len(difference_M)): diffM += difference_M[i] if difference_M[i] > 0: bullss += 1 else: bearss += 1 diffM = ((diffM/(len(difference_M)-1))) return (diffM, bullss, bearss) ################################################################## value2 = 14 value4 = value2 for i in range(16): aaplc.append(aaplc[len(aaplc)-1]) aaplo.append(aaplo[len(aaplo)-1]) aaplh.append(aaplh[len(aaplh)-1]) aapll.append(aapll[len(aapll)-1]) aaplv.append(aaplv[len(aaplv)-1]) for i in range(len(stock)-value2): value1 = value2 + i if i >= len(stock)-value2-14: burned_variable = [] burned_variable_W = [] burned_variable.append(diff()) burned_variable_W.append(diff_W()) aaplc[i] += aaplc[i-1] * ((burned_variable[0][0] + burned_variable_W[0][0]/7)/2)/100 aaplo[i] += aaplo[i-1] * ((burned_variable[0][0] + burned_variable_W[0][0]/7)/2)/100 aaplh[i] += aaplh[i-1] * ((burned_variable[0][0] + burned_variable_W[0][0]/7)/2)/100 aapll[i] += aapll[i-1] * ((burned_variable[0][0] + burned_variable_W[0][0]/7)/2)/100 aaplv[i] += aaplv[i-1] * ((burned_variable[0][0] + burned_variable_W[0][0]/7)/2)/100 RSI = Indicator(value1, value2)[3] K = Indicator(value1, value2)[4] OBV = Indicator(value1, value2)[1] N = Indicator(value1, value2)[2] SMMA = Indicator(value1, value2)[0] Aroon = Indicator(value1, value2)[5] MACD = Indicator(value1, value2)[6] Williams = Indicator(value1, value2)[7] K_.append(K) RSI_.append(RSI) OBV_.append(OBV) N_.append(N) SMMA_.append(SMMA) Aroon_.append(Aroon) MACD_.append(MACD) Output() if Buy[i] == 1: try: K__.append(K_[i]) OBV__.append(OBV_[i]) RSI__.append(RSI_[i]) N__.append(N_[i]) MACD__.append(MACD_[i]) except: pass diff_ = [] diff_W_ = [] diff_M_ = [] diff_.append(diff()) diff_W_.append(diff_W()) diff_M_.append(diff_M()) bulls = diff_[0][1] bears = diff_[0][2] bull = diff_W_[0][1] bear = diff_W_[0][2] bullss = diff_M_[0][1] bearss = diff_M_[0][2] indic = PSAR() data['PSAR'] = data.apply( lambda x: indic.calcPSAR(x['High'], x['Low']), axis=1) data['EP'] = indic.ep_list data['Trend'] = indic.trend_list data['AF'] = indic.af_list data.head() indic._calcPSAR() def How_Much(Buy, Cash, Stock): value0 = 0 value1 = 0.95 value2 = 0.95 value3 = 0.95 value4 = 0.15 value5 = 0.35 value6 = 0.75 j = 0 k = 0 Stock_Change = [] Track_Change = [] How_Much_Buy = [0] How_Much_Sell = [0] Cash = 1000 Stock = 1000 for i in range(len(Buy)): f = int(i) if i == 0: stock[f-1] = stock[f] Cash_.append(Cash) Change1 = -(stock[f-1]-stock[f])/(stock[f-1]) Change = Stock *(-(stock[f-1]-stock[f])/(stock[f-1])) Stock += Stock * (-(stock[f-1]-stock[f])/(stock[f-1])) Track_Change.append(Change1) if Stock < 0: How_Much_Buy.append(0) How_Much_Sell.append(0) print('f') continue if Cash < 0: How_Much_Buy.append(0) How_Much_Sell.append(0) print('g') continue if Buy[i] == 1: del How_Much_Sell[k] How_Much_Sell.append(0) if How_Much_Buy[j] == 3: Stock_Change.append('0') pass else: if How_Much_Buy[j] == 0: if Cash != 0 and Cash > 0: Stock += Cash*value1 Cash -= Cash*value1 Stock_Change.append('95') if How_Much_Buy[j] == 1: if Cash != 0 and Cash > 0: Stock += Cash*value1 Cash -= Cash*value1 Stock_Change.append('95') if How_Much_Buy[j] == 2: if Cash != 0 and Cash > 0: Stock += Cash*value2 Cash -= Cash*value2 Stock_Change.append('95') How_Much_Buy.append((How_Much_Buy[j] + 1)) j+=1 stock[f] if Sell[i] == 1: del How_Much_Buy[j] How_Much_Buy.append(0) if How_Much_Sell[k] == 3: Stock_Change.append('0') pass else: if How_Much_Sell[k] == 0: if Stock != 0 and Stock > 0: Cash += Stock*value4 Stock -= Stock*value4 Stock_Change.append('-15') if How_Much_Sell[k] == 1: if Stock != 0 and Stock > 0: Cash += Stock*value5 Stock -= Stock*value5 Stock_Change.append('-35') if How_Much_Sell[k] == 2: if Stock != 0 and Stock > 0: Cash += Stock*value6 Stock -= Stock*value6 Stock_Change.append('-75') How_Much_Sell.append((How_Much_Sell[k] + 1)) k+=1 if Buy[i] == 0 and Sell[i] == 0: Stock_Change.append('0') day.append(i) account_dict["Stock"].append(Stock) account_dict["Cash"].append(Cash) Total = Stock + Cash return (Total, Stock_Change, Stock, Cash, Track_Change) List = [] List.append(How_Much(Buy, Cash, Stock)) Stock_Change = List[0][1] Stock_Exchange.append(Stock_Change) Stock = List[0][2] Cash = List[0][3] Stocks_Change.append(List[0][4]) Stock_Names.append(Stock_Name) CS = Cash/Stock ################################################################ print() print(Stock_Name) print("Cash/Stock Ratio " + str(CS)[:-13]) print("You should Buy/Sell this percentage last 10 time periods " + Stock_Change[len(Stock_Change)- 6] + ' ' + Stock_Change[len(Stock_Change)- 5] + ' ' + Stock_Change[len(Stock_Change)- 4] + ' ' + Stock_Change[len(Stock_Change)- 3] + ' ' + Stock_Change[len(Stock_Change)- 2] + ' ' + Stock_Change[len(Stock_Change)- 1]) psar_close = data.loc[data['Trend']==1]['Close'] if CS < 2: if psar_close[len(psar_close)-1] == aaplc[len(aaplc)-2]: if int(Stock_Change[len(Stock_Change)- 10]) + int(Stock_Change[len(Stock_Change)- 9]) + int(Stock_Change[len(Stock_Change)- 8]) + int(Stock_Change[len(Stock_Change)- 7]) + int(Stock_Change[len(Stock_Change)- 6]) + int(Stock_Change[len(Stock_Change)- 5]) + int(Stock_Change[len(Stock_Change)- 4]) + int(Stock_Change[len(Stock_Change)- 3]) + int(Stock_Change[len(Stock_Change)- 2]) + int(Stock_Change[len(Stock_Change)- 1]) + int(Stock_Change[len(Stock_Change)-11]) > 0: CS_.append(Stock_Name) CS_.append(CS) CS_.append(Stock+Cash) if diff_W_[0][0] > 10: if psar_close[len(psar_close)-1] == aaplc[len(aaplc)-2]: if int(Stock_Change[len(Stock_Change)- 10]) + int(Stock_Change[len(Stock_Change)- 9]) + int(Stock_Change[len(Stock_Change)- 8]) + int(Stock_Change[len(Stock_Change)- 7]) + int(Stock_Change[len(Stock_Change)- 6]) + int(Stock_Change[len(Stock_Change)- 5]) + int(Stock_Change[len(Stock_Change)- 4]) + int(Stock_Change[len(Stock_Change)- 3]) + int(Stock_Change[len(Stock_Change)- 2]) + int(Stock_Change[len(Stock_Change)- 