mirror of
https://github.com/asavinov/intelligent-trading-bot.git
synced 2026-05-04 16:26:44 +00:00
223 lines
8.4 KiB
Python
223 lines
8.4 KiB
Python
import os
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import sys
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from datetime import timedelta, datetime
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import asyncio
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import requests
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from service.App import *
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from common.utils import *
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import logging
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log = logging.getLogger('notifier')
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transaction_file = Path("transactions.txt")
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async def notify_telegram():
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symbol = App.config["symbol"]
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status = App.status
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signal = App.signal
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signal_side = signal.get("side")
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close_price = signal.get('close_price')
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buy_score = signal.get('buy_score')
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sell_score = signal.get('sell_score')
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close_time = signal.get('close_time')
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model = App.config["signal_model"]
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buy_notify_threshold = model["buy_notify_threshold"]
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sell_notify_threshold = model["sell_notify_threshold"]
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buy_signal_threshold = model["buy_signal_threshold"]
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sell_signal_threshold = model["sell_signal_threshold"]
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trade_icon_step = model.get("trade_icon_step", 0)
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notify_frequency_minutes = model.get("notify_frequency_minutes", 1)
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# Crypto Currency Symbols: https://github.com/yonilevy/crypto-currency-symbols
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if symbol == "BTCUSDT":
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symbol_char = "₿"
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elif symbol == "ETHUSDT":
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symbol_char = "Ξ"
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else:
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symbol_char = symbol
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# Notification logic:
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# 1. Trade signal in the case it is suggested to really buy or sell: BUY or SELL and one corresponding score
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# 2. Notification signal simply to provide information (separate criteria): both scores
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# Icons:
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# DOWN: 📉, ⬇ ⬇️↘️🔽 🔴 (red), 🟥, ▼ (red), ↘ (red)
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# UP: 📈, ⬆, ⬆️ ↗️🔼 🟢 (green), 🟩, ▲ (green), ↗ (green)
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# ✅ 🔹 (blue) 📌 🔸 (orange)
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message = ""
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if signal_side == "BUY":
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score_steps = (np.abs(buy_score - buy_signal_threshold) // trade_icon_step) if trade_icon_step else 0
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message = "🟢"*int(score_steps+1) + f" *BUY: {symbol_char} {int(close_price):,} Score: {buy_score:+.2f}*"
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elif signal_side == "SELL":
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score_steps = (np.abs(sell_score - sell_signal_threshold) // trade_icon_step) if trade_icon_step else 0
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message = "🔴"*int(score_steps+1) + f" *SELL: {symbol_char} {int(close_price):,} Score: {-sell_score:+.2f}*"
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elif (close_time.minute % notify_frequency_minutes) == 0: # Info message with custom frequency
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if buy_score > sell_score:
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message = f"{symbol_char} {int(close_price):,} 📈{buy_score:+.2f}"
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else:
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message = f"{symbol_char} {int(close_price):,} 📉{-sell_score:+.2f}"
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message = message.replace("+", "%2B") # For Telegram to display plus sign
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if not message:
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return
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if buy_score < buy_notify_threshold and sell_score < sell_notify_threshold:
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return # Do not send notifications with low notification threshold (also no buy/sell notifications)
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bot_token = App.config["telegram_bot_token"]
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chat_id = App.config["telegram_chat_id"]
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#
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# Send signal
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#
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try:
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url = 'https://api.telegram.org/bot' + bot_token + '/sendMessage?chat_id=' + chat_id + '&parse_mode=markdown&text=' + message
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response = requests.get(url)
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response_json = response.json()
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if not response_json.get('ok'):
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log.error(f"Error sending notification.")
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except Exception as e:
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log.error(f"Error sending notification: {e}")
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#
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# Send transaction notification (if any)
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# Note that we assume that transactions may happen only if notifications are sent
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#
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transaction = await simulate_trade() # Here we check additional conditions (in addition to signaling)
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if not transaction:
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return
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profit, profit_percent, profit_descr, profit_percent_descr = await generate_transaction_stats()
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if transaction.get("status") == "SELL":
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message = "⚡💰 *SOLD: "
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elif transaction.get("status") == "BUY":
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message = "⚡💰 *BOUGHT: "
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else:
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log.error(f"ERROR: Should not happen")
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message += f" Profit: {profit_percent:.2f}% {profit:.2f}₮*"
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try:
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url = 'https://api.telegram.org/bot' + bot_token + '/sendMessage?chat_id=' + chat_id + '&parse_mode=markdown&text=' + message
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response = requests.get(url)
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response_json = response.json()
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if not response_json.get('ok'):
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log.error(f"Error sending notification.")
