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create.py
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# Copyright (c) 2021 Alberto Rota
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import datetime
import pandas as pd
import warnings
import mplfinance as mplf
detail = {'1year':'1w','1month':'1d','1week':'4h','1day':'15m','4hour':'3m','1hour':'1m'}
def chart(client, asset, begin):
# Get data from Binance
exchange = asset.upper()+"USDT"
#############################################################################
klines = client.get_historical_klines(
symbol=exchange,
start_str=begin,
interval=detail[begin]
)
#############################################################################
TIMESHIFT=7.2e6; # 2h ahead of UTC
df = pd.DataFrame(
klines,
columns=['dateTime', 'open', 'high', 'low', 'close','volume',
'closeTime', 'quoteAssetVolume', 'numberOfTrades',
'takerBuyBaseVol', 'takerBuyQuoteVol', 'ignore']
)
# Removes unnecessary columns
# df.drop(columns=['volume','closeTime', 'quoteAssetVolume', 'numberOfTrades',
# 'takerBuyBaseVol', 'takerBuyQuoteVol', 'ignore'],inplace=True)
df.dateTime = pd.to_datetime(df.dateTime+TIMESHIFT, unit='ms')
df.closeTime = pd.to_datetime(df.closeTime+TIMESHIFT, unit='ms')
for var in df.columns[1:]: #["open","high","low","close","volume"]:
df[var] = pd.to_numeric(df[var], downcast="float")
df.set_index('dateTime', inplace=True)
figpath = "savedfigs/"+exchange+"_"+df.index[-1].strftime("%d_%m_%Y__%H_%M.png")
with warnings.catch_warnings():
warnings.simplefilter("ignore")
mplf.plot(
df,
type='candle',
style='binance',
title="\n\n"+exchange.replace('USDT','/USDT'),
savefig=dict(fname=figpath,dpi=100,pad_inches=0),
)
variation=(df['close'].iloc[-1]-df['open'].iloc[0])/df['open'].iloc[0]*100
variation_str = "{:.2f}".format(variation)
currentprice_str = "{:.2f}".format(df['close'].iloc[-1])
if variation>=0:
cptn = f"🟩{exchange.replace('USDT','').upper()}: <b> {currentprice_str}$ | +{variation_str}%</b> 🟩 [since {begin}]"
else:
cptn = f"🟥{exchange.replace('USDT','').upper()}: <i> {currentprice_str}$ | {variation_str}%</i> 🟥 [since {begin}]"
cptn=cptn+"\n-------------------------------------------------------------------\n"+report(client,asset)
return figpath, cptn
def report(client, asset):
exchange = asset.upper()+"USDT"
#############################################################################
klines = client.get_historical_klines(
symbol=exchange,
start_str='1year',
interval='1w'
)
#############################################################################
df1y = pd.DataFrame(
klines,
columns=['dateTime', 'open', 'high', 'low', 'close','volume',
'closeTime', 'quoteAssetVolume', 'numberOfTrades', 'takerBuyBaseVol', 'takerBuyQuoteVol', 'ignore'])
klines = client.get_historical_klines(
symbol=exchange,
start_str='1week',
interval='1h'
)
df1w = pd.DataFrame(
klines,
columns=['dateTime', 'open', 'high', 'low', 'close','volume',
'closeTime', 'quoteAssetVolume', 'numberOfTrades', 'takerBuyBaseVol', 'takerBuyQuoteVol', 'ignore'])
for var in ["open","high","low","close"]:
df1y[var] = pd.to_numeric(df1y[var], downcast="float")
df1w[var] = pd.to_numeric(df1w[var], downcast="float")
variations = {
"1 year": (df1y['close'].iloc[-1]-df1y['open'].iloc[0])/df1y['open'].iloc[0]*100,
"1 month": (df1y['close'].iloc[-1]-df1y['open'].iloc[-4])/df1y['open'].iloc[-4]*100,
"1 week": (df1y['close'].iloc[-1]-df1y['open'].iloc[-2])/df1y['open'].iloc[-2]*100,
