from datetime import timedelta
from time import sleep
import psycopg2
from pandas import DataFrame
import pandas as pd
#либы для работы с exmo
import sys
import http.client
import urllib
import json
import hashlib
import hmac
import time
PSQL_heroku_keys = access.PSQL_heroku_keys()
#текущая метка времени с минутой часами
def now_str():
now = datetime.now()
now_str = str(now.year) + str(
now.month if now.month >= 10 else '0' + str(now.month)
) + str(now.day if now.day >= 10 else '0' + str(now.day)) + ' ' + str(
now.hour if now.hour >= 10 else '0' + str(now.hour)) + str(
now.minute if now.minute >= 10 else '0' +
str(now.minute)) + str(now.second if now.second >= 10 else '0' +
str(now.second))
return now_str
def get_tv_main():
check_at = now_str()
PSQL_heroku_keys = access.PSQL_heroku_keys()
interest_coins = ['ETHUSDT', 'LTCUSDT', 'EOSUSDT', 'XRPUSDT', 'DASHUSDT']
url = 'https://scanner.tradingview.com/crypto/scan'
strong_buy_binance = {
"filter": [{
"left": "name",
"operation": "nempty"
}, {
"left": "exchange",
"operation": "equal",
"right": "BINANCE"
}, {
"left": "Recommend.All",
"operation": "nequal",
"right": 0.5
}, {
"left": "Recommend.All",
"operation": "in_range",
"right": [0.5, 1]
}],
"options": {
"lang": "en"
},
"symbols": {
"query": {
"types": []
},
"tickers": []
},
"columns": [
"name", "close", "change", "change_abs", "high", "low", "volume",
"Recommend.All", "exchange", "description", "name", "subtype",
"update_mode", "pricescale", "minmov", "fractional", "minmove2"
],
"sort": {
"sortBy": "name",
"sortOrder": "asc"
},
"range": [0, 150]
}
strong_sell_binance = {
"filter": [{
"left": "name",
"operation": "nempty"
}, {
"left": "exchange",
"operation": "equal",
"right": "BINANCE"
}, {
"left": "Recommend.All",
"operation": "nequal",
"right": -0.5
}, {
"left": "Recommend.All",
"operation": "in_range",
"right": [-1, -0.5]
}],
"options": {
"lang": "en"
},
"symbols": {
"query": {
"types": []
},
"tickers": []
},
"columns": [
"name", "close", "change", "change_abs", "high", "low", "volume",
"Recommend.All", "exchange", "description", "name", "subtype",
"update_mode", "pricescale", "minmov", "fractional", "minmove2"
],
"sort": {
"sortBy": "name",
"sortOrder": "asc"
},
"range": [0, 150]
}
sell_binane = {
"filter": [{
"left": "name",
"operation": "nempty"
}, {
"left": "exchange",
"operation": "equal",
"right": "BINANCE"
}, {
"left": "Recommend.All",
"operation": "nequal",
"right": 0
}, {
"left": "Recommend.All",
"operation": "in_range",
"right": [-0.5, 0]
}],
"options": {
"lang": "en"
},
"symbols": {
"query": {
"types": []
},
"tickers": []
},
"columns": [
"name", "close", "change", "change_abs", "high", "low", "volume",
"Recommend.All", "exchange", "description", "name", "subtype",
"update_mode", "pricescale", "minmov", "fractional", "minmove2"
],
"sort": {
"sortBy": "name",
"sortOrder": "asc"
},
"range": [0, 150]
}
binance_strong_buy_str = requests.post(url, json=strong_buy_binance)
binance_strong_sell_str = requests.post(url, json=strong_sell_binance)
binance_sell_str = requests.post(url, json=sell_binane)
strong_sell_dict = json.loads(binance_strong_sell_str.text)
strong_buy_dict = json.loads(binance_strong_buy_str.text)
sell_dict = json.loads(binance_sell_str.text)
name_arr = []
rating_arr = []
prc_change_arr = []
abs_change_arr = []
high_change_arr = []
low_change_arr = []
for coin_dict in strong_sell_dict['data']:
coin_pair = coin_dict['d'][0]
if coin_pair in interest_coins:
name = coin_dict['d'][0]
prc_change = coin_dict['d'][2]
abs_change = coin_dict['d'][3]
high_change = coin_dict['d'][4]
low_change = coin_dict['d'][5]
name_arr.append(name)
rating_arr.append('strong_sell')
prc_change_arr.append(prc_change)
abs_change_arr.append(abs_change)
high_change_arr.append(high_change)
low_change_arr.append(low_change)
for coin_dict in strong_buy_dict['data']:
coin_pair = coin_dict['d'][0]
if coin_pair in interest_coins:
name = coin_dict['d'][0]
prc_change = coin_dict['d'][2]
abs_change = coin_dict['d'][3]
high_change = coin_dict['d'][4]
low_change = coin_dict['d'][5]
name_arr.append(name)
rating_arr.append('strong_buy')
prc_change_arr.append(prc_change)
abs_change_arr.append(abs_change)
high_change_arr.append(high_change)
low_change_arr.append(low_change)
for coin_dict in sell_dict['data']:
coin_pair = coin_dict['d'][0]
if coin_pair in interest_coins:
name = coin_dict['d'][0]
prc_change = coin_dict['d'][2]
abs_change = coin_dict['d'][3]
high_change = coin_dict['d'][4]
low_change = coin_dict['d'][5]
name_arr.append(name)
rating_arr.append('sell')
prc_change_arr.append(prc_change)
abs_change_arr.append(abs_change)
