def fast_refresh():
df = pd.DataFrame()
dddd = pd.DataFrame()
dfs = pd.DataFrame()
final = pd.DataFrame()
dfs2 = pd.DataFrame()
result = pd.DataFrame()
filter = pd.DataFrame()
alfa_bd = pd.DataFrame()
alfa_bd2 = pd.DataFrame()
hot_bd2 = pd.DataFrame()
live_bd2 = pd.DataFrame()
live_bd = pd.DataFrame()
hot_bd = pd.DataFrame()
alfa_bd = pd.read_csv(main_path_data + "\\alfa_bd_PU.csv", thousands=',')
alfa_bd2 = pd.read_csv(main_path_data + "\\alfa_bd_PB.csv", thousands=',')
hot_bd2 = pd.read_csv(main_path_data + "\\hot_bd_PB.csv", thousands=',')
live_bd2 = pd.read_csv(main_path_data + "\\live_bd_PB.csv", thousands=',')
try:
with open(main_path_data + "\\live_bd_PU.csv", 'r') as f:
live_bd = pd.read_csv(f, thousands=',')
f.close()
except Exception as e:
with open(main_path_data + "\\live_bd_PU.csv", 'r') as f:
live_bd = pd.read_csv(f, thousands=',')
f.close()
try:
with open(main_path_data + "\\hot_bd_PU.csv", 'r') as f:
hot_bd = pd.read_csv(f, thousands=',')
f.close()
except Exception as e:
print("EXCEPT to open HOT")
time.sleep(0.1)
with open(main_path_data + "\\hot_bd_PU.csv", 'r') as f:
hot_bd = pd.read_csv(f, thousands=',')
f.close()
pass
new_regims_f = open(second_path_data + "\\new_regims.json", 'r')
new_regims = json.load(new_regims_f)
new_regims_f.close()
bal = pd.read_csv(second_path_data + "\\balance.csv")
balances2 = bal.to_json(orient='records')
balances = json.loads(balances2)
new_com = open(second_path_data + "\\commis.json", 'r')
com = json.load(new_com)
new_com.close()
frames = [alfa_bd, hot_bd, live_bd, alfa_bd2, hot_bd2, live_bd2]
df = pd.concat(frames, ignore_index=True)
filter_a = df[df['birga'] == 'alfa'].index
filter_l = df[df['birga'] == 'live'].index
filter_h = df[df['birga'] == 'hot'].index
a = com['main']["alfa"]
l = com['main']["live"]
h = com['main']["hot"]
df.loc[(filter_a), 'Com'] = float(a)
df.loc[(filter_h), 'Com'] = float(h)
df.loc[(filter_l), 'Com'] = float(l)
dfs2 = pd.merge(df,
df,
left_on=df['valout'],
right_on=df['valin'],
how='outer')
dfs2['rates_x'] = dfs2['rates_x'].replace(',', '', regex=True).apply(
pd.to_numeric, errors='coerce')
dfs2['rates_y'] = dfs2['rates_y'].replace(',', '', regex=True).apply(
pd.to_numeric, errors='coerce')
dfs2['volume_x'] = dfs2['volume_x'].replace(',', '', regex=True).apply(
pd.to_numeric, errors='coerce')
dfs2['volume_y'] = dfs2['volume_y'].replace(',', '', regex=True).apply(
pd.to_numeric, errors='coerce')
dfs2.drop(['key_0'], axis=1, inplace=True)
dfs2['rates_y'] = dfs2['rates_y'].map('{:,.10f}'.format)
dfs2['rates_x'] = dfs2['rates_x'].map('{:,.10f}'.format)
############### Main dataframe with all data ####################
result = dfs2[(dfs2['valin_x'] == dfs2['valout_y'])]
