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FTX_Ichimoku_Scanner_With_Chikou_MultiThreaded_MultiTimeFrame_2.py
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FTX_Ichimoku_Scanner_With_Chikou_MultiThreaded_MultiTimeFrame_2.py
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import glob, os
from datetime import datetime
from datetime import timedelta
from binance.client import Client
from binance.enums import HistoricalKlinesType
import binance
import pandas as pd
import requests
import threading
import time
import ta
import math
import ftx
from enum import Enum
class ScanType(Enum):
UP = 0
DOWN = 1
client = ftx.FtxClient(
api_key='',
api_secret='',
subaccount_name=''
)
# Set this variable to ScanType.UP for scanning uptrend assets or to ScanType.DOWN for scanning downtrend assets
scan_type = ScanType.UP
# Set this variable to False to scan in spot mode
scan_futures = False
scan_spot = True
def log_to_results(str_to_log):
fr = open("results.txt", "a")
fr.write(str_to_log + "\n")
fr.close()
def log_to_errors(str_to_log):
fr = open("errors.txt", "a")
fr.write(str_to_log + "\n")
fr.close()
def log_to_trades(str_to_log):
fr = open("trades.txt", "a")
fr.write(str_to_log + "\n")
fr.close()
def log_to_evol(str_to_log):
fr = open("evol.txt", "a")
fr.write(str_to_log + "\n")
fr.close()
if os.path.exists("results.txt"):
os.remove("results.txt")
if os.path.exists("errors.txt"):
os.remove("errors.txt")
if os.path.exists("trades.txt"):
os.remove("trades.txt")
if os.path.exists("evol.txt"):
os.remove("evol.txt")
for fg in glob.glob("CS_*.txt"):
os.remove(fg)
print("Scanning type =", scan_type.name)
# log_to_results("Scanning type = " + scan_type.name)
results_count = 0
stop_thread = False
list_results = []
array_futures = []
# Set the timeframe to scan on the following line
interval_for_klinesT = Client.KLINE_INTERVAL_15MINUTE
print("Scanning timeframe =", str(interval_for_klinesT))
days_ago_for_klinest = "80 day ago UTC" # for daily download by default
if interval_for_klinesT == Client.KLINE_INTERVAL_1MINUTE:
days_ago_for_klinest = "80 minute ago UTC"
elif interval_for_klinesT == Client.KLINE_INTERVAL_3MINUTE:
days_ago_for_klinest = "240 minute ago UTC"
elif interval_for_klinesT == Client.KLINE_INTERVAL_5MINUTE:
days_ago_for_klinest = "400 minute ago UTC"
elif interval_for_klinesT == Client.KLINE_INTERVAL_15MINUTE:
days_ago_for_klinest = "1200 minute ago UTC"
elif interval_for_klinesT == Client.KLINE_INTERVAL_30MINUTE:
days_ago_for_klinest = "2400 minute ago UTC"
elif interval_for_klinesT == Client.KLINE_INTERVAL_1HOUR:
days_ago_for_klinest = "80 hour ago UTC"
elif interval_for_klinesT == Client.KLINE_INTERVAL_2HOUR:
days_ago_for_klinest = "160 hour ago UTC"
elif interval_for_klinesT == Client.KLINE_INTERVAL_4HOUR:
days_ago_for_klinest = "320 hour ago UTC"
elif interval_for_klinesT == Client.KLINE_INTERVAL_6HOUR:
days_ago_for_klinest = "480 hour ago UTC"
elif interval_for_klinesT == Client.KLINE_INTERVAL_8HOUR:
days_ago_for_klinest = "640 hour ago UTC"
elif interval_for_klinesT == Client.KLINE_INTERVAL_12HOUR:
days_ago_for_klinest = "960 hour ago UTC"
elif interval_for_klinesT == Client.KLINE_INTERVAL_1DAY:
days_ago_for_klinest = "80 day ago UTC"
elif interval_for_klinesT == Client.KLINE_INTERVAL_3DAY:
days_ago_for_klinest = "240 day ago UTC"
dict_evol = {}
new_results_found = False
def execute_code(symbol):
global results_count, dict_evol
global new_results_found
symbol_type = "n/a"
HISTORY_RESOLUTION_MINUTE = 60