high_change_arr.append(high_change)
low_change_arr.append(low_change)
traiding_view_main_info = pd.DataFrame({
'par_name': name_arr,
'rating': rating_arr,
'prc_change': prc_change_arr,
'abs_change': abs_change_arr,
'high_change': high_change_arr,
'low_change': low_change_arr
})
if traiding_view_main_info.shape[0] > 0:
#создаем подключение к PSQL
conn = psycopg2.connect(
"dbname='%(dbname)s' port='%(port)s' user='******' host='%(host)s' password='******'"
% PSQL_heroku_keys)
# создаем запрос
cur = conn.cursor()
for i, row in traiding_view_main_info.iterrows():
insert_dict = dict(row)
par_name = insert_dict['par_name']
rating = insert_dict['rating']
prc_change = insert_dict['prc_change']
abs_change = insert_dict['abs_change']
high_change = insert_dict['high_change']
low_change = insert_dict['low_change']
#создаем sql иньекцию
sql_tv = ''.join([
"insert into exmo_info.traiding_view_main (par_name , check_at , rating , prc_change , abs_change , high_change ,low_change) ",
"VALUES ('", par_name, "','", check_at, "','", rating, "',",
str(prc_change), ",",
str(abs_change), ",",
str(high_change), ",",
str(low_change), ")"
])
#создаем запись в exmo_info.ticker
cur.execute(sql_tv)
conn.commit()
sleep(0.1)
cur.close()
#закрываем подключение
conn.close()
return 200
def main():
check_at = now_str()
PSQL_heroku_keys = access.PSQL_heroku_keys()
#создаем подключение к PSQL
conn = psycopg2.connect(
"dbname='%(dbname)s' port='%(port)s' user='******' host='%(host)s' password='******'"
% PSQL_heroku_keys)
# создаем запрос
cur = conn.cursor()
cur_trade_state = ' '.join([
"select mt.par_name", "\n"
",mt.max_check_at", "\n"
",round(mt_info.deal_last / mt_info.deal_24h_avg , 2) as cur_state",
"\n"
",mt_info.deal_last", "\n"
",case when round(mt_info.deal_last / mt_info.deal_24h_avg , 3) <= 0.98 then 1 else 0 end as factor1_buy",
"\n"
", case when round(mt_info.deal_last / mt_info.deal_24h_avg , 3) <= 0.98 then mt_info.deal_last * 1.015 else null end as factor1_sellprice",
"\n"
"from ", "\n"
"--информация по максимальному состоянию", "\n"
"(", "\n"
"select par_name", "\n"
",max(check_at) as max_check_at", "\n"
"from exmo_info.ticker", "\n"
"group by par_name", "\n"
") as mt", "\n"
"left join exmo_info.ticker as mt_info on mt.par_name = mt_info.par_name",
"\n"
"and mt.max_check_at = mt_info.check_at"
])
probability_of_states = ' '.join([
"select hy.par_name",
"\n",
",hy.cur_state",
"\n",
",round(sum(hy.is_price_up) / sum(1.) , 2) as cur_state_price_up_prob",
"\n",
",round(sum(hy.is_factor1_up) / sum(1.) , 2) as cur_state_factor_up_prob",
"\n",
",sum(1) as cnt_cases",
"\n",
"from(",
"\n",
"select p.par_name",
"\n",
",round(p.deal_last / p.deal_24h_avg , 2) as cur_state",
"\n",
",p.check_at",
"\n",
",p.check_at + interval '2' day as end_date_order",
"\n",
",p.deal_last",
"\n",
",sum(case when pf.deal_last >= p.deal_last * 1.015 then 1 else 0 end) as cnt_price_up",
"\n",
",sum(case when round(pf.deal_last / pf.deal_24h_avg , 3) >= round(p.deal_last / p.deal_24h_avg , 3) + 0.015 then 1 else 0 end) as cnt_factor1_up",
"\n",
",max(case when pf.deal_last >= p.deal_last * 1.015 then 1 else 0 end) as is_price_up",
"\n",
",max(case when round(pf.deal_last / pf.deal_24h_avg , 3) >= round(p.deal_last / p.deal_24h_avg , 3) + 0.015 then 1 else 0 end) as is_factor1_up",
"\n",
",max(pf.deal_last) as max_future_deal",
"\n",
",sum(1) as cnt_trys",
"\n",
"from exmo_info.ticker as p",
"\n",
"left join exmo_info.ticker as pf on p.par_name = pf.par_name",
"\n",
"and pf.check_at > p.check_at",
"\n",
"and pf.check_at < p.check_at + interval '2' day ",
"\n",
"where p.check_at > now() - interval '180' day",
"\n",
"and p.check_at < now() - interval '2' day",
"\n",
"group by 1 , 2 , 3 , 4 , 5",
"\n",
"order by p.check_at",
"\n",
") as hy",
"\n",
"group by 1 , 2 ",
"\n",
"order by 1 , 2",
"\n",
])
cur.execute(cur_trade_state)
df_cur_state = pd.DataFrame(cur.fetchall(),
columns=[desc[0] for desc in cur.description])
cur.execute(probability_of_states)
df_state_probs = pd.DataFrame(
cur.fetchall(), columns=[desc[0] for desc in cur.description])
cur.close()
#закрываем подключение
conn.close()
#смотрим априорную вероятность увеличения стоимости в течение 2-х дней на 1.5%
cur_state_probability = pd.merge(df_cur_state,
df_state_probs,
how='left',
left_on=['par_name', 'cur_state'],
right_on=['par_name', 'cur_state'])
cur_state_probability.fillna('unknown', inplace=True)
result_to_send = cur_state_probability.to_json(orient='records')
url = 'https://fin-budg-bot.herokuapp.com/external_receive'
try:
r = requests.post(url, json=result_to_send)
except:
r = 'bad'
print(r)
return 200