Create TRADING BOT #111
Create TRADING BOT #111
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An AI automated crypto trading bot for python-binance
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`[10/31, 12:56 PM] ISLAM KATO: [10/30, 4:52 PM] ISLAM KATO: crypto_bot/ load_dotenv() API_KEY = os.getenv("BINANCE_API_KEY") Set up logginglogging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') def run_bot(symbol, timeframe, amount, testnet=True): if name == "main": [10/30, 4:55 PM] ISLAM KATO: import pandas as pd def get_historical_data(client, symbol, timeframe): def should_buy(df): def should_sell(df): [10/30, 4:55 PM] ISLAM KATO: import logging Define stop-loss and take-profit percentagesSTOP_LOSS_PCT = 0.02 # 2% def check_stop_loss(df, symbol): def check_take_profit(df, symbol): [10/30, 4:55 PM] ISLAM KATO: import pandas as pd Assuming get_historical_data() from strategy.py is availabledef run_backtest(): if name == 'main': def get_historical_data(client, symbol, timeframe, limit=200): def should_buy(df): def should_sell(df): [10/31, 12:44 PM] Aslam: https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage load_dotenv() TELEGRAM_BOT_TOKEN = os.getenv("TELEGRAM_BOT_TOKEN") def send_telegram_message(message): [10/31, 12:45 PM] Aslam: from notifier import send_telegram_message TRADE_LOG_FILE = "user_data/trade_history.csv" def log_trade(symbol, side, price, quantity): [10/31, 12:49 PM] Aslam: import pandas as pd def calculate_daily_profit(): [10/31, 12:49 PM] Aslam: from trade_logger import log_trade def schedule_daily_report(): if name == "main": |
1 participant
An AI automated crypto trading bot for python-binance
[10/31, 12:56 PM] ISLAM KATO: [10/30, 4:52 PM] ISLAM KATO: crypto_bot/
├── config.py
├── main.py
├── requirements.txt
├── strategy.py
└── user_data/
└── api_keys.txt
[10/30, 4:52 PM] ISLAM KATO: python-binance
pandas
numpy
python-dotenv
backtesting
ta
[10/30, 4:53 PM] ISLAM KATO: pip install -r requirements.txt
[10/30, 4:53 PM] ISLAM KATO: BINANCE_API_KEY="u9281AAiMfFXviHjdCkOeqX8OZNblciGAHtFryj58Ehnq8OX9hGAJ9hCGLlLMzhU"
BINANCE_API_SECRET="LCCczPRxccBMgDajWikdZfTD4tZ7NmD1BwneSbZvCOWNROmZ4s4P9QNd9OacGh4z"
[10/30, 4:53 PM] ISLAM KATO: import os
from dotenv import load_dotenv
load_dotenv()
API_KEY = os.getenv("BINANCE_API_KEY")
API_SECRET = os.getenv("BINANCE_API_SECRET")
[10/30, 4:54 PM] ISLAM KATO: import time
from binance.client import Client
from config import API_KEY, API_SECRET
from strategy import should_buy, should_sell, get_historical_data
from risk_management import check_stop_loss, check_take_profit
import logging
Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
def run_bot(symbol, timeframe, amount, testnet=True):
"""
Main function to run the trading bot.
if name == "main":
# Parameters for the bot
TRADING_SYMBOL = 'BTCUSDT'
TIMEFRAME = '5m'
TRADE_AMOUNT = 0.001 # Quantity of the asset
USE_TESTNET = True # Set to False for live trading
[10/30, 4:55 PM] ISLAM KATO: import pandas as pd
import ta
def get_historical_data(client, symbol, timeframe):
"""
Fetches historical candlestick data from Binance.
"""
try:
candles = client.get_klines(symbol=symbol, interval=timeframe)
df = pd.DataFrame(candles, columns=[
'timestamp', 'open', 'high', 'low', 'close', 'volume',
'close_time', 'quote_asset_volume', 'number_of_trades',
'taker_buy_base_asset_volume', 'taker_buy_quote_asset_volume', 'ignore'
])
df['close'] = pd.to_numeric(df['close'])
return df
except Exception as e:
logging.error(f"Failed to fetch historical data: {e}")
return None
def should_buy(df):
"""
Simple RSI-based buy strategy: buy when RSI crosses below 30.
"""
if df is None or df.empty:
return False
def should_sell(df):
"""
Simple RSI-based sell strategy: sell when RSI crosses above 70.
"""
if df is None or df.empty:
return False
[10/30, 4:55 PM] ISLAM KATO: import logging
Define stop-loss and take-profit percentages
STOP_LOSS_PCT = 0.02 # 2%
TAKE_PROFIT_PCT = 0.04 # 4%
def check_stop_loss(df, symbol):
"""
Checks for a stop-loss condition.
(This is a simplified example. A real bot needs to track entry price.)
"""
# For a live bot, you must track the entry price of your open position.
# For this example, we assume we just bought at the previous candle's close.
last_price = df['close'].iloc[-1]
entry_price = df['close'].iloc[-2]
def check_take_profit(df, symbol):
"""
Checks for a take-profit condition.
"""
last_price = df['close'].iloc[-1]
entry_price = df['close'].iloc[-2]
[10/30, 4:55 PM] ISLAM KATO: import pandas as pd
from backtesting import Backtest, Strategy
from backtesting.lib import crossover
import ta
Assuming get_historical_data() from strategy.py is available
def run_backtest():
"""
Runs a backtest on the defined trading strategy.