HISTORY_RESOLUTION_3MINUTE = 60 * 3
HISTORY_RESOLUTION_5MINUTE = 60 * 5
HISTORY_RESOLUTION_15MINUTE = 60 * 15
HISTORY_RESOLUTION_30MINUTE = 60 * 30
HISTORY_RESOLUTION_HOUR = 60 * 60
HISTORY_RESOLUTION_4HOUR = 60 * 60 * 4
HISTORY_RESOLUTION_DAY = 60 * 60 * 24
if interval_for_klinesT == Client.KLINE_INTERVAL_1MINUTE:
history_resolution = HISTORY_RESOLUTION_MINUTE
elif interval_for_klinesT == Client.KLINE_INTERVAL_5MINUTE:
history_resolution = HISTORY_RESOLUTION_5MINUTE
elif interval_for_klinesT == Client.KLINE_INTERVAL_15MINUTE:
history_resolution = HISTORY_RESOLUTION_15MINUTE
elif interval_for_klinesT == Client.KLINE_INTERVAL_1HOUR:
history_resolution = HISTORY_RESOLUTION_HOUR
elif interval_for_klinesT == Client.KLINE_INTERVAL_4HOUR:
history_resolution = HISTORY_RESOLUTION_4HOUR
elif interval_for_klinesT == Client.KLINE_INTERVAL_1DAY:
history_resolution = HISTORY_RESOLUTION_DAY
# history_resolution = history_resolution # define the resolution used for the scan here
delta_time = 0
if history_resolution == HISTORY_RESOLUTION_MINUTE: # using this resolution seems not ok, must be improved
delta_time = 1
if history_resolution == HISTORY_RESOLUTION_3MINUTE: # using this resolution seems not ok, must be improved
delta_time = 3
elif history_resolution == HISTORY_RESOLUTION_5MINUTE: # using this resolution seems not ok, must be improved
delta_time = 5
elif history_resolution == HISTORY_RESOLUTION_15MINUTE:
delta_time = 15
elif history_resolution == HISTORY_RESOLUTION_HOUR:
delta_time = 60
elif history_resolution == HISTORY_RESOLUTION_4HOUR:
delta_time = 60 * 4
elif history_resolution == HISTORY_RESOLUTION_DAY:
delta_time = 60 * 24
try:
data = client.get_historical_data(market_name=symbol, resolution=history_resolution, # 60min * 60sec = 3600 sec
limit=1000000, start_time=float(round(time.time())) - delta_time * 5000,
# 1000*3600 for resolution=3600*24 (daily) # 3600*3 for resolution=60*5 (5min) # 3600*3*15 for 60*15 # 3600 * 3 * 15 * 2 for 60*60
end_time=float(round(time.time())))
#if interval_for_klinesT != Client.KLINE_INTERVAL_1DAY:
dataH1 = client.get_historical_data(market_name=symbol, resolution=HISTORY_RESOLUTION_HOUR,
# 60min * 60sec = 3600 sec
limit=1000000,
start_time=float(round(time.time())) - 60 * 24 * 5000,
# 1000*3600 for resolution=3600*24 (daily) # 3600*3 for resolution=60*5 (5min) # 3600*3*15 for 60*15 # 3600 * 3 * 15 * 2 for 60*60
end_time=float(round(time.time())))
dataH4 = client.get_historical_data(market_name=symbol, resolution=HISTORY_RESOLUTION_4HOUR,
# 60min * 60sec = 3600 sec
limit=1000000,
start_time=float(round(time.time())) - 60 * 24 * 5000,
# 1000*3600 for resolution=3600*24 (daily) # 3600*3 for resolution=60*5 (5min) # 3600*3*15 for 60*15 # 3600 * 3 * 15 * 2 for 60*60
end_time=float(round(time.time())))
dataDaily = client.get_historical_data(market_name=symbol, resolution=HISTORY_RESOLUTION_DAY,
# 60min * 60sec = 3600 sec
limit=1000000,
start_time=float(round(time.time())) - 60 * 24 * 5000,
# 1000*3600 for resolution=3600*24 (daily) # 3600*3 for resolution=60*5 (5min) # 3600*3*15 for 60*15 # 3600 * 3 * 15 * 2 for 60*60
end_time=float(round(time.time())))
dataWeekly = client.get_historical_data(market_name=symbol, resolution=HISTORY_RESOLUTION_DAY * 7,
# 60min * 60sec = 3600 sec
limit=1000000,
start_time=float(round(time.time())) - 60 * 24 * 7 * 5000,
# 1000*3600 for resolution=3600*24 (daily) # 3600*3 for resolution=60*5 (5min) # 3600*3*15 for 60*15 # 3600 * 3 * 15 * 2 for 60*60
end_time=float(round(time.time())))