usdt = dfs2[(dfs2['valin_x'] == 'USD') & (dfs2['valout_y'] == 'USDT')]
usd = dfs2[(dfs2['valin_x'] == 'USDT') & (dfs2['valout_y'] == 'USD')]
final = result.append([usdt, usd])
final.reset_index(inplace=True, drop=True)
final.reset_index(level=0, inplace=True)
filter = final[(final['birga_x'] == final['birga_y'])
& (final['rates_x'] < final['rates_y']) &
(final['valin_x'] == final['valout_y'])]
final = final.drop(filter['index'], axis=0)
final['rates_x'] = final['rates_x'].replace(',', '', regex=True).apply(
pd.to_numeric, errors='coerce')
final['rates_y'] = final['rates_y'].replace(',', '', regex=True).apply(
pd.to_numeric, errors='coerce')
final['volume_x'] = final['volume_x'].replace(',', '', regex=True).apply(
pd.to_numeric, errors='coerce')
final['volume_y'] = final['volume_y'].replace(',', '', regex=True).apply(
pd.to_numeric, errors='coerce')
final['Com_x'] = final['Com_x'].replace(',', '',
regex=True).apply(pd.to_numeric,
errors='coerce')
final['Com_y'] = final['Com_y'].replace(',', '',
regex=True).apply(pd.to_numeric,
errors='coerce')
for i in balances:
balanc_a = final[(final['birga_x'] == 'alfa')
& (final['valin_x'] == i['Valuta'])].index
balanc_h = final[(final['birga_x'] == 'hot')
& (final['valin_x'] == i['Valuta'])].index
balanc_l = final[(final['birga_x'] == 'live')
& (final['valin_x'] == i['Valuta'])].index
a_b = i['alfa']
h_b = i['hot']
l_b = i['live']
final.loc[(balanc_a), 'Bal1'] = a_b / final.loc[(balanc_a), 'rates_x']
final.loc[(balanc_h), 'Bal1'] = h_b / final.loc[
(balanc_h), 'rates_x'] - (h_b / final.loc[(balanc_h), 'rates_x'] *
final.loc[(balanc_h), 'Com_x'] / 100)
final.loc[(balanc_l), 'Bal1'] = l_b / final.loc[
(balanc_l), 'rates_x'] - (l_b / final.loc[(balanc_l), 'rates_x'] *
final.loc[(balanc_l), 'Com_x'] / 100)
balanc_a2 = final[(final['birga_y'] == 'alfa')
& (final['valin_y'] == i['Valuta'])].index
balanc_h2 = final[(final['birga_y'] == 'hot')
& (final['valin_y'] == i['Valuta'])].index
balanc_l2 = final[(final['birga_y'] == 'live')
& (final['valin_y'] == i['Valuta'])].index
final.loc[(balanc_a2), 'Bal2'] = a_b
final.loc[(balanc_h2), 'Bal2'] = h_b
final.loc[(balanc_l2), 'Bal2'] = l_b
reg2 = final
final.loc[:, "volume"] = final[['volume_x', 'volume_y', 'Bal2',
'Bal1']].min(axis=1)
final['volume'] = final['volume'].map('{:,.10f}'.format)
final['volume'] = final['volume'].replace(',', '', regex=True).apply(
pd.to_numeric, errors='coerce')
final.loc[:, 'Vol1'] = final['rates_x'] * final['volume'] + (
final['rates_x'] * final['volume'] * final['Com_x'] / 100)
final.loc[:, 'Vol2'] = final['volume']
final.loc[:, 'Vol3'] = final['volume']
final.loc[:, 'Vol4'] = final['rates_y'] * final['volume'] - (
final['rates_y'] * final['volume'] * final['Com_y'] / 100)