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except Exception as e:
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log.error(f"Error sending notification: {e}")
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#
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# Send stats about previous transactions (including this one)
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#
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if transaction.get("status") == "SELL":
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message = "↗ *LONG transactions stats (4 weeks)*\n"
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elif transaction.get("status") == "BUY":
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message = "↘ *SHORT transactions stats (4 weeks)*\n"
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else:
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log.error(f"ERROR: Should not happen")
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message += f"🔸sum={profit_percent_descr['count'] * profit_percent_descr['mean']:.2f}% 🔸count={int(profit_percent_descr['count'])}\n"
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message += f"🔸mean={profit_percent_descr['mean']:.2f}% 🔸std={profit_percent_descr['std']:.2f}%\n"
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message += f"🔸min={profit_percent_descr['min']:.2f}% 🔸median={profit_percent_descr['50%']:.2f}% 🔸max={profit_percent_descr['max']:.2f}%\n"
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try:
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url = 'https://api.telegram.org/bot' + bot_token + '/sendMessage?chat_id=' + chat_id + '&parse_mode=markdown&text=' + message
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response = requests.get(url)
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response_json = response.json()
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if not response_json.get('ok'):
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log.error(f"Error sending notification.")
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except Exception as e:
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log.error(f"Error sending notification: {e}")
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async def simulate_trade():
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symbol = App.config["symbol"]
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status = App.status
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signal = App.signal
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signal_side = signal.get("side")
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close_price = signal.get('close_price')
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buy_score = signal.get('buy_score')
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sell_score = signal.get('sell_score')
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close_time = signal.get('close_time')
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# Previous transaction: BUY (we are currently selling) or SELL (we are currently buying)
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t_status = App.transaction.get("status")
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t_price = App.transaction.get("price")
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if signal_side == "BUY" and (not t_status or t_status == "SELL"):
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profit = t_price - close_price if t_price else 0.0
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t_dict = dict(timestamp=str(close_time), price=close_price, profit=profit, status="BUY")
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elif signal_side == "SELL" and (not t_status or t_status == "BUY"):
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profit = close_price - t_price if t_price else 0.0
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t_dict = dict(timestamp=str(close_time), price=close_price, profit=profit, status="SELL")
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else:
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return None
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# Save this transaction
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App.transaction = t_dict
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with open(transaction_file, 'a+') as f:
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f.write(",".join([f"{v:.2f}" if isinstance(v, float) else str(v) for v in t_dict.values()]) + "\n")
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return t_dict
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async def generate_transaction_stats():
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"""Here we assume that the latest transaction is saved in the file and this function computes various properties."""
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df = pd.read_csv(transaction_file, parse_dates=[0], header=None, names=["timestamp", "close", "profit", "status"])
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mask = (df['timestamp'] >= (datetime.now() - timedelta(weeks=4)))
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df = df[max(mask.idxmax()-1, 0):] # We add one previous row to use the previous close
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df["prev_close"] = df["close"].shift()
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df["profit_percent"] = df.apply(lambda x: 100.0*x["profit"]/x["prev_close"], axis=1)
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df = df.iloc[1:] # Remove the first row which was added to compute relative profit
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long_df = df[df["status"] == "SELL"]
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short_df = df[df["status"] == "BUY"]
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#
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# Determine properties of the latest transaction
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#
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# Sample output:
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# BTC, LONG or SHORT
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# sell price 24,000 (now), buy price (datetime) 23,000
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# profit abs: 1,000.00,
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# profit rel: 3.21%
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last_transaction = df.iloc[-1]
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transaction_dt = last_transaction["timestamp"]
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transaction_type = last_transaction["status"]
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profit = last_transaction["profit"]
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profit_percent = last_transaction["profit_percent"]
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#
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# Properties of last period of trade
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#
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if transaction_type == "SELL":
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df2 = long_df
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elif transaction_type == "BUY":
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df2 = short_df
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# Sample output for abs profit
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# sum 1,200.00, mean 400.00, median 450.00, std 250.00, min -300.0, max 1200.00
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profit_sum = df2["profit"].sum()
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profit_descr = df2["profit"].describe() # count, mean, std, min, 50% max
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profit_percent_sum = df2["profit_percent"].sum()
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profit_percent_descr = df2["profit_percent"].describe() # count, mean, std, min, 50% max
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return profit, profit_percent, profit_descr, profit_percent_descr
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if __name__ == '__main__':
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pass
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