# print(" (ok)")
dframe = pd.DataFrame(data)
dframeH1 = pd.DataFrame(dataH1)
dframeH4 = pd.DataFrame(dataH4)
dframeDaily = pd.DataFrame(dataDaily)
dframeWeekly = pd.DataFrame(dataWeekly)
try:
dframe['close'] = pd.to_numeric(dframe['close'])
dframeH1['close'] = pd.to_numeric(dframeH1['close'])
dframeH4['close'] = pd.to_numeric(dframeH4['close'])
dframeDaily['close'] = pd.to_numeric(dframeDaily['close'])
dframeWeekly['close'] = pd.to_numeric(dframeWeekly['close'])
except:
print(symbol, "ERREUR DFRAME CLOSE")
# print(dframe)
return
try:
dframe['high'] = pd.to_numeric(dframe['high'])
dframeH1['high'] = pd.to_numeric(dframeH1['high'])
dframeH4['high'] = pd.to_numeric(dframeH4['high'])
dframeDaily['high'] = pd.to_numeric(dframeDaily['high'])
dframeWeekly['high'] = pd.to_numeric(dframeWeekly['high'])
except:
# print(symbol, "ERREUR DFRAME HIGH")
return
try:
dframe['low'] = pd.to_numeric(dframe['low'])
dframeH1['low'] = pd.to_numeric(dframeH1['low'])
dframeH4['low'] = pd.to_numeric(dframeH4['low'])
dframeDaily['low'] = pd.to_numeric(dframeDaily['low'])
dframeWeekly['low'] = pd.to_numeric(dframeWeekly['low'])
except:
# print(symbol, "ERREUR DFRAME LOW")
return
try:
dframe['open'] = pd.to_numeric(dframe['open'])
dframeH1['open'] = pd.to_numeric(dframeH1['open'])
dframeH4['open'] = pd.to_numeric(dframeH4['open'])
dframeDaily['open'] = pd.to_numeric(dframeDaily['open'])
dframeWeekly['open'] = pd.to_numeric(dframeWeekly['open'])
except:
# print(symbol, "ERREUR DFRAME OPEN")
return
dframe = dframe.set_index(dframe['startTime'])
dframeH1 = dframeH1.set_index(dframeH1['startTime'])
dframeH4 = dframeH4.set_index(dframeH4['startTime'])
dframeDaily = dframeDaily.set_index(dframeDaily['startTime'])
dframeWeekly = dframeWeekly.set_index(dframeWeekly['startTime'])
# dframe.index = pd.to_datetime(dframe.index, unit='ms')
except requests.exceptions.HTTPError:
print("Erreur (HTTPError) tentative obtention données historiques pour " + symbol)
log_to_errors("Erreur (HTTPError) tentative obtention données historiques pour " + symbol)
return
except requests.exceptions.ConnectionError:
print("Erreur (ConnectionError) tentative obtention données historiques pour " + symbol)
log_to_errors("Erreur (ConnectionError) tentative obtention données historiques pour " + symbol)
return
except binance.exceptions.BinanceAPIException:
# in case the symbol does not exist in futures then this exception is thrown
print(symbol + " Does not exist")
return
# a = time.time()
# my_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(a))
# my_time_2 = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(a - delta_time))
# dframe = pd.DataFrame(data)
# dframe['time'] = pd.to_datetime(dframe['time'], unit='ms')
# print(dframe)
try:
dframe['ICH_SSA'] = ta.trend.ichimoku_a(dframe['high'], dframe['low'], window1=9, window2=26).shift(25)
dframe['ICH_SSB'] = ta.trend.ichimoku_b(dframe['high'], dframe['low'], window2=26, window3=52).shift(25)
dframe['ICH_KS'] = ta.trend.ichimoku_base_line(dframe['high'], dframe['low'])
dframe['ICH_TS'] = ta.trend.ichimoku_conversion_line(dframe['high'], dframe['low'])
dframe['ICH_CS'] = dframe['close'].shift(-25)
dframeH1['ICH_SSA'] = ta.trend.ichimoku_a(dframeH1['high'], dframeH1['low'], window1=9,window2=26).shift(25)
dframeH1['ICH_SSB'] = ta.trend.ichimoku_b(dframeH1['high'], dframeH1['low'], window2=26,window3=52).shift(25)
dframeH1['ICH_KS'] = ta.trend.ichimoku_base_line(dframeH1['high'], dframeH1['low'])