fil_a = final[final['birga_x'] == 'alfa'].index
final.loc[(fil_a), 'Vol3'] = final.loc[(fil_a), 'Vol2'] - (final.loc[
(fil_a), 'Vol2'] * final.loc[(fil_a), 'Com_x'] / 100)
final.loc[(fil_a),
'Vol1'] = final.loc[(fil_a), 'rates_x'] * final.loc[(fil_a),
'volume']
final.loc[(fil_a), 'Vol4'] = final.loc[(fil_a), 'rates_y'] * final.loc[
(fil_a), 'Vol3'] - (final.loc[(fil_a), 'rates_y'] *
final.loc[(fil_a), 'Vol3'] * final['Com_y'] / 100)
final['Vol1'] = final['Vol1'].map('{:,.10f}'.format)
final['Vol2'] = final['Vol2'].map('{:,.10f}'.format)
final['Vol3'] = final['Vol3'].map('{:,.10f}'.format)
final['Vol4'] = final['Vol4'].map('{:,.10f}'.format)
final['Vol1'] = final['Vol1'].replace(',', '',
regex=True).apply(pd.to_numeric,
errors='coerce')
final['Vol2'] = final['Vol2'].replace(',', '',
regex=True).apply(pd.to_numeric,
errors='coerce')
final['Vol3'] = final['Vol3'].replace(',', '',
regex=True).apply(pd.to_numeric,
errors='coerce')
final['Vol4'] = final['Vol4'].replace(',', '',
regex=True).apply(pd.to_numeric,
errors='coerce')
final.loc[:, 'profit'] = final['Vol4'] - final['Vol1']
final.loc[:, 'percent'] = final['profit'] / final['Vol1'] * 100
final['profit'] = final['profit'].replace(',', '', regex=True).apply(
pd.to_numeric, errors='coerce')
final['percent'] = final['percent'].replace(',', '', regex=True).apply(
pd.to_numeric, errors='coerce')
dddd = final.dropna()
final = dddd
final = final[final['direction_y'] != 'buy']
#################################################################################################
reg2.loc[:, "volume"] = reg2[['volume_y', 'Bal2', 'Bal1']].min(axis=1)
reg2.loc[:, 'Vol1'] = reg2['rates_x'] * reg2['volume'] + (
reg2['rates_x'] * reg2['volume'] * reg2['Com_x'] / 100)
reg2.loc[:, 'Vol2'] = reg2['volume']
reg2.loc[:, 'Vol3'] = reg2['volume']
reg2.loc[:, 'Vol4'] = reg2['rates_y'] * reg2['volume'] - (
reg2['rates_y'] * reg2['volume'] * reg2['Com_y'] / 100)
fil_a = reg2[reg2['birga_x'] == 'alfa'].index
reg2.loc[(fil_a), 'Vol3'] = reg2.loc[(fil_a), 'Vol2'] - (reg2.loc[
(fil_a), 'Vol2'] * reg2.loc[(fil_a), 'Com_x'] / 100)
reg2.loc[(fil_a),
'Vol1'] = reg2.loc[(fil_a), 'rates_x'] * reg2.loc[(fil_a),
'volume']
reg2.loc[(fil_a), 'Vol4'] = reg2.loc[(fil_a), 'rates_y'] * reg2.loc[
(fil_a), 'Vol3'] - (reg2.loc[(fil_a), 'rates_y'] *
reg2.loc[(fil_a), 'Vol3'] * reg2['Com_y'] / 100)
reg2.loc[:, 'profit'] = reg2['Vol4'] - reg2['Vol1']
reg2.loc[:, 'percent'] = reg2['profit'] / reg2['Vol1'] * 100
reg2['profit'] = reg2['profit'].apply(pd.to_numeric, errors='coerce')
reg2['percent'] = reg2['percent'].apply(pd.to_numeric, errors='coerce')
reg2 = reg2[reg2['direction_y'] != 'buy']
reg2.to_csv(main_path_data + "\\vilki_all.csv", header=True, index=False)