1]) + int(Stock_Change[len(Stock_Change)- 11]) > 0: CS_.append(Stock_Name) CS_.append(CS) CS_.append(Stock+Cash) ################################### btc = Stock_Name btc = btc[:-4] + "USD" print(btc) try: account = alpaca.get_account() account_cash = float(account.cash) account_equity = float(account.equity) position = alpaca.get_position(btc).qty cash_to_spend = account_cash / 1.1 # btc_snapshot = alpaca.get_crypto_snapshot(symbol=btc, exchange="CBSE") btc_snapshot = alpaca.get_crypto_snapshot(symbol=btc, exchange="CBSE") btc_latest_price = btc_snapshot.latest_quote.ap # btc_latest_price = btc_snapshot.latest_quote.ap bs = "" def calculate_order_size(cash_to_spend, latest_price, position): change = 0 psar_boost = 0.8 if psar_close[len(psar_close)-1] == aaplc[len(aaplc)-2]: psar_boost = 1.2 if CS < 1: change = 0.85 * psar_boost bs = "buy" if CS > 1: change = 0.85 bs = "sell" if (int(Stock_Change[len(Stock_Change)- 6]) + int(Stock_Change[len(Stock_Change)- 5]) + int(Stock_Change[len(Stock_Change)- 4]) + int(Stock_Change[len(Stock_Change)- 3]) + int(Stock_Change[len(Stock_Change)- 2]) + int(Stock_Change[len(Stock_Change)- 1])) > 0: if change == 0: change = (int(Stock_Change[len(Stock_Change)- 6]) + int(Stock_Change[len(Stock_Change)- 5]) + int(Stock_Change[len(Stock_Change)- 4]) + int(Stock_Change[len(Stock_Change)- 3]) + int(Stock_Change[len(Stock_Change)- 2]) +int(Stock_Change[len(Stock_Change)- 1]))/110 bs = "buy" elif bs == "buy": change = 0.95 * psar_boost bs = "buy" else: change = 0.45* psar_boost bs = "buy" if (int(Stock_Change[len(Stock_Change)- 6]) +int(Stock_Change[len(Stock_Change)- 5]) + int(Stock_Change[len(Stock_Change)- 4]) + int(Stock_Change[len(Stock_Change)- 3]) + int(Stock_Change[len(Stock_Change)- 2]) +int(Stock_Change[len(Stock_Change)- 1])) < 0: if change == 0: change = (int(Stock_Change[len(Stock_Change)- 6]) +int(Stock_Change[len(Stock_Change)- 5]) + int(Stock_Change[len(Stock_Change)- 4]) + int(Stock_Change[len(Stock_Change)- 3]) + int(Stock_Change[len(Stock_Change)- 2]) +int(Stock_Change[len(Stock_Change)- 1]))/110 bs = "sell" elif bs == "sell": change = 0.45 bs = "sell" else: change = 0.65 bs = "buy" position = float(position) precision_factor = 10000 if bs == "sell": units_to_ = floor(change* position * precision_factor) units_to_ /= precision_factor if bs == "buy": units_to_ = floor(change*cash_to_spend * precision_factor / latest_price) units_to_/= precision_factor return (units_to_, change, bs) btc_units = [] btc_units.append(calculate_order_size(cash_to_spend, btc_latest_price, position)) # if (position/btc_latest_price) < 1: # pass bs = btc_units[0][2] change = btc_units[0][1] btc_units = btc_units[0][0] print("Order Excecuted") alpaca.submit_order(symbol=btc, qty=btc_units, side=bs, type='market', time_in_force='gtc') print(bs + btc_units) except: print("") minutesToSleep = 5 - datetime.datetime.now().minute % 5 time.sleep(minutesToSleep * 60)