dframeH1['ICH_TS'] = ta.trend.ichimoku_conversion_line(dframeH1['high'], dframeH1['low'])
dframeH1['ICH_CS'] = dframeH1['close'].shift(-25)
dframeH4['ICH_SSA'] = ta.trend.ichimoku_a(dframeH4['high'], dframeH4['low'], window1=9,window2=26).shift(25)
dframeH4['ICH_SSB'] = ta.trend.ichimoku_b(dframeH4['high'], dframeH4['low'], window2=26,window3=52).shift(25)
dframeH4['ICH_KS'] = ta.trend.ichimoku_base_line(dframeH4['high'], dframeH4['low'])
dframeH4['ICH_TS'] = ta.trend.ichimoku_conversion_line(dframeH4['high'], dframeH4['low'])
dframeH4['ICH_CS'] = dframeH4['close'].shift(-25)
dframeDaily['ICH_SSA'] = ta.trend.ichimoku_a(dframeDaily['high'], dframeDaily['low'], window1=9,window2=26).shift(25)
dframeDaily['ICH_SSB'] = ta.trend.ichimoku_b(dframeDaily['high'], dframeDaily['low'], window2=26,window3=52).shift(25)
dframeDaily['ICH_KS'] = ta.trend.ichimoku_base_line(dframeDaily['high'], dframeDaily['low'])
dframeDaily['ICH_TS'] = ta.trend.ichimoku_conversion_line(dframeDaily['high'], dframeDaily['low'])
dframeDaily['ICH_CS'] = dframeDaily['close'].shift(-25)
dframeWeekly['ICH_SSA'] = ta.trend.ichimoku_a(dframeWeekly['high'], dframeWeekly['low'], window1=9,window2=26).shift(25)
dframeWeekly['ICH_SSB'] = ta.trend.ichimoku_b(dframeWeekly['high'], dframeWeekly['low'], window2=26,window3=52).shift(25)
dframeWeekly['ICH_KS'] = ta.trend.ichimoku_base_line(dframeWeekly['high'], dframeWeekly['low'])
dframeWeekly['ICH_TS'] = ta.trend.ichimoku_conversion_line(dframeWeekly['high'], dframeWeekly['low'])
dframeWeekly['ICH_CS'] = dframeWeekly['close'].shift(-25)
except KeyError as err:
print("ERR001", err)
return
for indexdf, rowdf in dframeH1.iterrows():
ssaH1 = dframeH1['ICH_SSA'].iloc[-1]
ssbH1 = dframeH1['ICH_SSB'].iloc[-1]
#print(symbol, "ssaH1, ssbH1", ssaH1, ssbH1)
ksH1 = rowdf['ICH_KS']
tsH1 = rowdf['ICH_TS']
for indexdf, rowdf in dframeH4.iterrows():
ssaH4 = dframeH4['ICH_SSA'].iloc[-1]
ssbH4 = dframeH4['ICH_SSB'].iloc[-1]
#print(symbol, "ssaH4, ssbH4", ssaH4, ssbH4)
ksH4 = rowdf['ICH_KS']
tsH4 = rowdf['ICH_TS']
for indexdf, rowdf in dframeDaily.iterrows():
ssaDaily = dframeDaily['ICH_SSA'].iloc[-1]
ssbDaily = dframeDaily['ICH_SSB'].iloc[-1]
#print(symbol, "ssaDaily, ssbDaily", ssaDaily, ssbDaily)
ksDaily = rowdf['ICH_KS']
tsDaily = rowdf['ICH_TS']
for indexdf, rowdf in dframeWeekly.iterrows():
ssaWeekly = dframeWeekly['ICH_SSA'].iloc[-1]
ssbWeekly = dframeWeekly['ICH_SSB'].iloc[-1]
#print(symbol, "ssaDaily, ssbDaily", ssaDaily, ssbDaily)
ksWeekly = rowdf['ICH_KS']
tsWeekly = rowdf['ICH_TS']
for indexdf, rowdf in dframe.iterrows():
openp = rowdf['open']
high = rowdf['high']
low = rowdf['low']
close = rowdf['close']
ssa = rowdf['ICH_SSA']
ssb = rowdf['ICH_SSB']
ks = rowdf['ICH_KS']
ts = rowdf['ICH_TS']
# cs = rowdf['ICH_CS']
try:
ssa = dframe['ICH_SSA'].iloc[-1] # bougie n-1 car bougie 0 donne nan ?
ssb = dframe['ICH_SSB'].iloc[-1] # bougie n-1 car bougie 0 donne nan ?
ssa2 = dframe['ICH_SSA'].iloc[-2] # bougie n-1 car bougie 0 donne nan ?
ssa3 = dframe['ICH_SSA'].iloc[-3] # bougie n-1 car bougie 0 donne nan ?
ssa4 = dframe['ICH_SSA'].iloc[-4] # bougie n-1 car bougie 0 donne nan ?
ssa5 = dframe['ICH_SSA'].iloc[-5] # bougie n-1 car bougie 0 donne nan ?
ssb2 = dframe['ICH_SSB'].iloc[-2] # bougie n-1 car bougie 0 donne nan ?
ssb3 = dframe['ICH_SSB'].iloc[-3] # bougie n-1 car bougie 0 donne nan ?
ssb4 = dframe['ICH_SSB'].iloc[-4] # bougie n-1 car bougie 0 donne nan ?
ssb5 = dframe['ICH_SSB'].iloc[-5] # bougie n-1 car bougie 0 donne nan ?