#################################################################################################
def regim_filter():
fids = pd.DataFrame()
for k, v in new_regims.items():
dft = final[(final["birga_x"] == v["birga1"])
& (final["birga_y"] == v["birga2"]) &
(final["valin_x"] == v["val1"]) &
(final["valout_x"] == v["val2"]) &
(final["valout_y"] == v["val3"]) &
(final["percent"] > float(v["profit"])) &
(final["Vol2"] > float(v["order"]))]
per = (float(v["per"]) / 100)
dft.loc[:, 'Vol1'] = dft['Vol1'] * per
dft.loc[:, 'Vol2'] = dft['Vol2'] * per
dft.loc[:, 'Vol3'] = dft['Vol3'] * per
dft.loc[:, 'Vol4'] = dft['Vol4'] * per
dft = dft[(dft["Vol3"] > float(v["order"]))]
now = dt.datetime.now()
timerr = now.strftime("%H:%M:%S")
dft['avtomat'] = v["avtomat"]
dft['regim'] = k
dft['timed'] = timerr
dft = dft.sort_values(by=['Vol2'], ascending=False)
dft = dft.head(1)
fids = pd.concat([dft, fids], ignore_index=True, join='outer')
return fids
dfs = regim_filter()
dfs = dfs.reindex(columns=[
'regim', 'timed', 'birga_x', 'birga_y', 'valin_x', 'valout_x',
'valout_y', 'rates_x', 'rates_y', 'Vol1', 'Vol2', 'Vol3', 'Vol4',
'percent', 'avtomat'
])
dfs.columns = [
'regim', 'timed', 'b1', 'b2', 'val1', 'val2', 'val3', 'kurs1', 'kurs2',
'Vol1', 'Vol2', 'Vol3', 'Vol4', 'profit', 'Go'
]
dfs['profit'] = dfs['profit'].map('{:,.2f}'.format)
dfs.index = range(len(dfs))
dfs = dfs.sort_values(by=['profit'], ascending=False)
if dfs.shape[0] > 0:
dfs.to_csv(second_path_data + "\\vilki.csv", header=True, index=False)
filter1 = dfs[(dfs['Go'] == 'on')]
if filter1.shape[0] > 0:
NewOrders.order(filter1.iloc[0]['regim'], filter1.iloc[0]['b1'],
filter1.iloc[0]['b2'], filter1.iloc[0]['Vol1'],
filter1.iloc[0]['val1'],
str(filter1.iloc[0]['kurs1']),
filter1.iloc[0]['Vol2'], filter1.iloc[0]['val2'],
filter1.iloc[0]['Vol3'], filter1.iloc[0]['val2'],
str(filter1.iloc[0]['kurs2']),
filter1.iloc[0]['Vol4'], filter1.iloc[0]['val3'])
Balance.NewBalance()
time.sleep(1.1)
else:
dw2 = {
'regim': 'пусто',
'timed': 'пусто',
'b1': 'пусто',
'b2': 'пусто',
'val1': 'пусто',
'val2': 'пусто',
'val3': 'пусто',
'kurs1': 0,
'kurs2': 0,
'Vol1': 0,
'Vol2': 0,
'Vol3': 0,
'Vol4': 0,
'profit': 0,
'Go': 'пусто',
}
df2 = pd.DataFrame(data=dw2, index=[0])
df2.drop_duplicates(inplace=True)
df2.to_csv(second_path_data + "\\vilki.csv", header=True, index=False)
return
def regim():
all_vilki2 = pd.read_csv(second_path_data + "\\vilki2_all.csv")
fids = pd.DataFrame()
for k, v in regims2.items():
if v['avtomat'] == 'on':
dft = vilki_all[(vilki_all["birga_x"] == v["birga1"])
& (vilki_all["birga_y"] == v["birga2"]) &
(vilki_all["valin_x"] == v["val1"]) &
(vilki_all["valout_x"] == v["val2"]) &
(vilki_all["Vol3"] > 0) &