"24 hours": (df1w['close'].iloc[-1]-df1w['open'].iloc[-25])/df1w['open'].iloc[-25]*100,
"1 hour": (df1w['close'].iloc[-1]-df1w['open'].iloc[-2])/df1w['open'].iloc[-2]*100,
}
prices = {
"1 year": df1y['open'].iloc[0],
"1 month": df1y['open'].iloc[-4],
"1 week": df1y['open'].iloc[-2],
"24 hours": df1w['open'].iloc[-25],
"1 hour": df1w['open'].iloc[-2],
}
message = ""
for k in variations.keys():
variation_str = "{:.2f}".format(variations[k])
price_str = "{:.2f}".format(prices[k])
if variations[k]>=0:
message=message+f"🟩 <b>{k} | +{variation_str}% [was {price_str}$]</b> \n"
else:
message=message+ f"🟥 <i>{k} | {variation_str}% [was {price_str}$]</i> \n"
return message
def profits(client,user_id):
USD2EUR = client.get_avg_price(symbol="EURUSDT")
wallet = f"data/wallets/{user_id}.csv"
try:
df = pd.read_csv(wallet)
except:
print(">> "+datetime.datetime.now().strftime(f"[%d/%m/%Y-%H:%M:%S]-{user_id}: ")+
f" Requested a wallet that doesn't exist")
return "You have to set up a wallet first! Do it from '💰Set Wallet'"
message = "<b>Your Wallet:</b>\n"
message=message+"-------------------------------------------------------------------\n"
total_profit=0
invested=0;
for index, row in df.iterrows():
#############################################################################
current_price = client.get_avg_price(symbol=f"{row['asset'].upper()}USDT")
#############################################################################
variation = (float(current_price['price'])-row['buy_price'])/row['buy_price']
profitdollar = row['buy_price']*row['quantity']*variation
profiteur=profitdollar/float(USD2EUR['price'])
invested = invested+row['buy_price']*row['quantity']
variation_str = "{:.2f}".format(variation*100)
profitdollar_str = "{:.2f}".format(profitdollar)
profiteur_str = "{:.2f}".format(profiteur)
if variation>=0:
message=message+f"🟩 {row['asset'].upper()} | <b> +{variation_str}%</b> | <b>+{profitdollar_str}</b>$ | <b>+{profiteur_str}€ </b>\n"
else:
message=message+f"🟥 {row['asset'].upper()} | <i> {variation_str}%</i> | <i>{profitdollar_str}</i>$ | <i>{profiteur_str}€ </i>\n"
total_profit+=profitdollar
investedeur=invested/float(USD2EUR['price'])
invested_str = "{:.2f}".format(invested)
investedeur_str = "{:.2f}".format(investedeur)
current=invested+total_profit
current_str = "{:.2f}".format(current)
currenteur=current/float(USD2EUR['price'])
currenteur_str = "{:.2f}".format(currenteur)
message=message+"-------------------------------------------------------------------\n"
message=message+f"Invested Capital: <i>{invested_str}$ </i>| <i>{investedeur_str}€ </i>\n"
message=message+"-------------------------------------------------------------------\n"
message=message+f"Current Capital: <b>{current_str}$ </b>| <b>{currenteur_str}€ </b>\n"
message=message+"-------------------------------------------------------------------\n"
# message=message+"------------------------------------\n"
totalprofituer=total_profit/float(USD2EUR['price'])
variation = (currenteur-investedeur)/investedeur
variation_str = "{:.2f}".format(variation*100)
totalprofit_str = "{:.2f}".format(total_profit)
totalprofiteur_str = "{:.2f}".format(totalprofituer)
if total_profit>=0:
message=message+f"🟩 <b>Total</b>: <b>+{totalprofit_str}$</b> | <b>{variation_str}%</b> | <b>+{totalprofiteur_str}€</b>\n"
else:
message=message+f"🟥 <b>Total</b>: <i>{totalprofit_str}$</i> | <i>{variation_str}%</i> | <i>{totalprofiteur_str}€</i>\n"
return message