# print(ssa, ssb, ssa2, ssb2)
cs = dframe['ICH_CS'].iloc[-26] # cs bougie n en cours
cs2 = dframe['ICH_CS'].iloc[-27] # cs bougie n-1
cs3 = dframe['ICH_CS'].iloc[-28] # cs bougie n en cours
# print(cs, cs2)
csH1 = dframeH1['ICH_CS'].iloc[-26] # cs bougie n en cours
csH4 = dframeH4['ICH_CS'].iloc[-26] # cs bougie n en cours
csDaily = dframeDaily['ICH_CS'].iloc[-26] # cs bougie n en cours
csWeekly = dframeDaily['ICH_CS'].iloc[-26] # cs bougie n en cours
openpH1 = dframeH1['open'].iloc[-1]
openpH4 = dframeH4['open'].iloc[-1]
openpDaily = dframeDaily['open'].iloc[-1]
openpWeekly = dframeWeekly['open'].iloc[-1]
closeH1 = dframeH1['close'].iloc[-1]
closeH4 = dframeH4['close'].iloc[-1]
closeDaily = dframeDaily['close'].iloc[-1]
closeWeekly = dframeWeekly['close'].iloc[-1]
highH1 = dframeH1['high'].iloc[-1]
highH4 = dframeH4['high'].iloc[-1]
highDaily = dframeDaily['high'].iloc[-1]
highWeekly = dframeWeekly['high'].iloc[-1]
lowH1 = dframeH1['low'].iloc[-1]
lowH4 = dframeH4['low'].iloc[-1]
lowDaily = dframeDaily['low'].iloc[-1]
lowWeekly = dframeWeekly['low'].iloc[-1]
openchikouH1 = dframeH1['open'].iloc[-26]
openchikouH4 = dframeH4['open'].iloc[-26]
openchikouDaily = dframeDaily['open'].iloc[-26]
openchikouWeekly = dframeWeekly['open'].iloc[-26]
closechikouH1 = dframeH1['close'].iloc[-26]
closechikouH4 = dframeH4['close'].iloc[-26]
closechikouDaily = dframeDaily['close'].iloc[-26]
closechikouWeekly = dframeWeekly['close'].iloc[-26]
highchikouH1 = dframeH1['high'].iloc[-26]
highchikouH4 = dframeH4['high'].iloc[-26]
highchikouDaily = dframeDaily['high'].iloc[-26]
highchikouWeekly = dframeWeekly['high'].iloc[-26]
lowchikouH1 = dframeH1['low'].iloc[-26]
lowchikouH4 = dframeH4['low'].iloc[-26]
lowchikouDaily = dframeDaily['low'].iloc[-26]
lowchikouWeekly = dframeWeekly['low'].iloc[-26]
ssbchikou = dframe['ICH_SSB'].iloc[-52]
ssbchikou2 = dframe['ICH_SSB'].iloc[-27] #tocheck
ssbchikou3 = dframe['ICH_SSB'].iloc[-28] #tocheck
ssbchikou4 = dframe['ICH_SSB'].iloc[-29] #tocheck
ssbchikou5 = dframe['ICH_SSB'].iloc[-30] #tocheck
ssbchikou6 = dframe['ICH_SSB'].iloc[-31] #tocheck
# print(ssbchikou, ssbchikou2, ssbchikou3, ssbchikou4, ssbchikou5, ssbchikou6)
ssachikou = dframe['ICH_SSA'].iloc[-26]
ssachikou2 = dframe['ICH_SSA'].iloc[-27]
ssachikou3 = dframe['ICH_SSA'].iloc[-28]
# print(ssachikou, ssachikou2, ssachikou3)
closechikou = dframe['close'].iloc[-26]
closechikou2 = dframe['close'].iloc[-27]
# print(closechikou, closechikou2)
openchikou = dframe['open'].iloc[-26]
openchikou2 = dframe['open'].iloc[-27]
lowchikou = dframe['low'].iloc[-26]
lowchikou2 = dframe['low'].iloc[-27]
highchikou = dframe['high'].iloc[-26]
highchikou2 = dframe['high'].iloc[-27]
kijunchikou = dframe['ICH_KS'].iloc[-26]
kijunchikou2 = dframe['ICH_KS'].iloc[-27]
kijunchikou3 = dframe['ICH_KS'].iloc[-28]
# print(kijunchikou, kijunchikou2, kijunchikou3)
tenkanchikou = dframe['ICH_TS'].iloc[-26]
tenkanchikou2 = dframe['ICH_TS'].iloc[-27]
tenkanchikou3 = dframe['ICH_TS'].iloc[-28]
# print(tenkanchikou, tenkanchikou2, tenkanchikou3)
timestamp = pd.to_datetime(rowdf['time'], unit='ms')
# print(timestamp, openp, close, ssa, ssb)
except IndexError as error:
print("ERR002", symbol + " EXCEPTION " + str(error))
log_to_errors(symbol + " EXCEPTION " + str(error) + '\n')
# quit(0)
break
timestamp = pd.to_datetime(rowdf['time'], unit='ms')
# timestamp = pd.to_datetime(rowdf['timestamp'], unit='ms')
error_nan_values = False
# To check the values of Ichimoku data (use TradingView with Ichimoku Cloud to compare them)
# log_to_results(str(timestamp) + " " + symbol + " closecs=" + str(closechikou) + " closecs2=" + str(closechikou2) + " CS=" + str(cs) + " CS2=" + str(cs2) + " SSBCS=" + str(ssbchikou) + " SSBCS2=" + str(ssbchikou2) + " SSBCS3=" + str(ssbchikou3) + " KSCS=" + str(kijunchikou)+ " KSCS2=" + str(kijunchikou2)+ " KSCS3=" + str(kijunchikou3) + " TSCS=" + str(tenkanchikou)+ " TSCS2=" + str(tenkanchikou2)+ " TSCS3=" + str(tenkanchikou3) + " SSACS=" + str(ssachikou) + " SSACS2=" + str(ssachikou2) + " SSACS3=" + str(ssachikou3) + " SSA=" + str(ssa) + " SSB=" + str(ssb))
# exit()
if math.isnan(ssa) or math.isnan(ssb) or math.isnan(closechikou) or math.isnan(closechikou2) or math.isnan(