(vilki_all["volume"] > float(v["order"])) &
(vilki_all["valout_y"] == v["val3"])]
if dft.shape[0] > 0:
dft.loc[:, 'rates_x'] = dft['rates_x'].map('{:,.9f}'.format)
dft.loc[:, 'rates_y'] = dft['rates_y'].map('{:,.9f}'.format)
bir = v["birga1"]
dft = dft[dft['percent'] == dft['percent'].max()]
dft.loc[:, 'regim'] = k
dft.loc[:, 'min_A'] = v["order"]
if v["val1"] == "BTC":
if bir == 'hot':
dft.loc[:, 'new_kurs'] = hs_PU2.iloc[0]['rates']
dft.loc[:, 'min_kurs'] = hot2.iloc[0]['rates']
elif bir == 'live':
dft.loc[:, 'new_kurs'] = ls_PU2.iloc[0]['rates']
dft.loc[:, 'min_kurs'] = live2.iloc[0]['rates']
elif bir == 'alfa':
dft.loc[:, 'new_kurs'] = as_PU2.iloc[0]['rates']
dft.loc[:, 'min_kurs'] = alfa2.iloc[0]['rates']
else:
if bir == 'hot':
dft.loc[:, 'new_kurs'] = hs_PU.iloc[0]['rates']
dft.loc[:, 'min_kurs'] = hot.iloc[0]['rates']
elif bir == 'live':
dft.loc[:, 'new_kurs'] = ls_PU.iloc[0]['rates']
dft.loc[:, 'min_kurs'] = live.iloc[0]['rates']
elif bir == 'alfa':
dft.loc[:, 'new_kurs'] = as_PU.iloc[0]['rates']
dft.loc[:, 'min_kurs'] = alfa.iloc[0]['rates']
tickers_all = [
'BTC/USD', 'PZM/USD', 'PZM/USDT', 'ETH/USD', 'ETH/USDT',
'PZM/BTC', 'ETH/BTC'
]
parametr1 = "{}/{}".format(v["val1"], v["val2"])
parametr2 = "{}/{}".format(v["val2"], v["val1"])
for i in tickers_all:
if i == parametr1:
para = i
elif i == parametr2:
para = i
dft.loc[:, 'My_kurs'] = float(dft.iloc[0]['new_kurs']) + (
float(rools2[bir]['amount_precision'][para]) *
int(v['per']))
dft.loc[:, '0_kurs'] = float(dft.iloc[0]['rates_y']) - (
float(dft.iloc[0]['rates_y']) * float(v["profit"]) / 100)
dft['Vol3'] = dft['Vol3'].apply(pd.to_numeric, errors='coerce')
dft['Vol4'] = dft['Vol4'].apply(pd.to_numeric, errors='coerce')
dft['My_kurs'] = dft['My_kurs'].apply(pd.to_numeric,
errors='coerce')
if bir == 'alfa':
dft.loc[:, 'Vol2'] = dft.iloc[0]['Vol3'] + (
dft.iloc[0]['Vol3'] * dft.iloc[0]['Com_x'] / 100)
dft.loc[:, 'Vol1'] = dft.iloc[0]['Vol2'] * float(
dft.iloc[0]['My_kurs'])
else:
dft.loc[:, 'Vol2'] = dft.iloc[0]['Vol3']
dft.loc[:, 'Vol1'] = dft.iloc[0]['Vol3'] * dft.iloc[0][
'My_kurs'] + (dft.iloc[0]['Vol3'] *
dft.iloc[0]['My_kurs'] *
dft.iloc[0]['Com_x'] / 100)
dft.loc[:, 'prof'] = dft['Vol4'] - dft['Vol1']
dft.loc[:, 'per'] = dft['prof'] / dft['Vol1'] * 100
dft.loc[:, 'New_per'] = float(v["profit"])
dft.drop(['index'], axis=1, inplace=True)
df_vilki = vilki2[vilki2["regim"] == int(k)]
if df_vilki.shape[0] > 0:
my_order = int(df_vilki.iloc[0]['order_id'])
my_dict = finished_orders[df_vilki.iloc[0]['birga_x']]
if str(my_order) in my_dict:
if my_dict[str(my_order)] >= float(
df_vilki.iloc[0]['min_A']):
check_vol = float(
df_vilki.iloc[0]['Vol2']) - float(
my_dict[str(my_order)])
if check_vol <= 0:
birga = df_vilki.iloc[0]['birga_y']
val3 = df_vilki.iloc[0]['valin_y']
val4 = df_vilki.iloc[0]['valout_y']
rate2 = str(df_vilki.iloc[0]['rates_y'])
val3_vol = df_vilki.iloc[0]['Vol3']
if birga == 'alfa':
reponse = Reg2_Orders.alfa(
val3, val4, rate2, val3_vol)
elif birga == 'live':
reponse = Reg2_Orders.live(
val3, val4, rate2, val3_vol)
elif birga == 'hot':
reponse = Reg2_Orders.hot(
val3, val4, rate2, val3_vol)
else:
reponse = "No such BIRGA"
now = dt.datetime.now()
timer2 = now.strftime("%Y-%m-%d %H:%M:%S")
profit = (df_vilki.iloc[0]['Vol4'] -
df_vilki.iloc[0]['Vol1']
) / df_vilki.iloc[0]['Vol1'] * 100
dw2 = {
'regim': df_vilki.iloc[0]['regim'],
'timed': [timer2],
'b1': [df_vilki.iloc[0]['birga_x']],
'b2': [df_vilki.iloc[0]['birga_y']],
'val1': [df_vilki.iloc[0]['valin_x']],
'val2': [df_vilki.iloc[0]['valout_x']],
'val3': [df_vilki.iloc[0]['valout_y']],
'kurs1': [df_vilki.iloc[0]['rates_x']],
'kurs2': [df_vilki.iloc[0]['rates_y']],
'Vol1': [df_vilki.iloc[0]['Vol1']],
'Vol2': [df_vilki.iloc[0]['Vol2']],
'Vol3': [df_vilki.iloc[0]['Vol3']],
'Vol4': [df_vilki.iloc[0]['Vol4']],
'profit': [profit],
'rep1': [reponse],
'rep2': [my_order],
}
df2 = pd.DataFrame(data=dw2, index=[0])
df2.drop_duplicates(inplace=True)
all_vilki2 = pd.concat([dft, all_vilki2],
ignore_index=True,
join='outer')
nl = '\n'
bot_sendtext(
f" ЕСТЬ ВИЛКА: {nl} РЕЖИМ : {df_vilki.iloc[0]['regim']} {nl} {df_vilki.iloc[0]['birga_x']} / {df_vilki.iloc[0]['birga_y']} {nl} "
f"{reponse} / {my_order} {nl} {df_vilki.iloc[0]['valin_x']} -> {df_vilki.iloc[0]['valout_x']} -> {df_vilki.iloc[0]['valout_y']} {nl} "
f"PROFIT: {profit} {nl} ")
vilki2.drop(
vilki2.index[vilki2["regim"] == int(k)],
inplace=True)
############### delete from finished json #############################
a_file = open(
main_path_data + "\\finished_orders.json",
"r")
json_object = json.load(a_file)
a_file.close()
del json_object[df_vilki.iloc[0]['birga_x']][
str(my_order)]
a_file = open(
main_path_data + "\\finished_orders.json",
"w")
json.dump(json_object, a_file)
a_file.close()
Balance.NewBalance()
elif check_vol > 0 and check_vol < float(
df_vilki.iloc[0]['min_A']):
birga = df_vilki.iloc[0]['birga_x']
val3 = df_vilki.iloc[0]['valin_y']
val4 = df_vilki.iloc[0]['valout_y']
rate2 = df_vilki.iloc[0]['rates_y']
birga2 = df_vilki.iloc[0]['birga_y']
nl = '\n'
if birga == 'alfa':
val1_vol = (my_dict[str(my_order)] *
df_vilki.iloc[0]['My_kurs'])
val2_vol = my_dict[str(my_order)]
val3_vol = my_dict[str(my_order)] - (
my_dict[str(my_order)] *
df_vilki.iloc[0]['Com_x'] / 100)
val4_vol = (
val3_vol * df_vilki.iloc[0]['rates_y']
) - (val3_vol * df_vilki.iloc[0]['rates_y']
* df_vilki.iloc[0]['Com_y'] / 100)
else:
val1_vol = (
my_dict[str(my_order)] *