cs) or math.isnan(cs2) or math.isnan(ssbchikou) or math.isnan(ssbchikou2) or math.isnan(
ssbchikou3) or math.isnan(kijunchikou) or math.isnan(kijunchikou2) or math.isnan(
kijunchikou3) or math.isnan(tenkanchikou) or math.isnan(tenkanchikou2) or math.isnan(
tenkanchikou3) or math.isnan(ssachikou) or math.isnan(ssbchikou2) or math.isnan(ssachikou3):
print(symbol + " THERE ARE NAN VALUES IN ICHIMOKU DATA")
log_to_errors(symbol + " THERE ARE NAN VALUES IN ICHIMOKU DATA" + '\n')
error_nan_values = True
# quit(0)
if error_nan_values:
break
filename = "CS_" + symbol.replace('/', '_') + ".txt"
if os.path.exists(filename):
os.remove(filename)
# now_cs = datetime.datetime_result_min() - timedelta(hours=4 * 26)
# # print("now_cs=" + str(now_cs))
# # quit(0)
# if timestamp.year == now_cs.year and timestamp.month == now_cs.year and timestamp.day == now_cs.day and timestamp.hour == now_cs.hour:
# print(str(cs))
data_minute = timestamp.minute
data_hour = timestamp.hour
data_day = timestamp.day
data_month = timestamp.month
data_year = timestamp.year
if interval_for_klinesT == Client.KLINE_INTERVAL_1MINUTE:
datetime_result_min = datetime.now() - timedelta(minutes=1)
elif interval_for_klinesT == Client.KLINE_INTERVAL_3MINUTE:
# datetime_result_min = datetime.now() - timedelta(minutes=15)
datetime_result_min = datetime.now() - timedelta(minutes=3)
elif interval_for_klinesT == Client.KLINE_INTERVAL_5MINUTE:
# datetime_result_min = datetime.now() - timedelta(minutes=15)
datetime_result_min = datetime.now() - timedelta(minutes=5)
elif interval_for_klinesT == Client.KLINE_INTERVAL_15MINUTE:
# datetime_result_min = datetime.now() - timedelta(hours=1)
datetime_result_min = datetime.now() - timedelta(minutes=15)
elif interval_for_klinesT == Client.KLINE_INTERVAL_30MINUTE:
# datetime_result_min = datetime.now() - timedelta(hours=1)
datetime_result_min = datetime.now() - timedelta(minutes=30)
elif interval_for_klinesT == Client.KLINE_INTERVAL_1HOUR:
datetime_result_min = datetime.now() - timedelta(hours=1)
elif interval_for_klinesT == Client.KLINE_INTERVAL_2HOUR:
datetime_result_min = datetime.now() - timedelta(hours=2)
elif interval_for_klinesT == Client.KLINE_INTERVAL_4HOUR:
datetime_result_min = datetime.now() - timedelta(hours=4)
elif interval_for_klinesT == Client.KLINE_INTERVAL_6HOUR:
datetime_result_min = datetime.now() - timedelta(hours=6)
elif interval_for_klinesT == Client.KLINE_INTERVAL_8HOUR:
datetime_result_min = datetime.now() - timedelta(hours=8)
elif interval_for_klinesT == Client.KLINE_INTERVAL_12HOUR:
datetime_result_min = datetime.now() - timedelta(hours=12)
elif interval_for_klinesT == Client.KLINE_INTERVAL_1DAY:
datetime_result_min = datetime.now() - timedelta(hours=timestamp.hour)
elif interval_for_klinesT == Client.KLINE_INTERVAL_3DAY:
datetime_result_min = datetime.now() - timedelta(hours=72)
else:
datetime_result_min = datetime.now() - timedelta(hours=1) # We should never get here
datetime_result_min_minute = datetime_result_min.minute
datetime_result_min_hour = datetime_result_min.hour
datetime_result_min_day = datetime_result_min.day
datetime_result_min_month = datetime_result_min.month
datetime_result_min_year = datetime_result_min.year
# if math.isnan(ssa):
# print(symbol, "ssa is null")
#
# if math.isnan(ssb):
# print(symbol, "ssb is null")
evol_co = round(((close - openp) / openp) * 100, 4)
scan = True
if interval_for_klinesT == Client.KLINE_INTERVAL_1MINUTE:
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year and data_hour == datetime_result_min_hour and data_minute >= datetime_result_min_minute
elif interval_for_klinesT == Client.KLINE_INTERVAL_3MINUTE:
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year and data_hour == datetime_result_min_hour and data_minute >= datetime_result_min_minute
elif interval_for_klinesT == Client.KLINE_INTERVAL_5MINUTE:
# print("comparing : " + str(data_hour) + " " + str(data_minute) + " to " + str(datetime_result_min_hour) + " " + str(datetime_result_min_minute))
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year and data_hour == datetime_result_min_hour and data_minute >= datetime_result_min_minute
elif interval_for_klinesT == Client.KLINE_INTERVAL_15MINUTE:
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year and data_hour == datetime_result_min_hour and data_minute >= datetime_result_min_minute
elif interval_for_klinesT == Client.KLINE_INTERVAL_30MINUTE:
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year and data_hour == datetime_result_min_hour and data_minute >= datetime_result_min_minute
elif interval_for_klinesT == Client.KLINE_INTERVAL_1HOUR:
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year and data_hour > datetime_result_min_hour # and data_minute >= datetime_result_min_minute
elif interval_for_klinesT == Client.KLINE_INTERVAL_2HOUR:
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year and data_hour > datetime_result_min_hour # and data_minute >= datetime_result_min_minute
elif interval_for_klinesT == Client.KLINE_INTERVAL_4HOUR:
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year and data_hour > datetime_result_min_hour # and data_minute >= datetime_result_min_minute
elif interval_for_klinesT == Client.KLINE_INTERVAL_6HOUR:
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year and data_hour > datetime_result_min_hour # and data_minute >= datetime_result_min_minute
elif interval_for_klinesT == Client.KLINE_INTERVAL_8HOUR:
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year and data_hour > datetime_result_min_hour # and data_minute >= datetime_result_min_minute
elif interval_for_klinesT == Client.KLINE_INTERVAL_12HOUR:
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year and data_hour > datetime_result_min_hour # and data_minute >= datetime_result_min_minute
elif interval_for_klinesT == Client.KLINE_INTERVAL_1DAY:
result_ok = data_day >= datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year # and data_hour >= datetime_result_min_hour
elif interval_for_klinesT == Client.KLINE_INTERVAL_3DAY:
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year # and data_hour >= datetime_result_min_hour
else:
result_ok = data_day == datetime_result_min_day and data_month == datetime_result_min_month and data_year == datetime_result_min_year and data_hour >= datetime_result_min_hour
# if symbol == "ETHUSDT":
# print ("ETHUSDT SSACHIKOU = " + str(ssachikou))
# print ("ETHUSDT SSBCHIKOU = " + str(ssbchikou))
if scan:
if result_ok:
#print(symbol, "result ok")
# if openp < ssb < close or openp > ssb and close > ssb:
# Define your own criterias for filtering assets on the line below
if scan_type == ScanType.UP:
condition_is_satisfied = close > ks and close > ts and close > ssa and close > ssb and cs > ssachikou and cs > ssbchikou and cs > tenkanchikou and cs > kijunchikou and cs > highchikou
#condition_is_satisfied = openp >= ksDaily and close >= ksDaily and openp > ksWeekly and close > ksWeekly and openp > ssaDaily and close > ssaDaily and openp > ssbDaily and close > ssbDaily and openp > ssaWeekly and close > ssaWeekly and openp > ssbWeekly and close > ssbWeekly
#if condition_is_satisfied is True:
# log_to_results(symbol + " " + "openp <= ksDaily and close <= ksDaily")
elif scan_type == ScanType.DOWN:
condition_is_satisfied = openp < ks and close < ks and close < ts and close < openp and close < ssa and close < ssb and cs < lowchikou and cs < kijunchikou and cs < ssbchikou and cs < ssachikou and cs < tenkanchikou
#log_to_results(symbol + " ssa daily = " + str(ssaDaily) + " ssb daily = " + str(ssbDaily) + " ks daily = " + str(ksDaily) + " ts daily = " + str(tsDaily))
#log_to_results(symbol + " ssa w = " + str(ssaWeekly) + " ssb w = " + str(ssbWeekly) + " ks w = " + str(ksWeekly) + " ts w = " + str(tsWeekly))
#log_to_results(symbol + " ssa w = " + str(ssaWeekly) + " ssb w = " + str(ssbWeekly) + " ks w = " + str(ksWeekly) + " ts w = " + str(tsWeekly))
if condition_is_satisfied:
cs_results = ""
str_result = str(timestamp) + " " + symbol + " " + symbol_type + " SSA=" + str(ssa) + " SSB=" + str(
ssb) + " KS=" + str(ks) + " TS=" + str(ts) + " O=" + str(
openp) + " H=" + str(high) + " L=" + str(low) + " SSBCS=" + str(
ssbchikou)# + " C=" + str(close) + " CS=" + str(cs) + " EVOL%=" + str(evol_co) # We don't concatenate the variable parts (for comparisons in list_results)
if not (str_result in list_results):
if not new_results_found:
new_results_found = True
results_count = results_count + 1
list_results.append(str_result)
#log_to_results(str_result)
str_result = cs_results + "\n" + str(results_count) + " " + str_result + " C=" + str(
close) + " CS=" + str(cs) + " EVOL(C/O)%=" + str(
evol_co) # We add the data with variable parts
tf = str(interval_for_klinesT)
#log_to_results("tf=" + tf)
interval = ""
if tf=="1m":
interval = 1
elif tf=="3m":
interval = 3
elif tf=="5m":
interval = 5
elif tf=="15m":
interval = 15
elif tf=="30m":
interval = 30
elif tf=="1h":
interval = 60
elif tf=="2h":
interval = 120
elif tf=="3h":
interval = 180
elif tf=="4h":
interval = 240
elif tf=="1d":
interval = 960
if scan_futures:
str_result += "\nhttps://tradingview.com/chart/?symbol=FTX%3A" + symbol.replace("-", "") + "&interval=" + str(interval)
else:
str_result += "\nhttps://tradingview.com/chart/?symbol=FTX%3A" + symbol.replace("/", "") + "&interval=" + str(interval)
print(str_result + "\n")
log_to_results(str(datetime.now()) + ":" + str_result + "\n")
dict_evol[symbol] = evol_co
else:
# if result_ok:
print(timestamp, symbol, "O", openp, "H", high, "L", low, "C", close, "SSA", ssa, "SSB", ssb, "KS", ks,
"TS", ts, "CS", cs, "SSB CS", ssbchikou)
str_result = str(timestamp) + " " + symbol + " O=" + str(openp) + " H=" + str(high) + " L=" + str(
low) + " C=" + str(close) + " SSA=" + str(
ssa) + " SSB=" + str(ssb) + " KS=" + str(ks) + " TS=" + str(ts) + " CS=" + str(cs) + " SSB CS=" + str(
ssbchikou) + " EVOL%(C/O)=" + str(evol_co)
log_to_results(str(datetime.now()) + ":" + str_result)
# Set loop_scan to True to scan in loop
loop_scan = False
maxthreads = 75
threadLimiter = threading.BoundedSemaphore(maxthreads)
def scan_one(symbol):
threadLimiter.acquire()
try:
execute_code(symbol)
finally:
threadLimiter.release()
threads = []
def main_thread(name):
global client, list_results, results_count, stop_thread, interval_for_klinesT
global new_results_found
log_to_evol(str(datetime.now()))
while not stop_thread:
# dict_evol = {}
# new_results_found = False
# info_binance = Client().get_all_tickers()
# print(info_binance)
# exit()
markets = requests.get('https://ftx.com/api/markets').json()
df = pd.DataFrame(markets['result'])
df.set_index('name')
# df = pd.DataFrame(info_binance)
# df.set_index('symbol')
for index, row in df.iterrows():
# symbol = row['symbol']
# symbol_type = "n/a" #row['type']
symbol = row['name']
symbol_type = row['type']
if scan_futures is True and not (symbol_type == "future"):
# print("this is not future, this is ", symbol_type)
continue
if scan_spot is True and not (symbol_type == "spot"):
# print("this is not future, this is ", symbol_type)
continue
# print(symbol, symbol_type)
# filtering symbols to scan here
# if not symbol.endswith('USDT') or symbol.endswith("DOWNUSDT") or symbol.endswith("UPUSDT"):
# continue
#if symbol != 'BTC/USD':
# continue
# else:
# print(symbol, "found")
# if scan_futures:
# print(symbol, "trying to scan in futures", end=" ")
# print(symbol, "trying to scan in futures")
# else:
# print(symbol, "trying to scan", end=" ")
# print(symbol, "trying to scan")
if symbol.endswith("BEAR/USD") or symbol.endswith("BULL/USD") or symbol.endswith("HEDGE/USD"):
continue
if symbol.endswith("BEAR/USDT") or symbol.endswith("BULL/USDT") or symbol.endswith("HEDGE/USDT"):
continue
if symbol.endswith("HALF/USDT") or symbol.endswith("HALF/USD"):
continue
disable = False
if disable == False:
try:
t = threading.Thread(target=scan_one, args=(symbol,))
threads.append(t)
t.start()
except requests.exceptions.ConnectionError:
continue
for tt in threads:
tt.join()
print(str(datetime.now()) + " All threads finished.")
log_to_results(str(datetime.now()) + " All threads finished.")
time.sleep(1)
if loop_scan is False:
stop_thread = True
else:
stop_thread = False
#####
if new_results_found:
log_to_results(100 * '*' + "\n")
new_dict = sorted(dict_evol.items(), key=lambda kv: (kv[1], kv[0]))
if new_dict:
print(str(datetime.now()) + " " + str(new_dict))
log_to_evol(str(datetime.now()) + " " + str(new_dict))
# Remove the line below to scan in loop
# stop_thread = True
x = threading.Thread(target=main_thread, args=(1,))
x.start()