df_vilki.iloc[0]['My_kurs']) + (
my_dict[str(my_order)] *
df_vilki.iloc[0]['rates_x'] *
df_vilki.iloc[0]['Com_x'] / 100)
val2_vol = my_dict[str(my_order)]
val3_vol = my_dict[str(my_order)]
val4_vol = (
val3_vol * df_vilki.iloc[0]['rates_y']
) - (val3_vol * df_vilki.iloc[0]['rates_y']
* df_vilki.iloc[0]['Com_y'] / 100)
if birga2 == 'alfa':
reponse = Reg2_Orders.alfa(
val3, val4, str(rate2), val3_vol)
elif birga2 == 'live':
reponse = Reg2_Orders.live(
val3, val4, str(rate2), val3_vol)
elif birga2 == 'hot':
reponse = Reg2_Orders.hot(
val3, val4, str(rate2), val3_vol)
else:
reponse = "No such BIRGA"
if birga == 'alfa':
reponse2 = Reg2_Orders.alfa_cancel(
str(my_order))
elif birga == 'live':
reponse2 = Reg2_Orders.live_cancel(
df_vilki.iloc[0]['valin_x'],
df_vilki.iloc[0]['valout_x'],
str(my_order))
elif birga == 'hot':
reponse2 = Reg2_Orders.hot_cancel(
df_vilki.iloc[0]['valin_x'],
df_vilki.iloc[0]['valout_x'],
str(my_order))
else:
reponse2 = "No such BIRGA"
now = dt.datetime.now()
timer2 = now.strftime("%Y-%m-%d %H:%M:%S")
profit = (val4_vol - val1_vol) / val1_vol * 100
dw2 = {
'regim': df_vilki.iloc[0]['regim'],
'timed': [timer2],
'b1': [df_vilki.iloc[0]['birga_x']],
'b2': [df_vilki.iloc[0]['birga_y']],
'val1': [df_vilki.iloc[0]['valin_x']],
'val2': [df_vilki.iloc[0]['valout_x']],
'val3': [df_vilki.iloc[0]['valout_y']],
'kurs1': [df_vilki.iloc[0]['My_kurs']],
'kurs2': [df_vilki.iloc[0]['rates_y']],
'Vol1': [val1_vol],
'Vol2': [val2_vol],
'Vol3': [val3_vol],
'Vol4': [val4_vol],
'profit': [profit],
'rep1': [reponse],
'rep2': [my_order],
}
df2 = pd.DataFrame(data=dw2, index=[0])
df2.drop_duplicates(inplace=True)
all_vilki2 = pd.concat([dft, all_vilki2],
ignore_index=True,
join='outer')
bot_sendtext(
f" ЕСТЬ ВИЛКА: {nl} РЕЖИМ : {df_vilki.iloc[0]['regim']} {nl} {df_vilki.iloc[0]['birga_x']} / {df_vilki.iloc[0]['birga_y']} {nl} "
f"{reponse} / {my_order} {nl} {df_vilki.iloc[0]['valin_x']} -> {df_vilki.iloc[0]['valout_x']} -> {df_vilki.iloc[0]['valout_y']} {nl} "
f"{df_vilki.iloc[0]['birga_x']} / {df_vilki.iloc[0]['rates_y']} {nl} "
f"{df_vilki.iloc[0]['Vol2']} / {df_vilki.iloc[0]['Vol3']} {nl} "
f"PROFIT: {profit} {nl} ")
vilki2.drop(
vilki2.index[vilki2["regim"] == int(k)],
inplace=True)
############### delete from finished json #############################
a_file32 = open(
main_path_data + "\\finished_orders.json",
"r")
json_object = json.load(a_file32)
a_file32.close()
del json_object[df_vilki.iloc[0]['birga_x']][
str(my_order)]
a_file23 = open(
main_path_data + "\\finished_orders.json",
"w")
json.dump(json_object, a_file23)
a_file23.close()
Balance.NewBalance()
else:
del my_dict[str(my_order)]
f33 = open(
main_path_data + "\\finished_orders.json", "w")
json.dump(my_dict, f33)
f33.close()
else:
print("###### dft ######", '\n', dft, '\n')
adf = dft[
(dft['per'] >= dft['New_per']) &
# (float(dft['My_kurs']) <= float(df_vilki.iloc[0]['My_kurs'])) &
(float(dft['rates_y']) >= float(df_vilki.iloc[0]
['rates_y'])) &
(dft['Vol2'] >= float(df_vilki.iloc[0]['Vol2']))]
# print( "PERC :", dft.iloc[0]['per'], "___###___", "PERC NEW :",dft.iloc[0]['New_per'])
# print("My_kurs :", dft.iloc[0]['My_kurs'], "___###___", "My_kurs OLD :", df_vilki.iloc[0]['My_kurs'])
# print("rates_y :", dft.iloc[0]['rates_y'], "___###___", "rates_y OLD :", df_vilki.iloc[0]['rates_y'])
# print( "Vol2 :", dft.iloc[0]['Vol2'], "___###___", "Vol2 OLD :",df_vilki.iloc[0]['Vol2'])
print('\n', "###### adf ######", '\n', adf, '\n')
if adf.shape[0] > 0:
pass
else:
bir = df_vilki.iloc[0]['birga_x']
if bir == 'alfa':
reponse2 = Reg2_Orders.alfa_cancel(
df_vilki.iloc[0]['order_id'])
elif bir == 'live':
reponse2 = Reg2_Orders.live_cancel(
df_vilki.iloc[0]['valin_x'],
df_vilki.iloc[0]['valout_x'],
df_vilki.iloc[0]['order_id'])
elif bir == 'hot':
reponse2 = Reg2_Orders.hot_cancel(
df_vilki.iloc[0]['valin_x'],
df_vilki.iloc[0]['valout_x'],
df_vilki.iloc[0]['order_id'])
else:
reponse2 = "No such BIRGA"
vilki2.drop(
vilki2.index[vilki2["regim"] == int(k)],
inplace=True)
############### delete from finished json #############################
a_file = open(
main_path_data + "\\finished_orders.json", "r")
json_object = json.load(a_file)
a_file.close()
# print("######",str(int(df_vilki.iloc[0]['order_id'])))
if str(int(df_vilki.iloc[0]
['order_id'])) in json_object[bir]:
del json_object[bir][str(
int(df_vilki.iloc[0]['order_id']))]
a_file = open(
main_path_data + "\\finished_orders.json", "w")
json.dump(json_object, a_file)
a_file.close()
else:
# print("###### dft ######", '\n', dft, '\n')
dft = dft[(dft["per"] > float(dft.iloc[0]["New_per"]))
& (dft["Vol2"] > float(dft.iloc[0]["min_A"])) &
(dft['My_kurs'] < float(
dft.iloc[0]["min_kurs"]))]
# print(dft)
if dft.shape[0] > 0:
val1 = dft.iloc[0]['valin_x']
val2 = dft.iloc[0]['valout_x']
rate1 = str(dft.iloc[0]['My_kurs'])
val2_vol = dft.iloc[0]['Vol2']
if bir == 'alfa':
reponse = Reg2_Orders.alfa(val1, val2, rate1,
val2_vol)
elif bir == 'live':
reponse = Reg2_Orders.live(val1, val2, rate1,
val2_vol)
elif bir == 'hot':
reponse = Reg2_Orders.hot(val1, val2, rate1,
val2_vol)
else:
reponse = "No such BIRGA"
pass
dft.loc[:, 'order_id'] = reponse
vilki2 = pd.concat([dft, vilki2],
ignore_index=True,
join='outer')
fids = pd.concat([dft, fids], ignore_index=True, join='outer')
vilki2.to_csv(second_path_data + "\\vilki2.csv", header=True, index=False)
all_vilki2.to_csv(second_path_data + "\\vilki2_all.csv",
header=True,
index=False)