"""
# You will need to download historical data for your backtest
# For example: df = get_historical_data(client, 'BTCUSDT', Client.KLINE_INTERVAL_5MINUTE)
# For this example, let's load a sample dataset
df = pd.read_csv("BTCUSDT_5m_sample.csv", index_col=0, parse_dates=True)
if name == 'main':
run_backtest()
[10/30, 4:56 PM] ISLAM KATO: git clone your_repo_url
[10/30, 4:56 PM] ISLAM KATO: pm2 start main.py --name crypto_bot
pm2 logs crypto_bot
[10/31, 12:56 PM] ISLAM KATO: [10/31, 12:42 PM] Aslam: import pandas as pd
import ta
import logging
def get_historical_data(client, symbol, timeframe, limit=200):
"""
Fetches historical candlestick data from Binance.
"""
try:
candles = client.get_klines(symbol=symbol, interval=timeframe, limit=limit)
df = pd.DataFrame(candles, columns=[
'timestamp', 'open', 'high', 'low', 'close', 'volume',
'close_time', 'quote_asset_volume', 'number_of_trades',
'taker_buy_base_asset_volume', 'taker_buy_quote_asset_volume', 'ignore'
])
df['close'] = pd.to_numeric(df['close'])
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
def should_buy(df):
"""
RSI + EMA strategy:
- RSI crosses below 30 (oversold)
- Price is above the EMA (uptrend confirmation)
"""
if df is None or len(df) < 2:
return False
def should_sell(df):
"""
RSI + EMA strategy:
- RSI crosses above 70 (overbought)
- Price falls below the EMA (downtrend confirmation)
"""
if df is None or len(df) < 2:
return False
[10/31, 12:44 PM] Aslam: https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage
[10/31, 12:44 PM] Aslam: import os
import requests
from dotenv import load_dotenv
import logging
load_dotenv()
TELEGRAM_BOT_TOKEN = os.getenv("TELEGRAM_BOT_TOKEN")
TELEGRAM_CHAT_ID = os.getenv("TELEGRAM_CHAT_ID")
def send_telegram_message(message):
"""
Sends a Telegram message using your bot token and chat ID.
"""
if not TELEGRAM_BOT_TOKEN or not TELEGRAM_CHAT_ID:
logging.warning("Telegram credentials not found. Skipping notification.")
return
[10/31, 12:45 PM] Aslam: from notifier import send_telegram_message⚠️ STOP-LOSS triggered for {symbol}")
[10/31, 12:45 PM] Aslam: if should_buy(df):
logging.info(f"Buy signal detected for {symbol}. Placing buy order...")
order = client.create_market_buy_order(symbol, quantity=amount)
logging.info(f"Buy order placed: {order}")
send_telegram_message(f"✅ BUY ORDER placed for {symbol} — amount: {amount}")
open_position = True
[10/31, 12:46 PM] Aslam: elif check_take_profit(df, symbol):
logging.info(f"Take-profit triggered for {symbol}.")
order = client.create_market_sell_order(symbol, quantity=amount)
send_telegram_message(f"🎯 TAKE-PROFIT triggered for {symbol}")
open_position = False
elif check_stop_loss(df, symbol):
logging.warning(f"Stop-loss triggered for {symbol}.")
order = client.create_market_sell_order(symbol, quantity=amount)
send_telegram_message(f"
open_position = False
elif should_sell(df):
logging.info(f"Sell signal detected for {symbol}.")
order = client.create_market_sell_order(symbol, quantity=amount)
send_telegram_message(f"📉 SELL ORDER placed for {symbol}")
open_position = False
[10/31, 12:46 PM] Aslam: send_telegram_message(f"❌ ERROR in main loop: {e}")
[10/31, 12:48 PM] Aslam: crypto_bot/
├── trade_logger.py # Records all trades (buy/sell/time/price)
├── profit_reporter.py # Calculates daily P/L and sends a Telegram summary
[10/31, 12:48 PM] Aslam: import csv
import os
from datetime import datetime
TRADE_LOG_FILE = "user_data/trade_history.csv"
def log_trade(symbol, side, price, quantity):
"""
Logs each executed trade (buy/sell) to a CSV file.
"""
os.makedirs(os.path.dirname(TRADE_LOG_FILE), exist_ok=True)
file_exists = os.path.isfile(TRADE_LOG_FILE)
[10/31, 12:49 PM] Aslam: import pandas as pd
from datetime import datetime, timedelta
from notifier import send_telegram_message
from trade_logger import TRADE_LOG_FILE
def calculate_daily_profit():
"""
Calculates total profit/loss from trade history for the last 24 hours.
"""
try:
df = pd.read_csv(TRADE_LOG_FILE, parse_dates=['datetime'])
except FileNotFoundError:
send_telegram_message("📊 No trade history found yet.")
return
[10/31, 12:49 PM] Aslam: from trade_logger import log_trade
[10/31, 12:50 PM] Aslam: if should_buy(df):
logging.info(f"Buy signal detected for {symbol}. Placing buy order...")
order = client.create_market_buy_order(symbol, quantity=amount)
last_price = df['close'].iloc[-1]
log_trade(symbol, "BUY", last_price, amount)
send_telegram_message(f"✅ BUY {symbol} at {last_price} for {amount}")
open_position = True
[10/31, 12:51 PM] Aslam: elif should_sell(df):
logging.info(f"Sell signal detected for {symbol}. Placing sell order...")
order = client.create_market_sell_order(symbol, quantity=amount)
last_price = df['close'].iloc[-1]
log_trade(symbol, "SELL", last_price, amount)
send_telegram_message(f"📉 SELL {symbol} at {last_price} for {amount}")
open_position = False
[10/31, 12:52 PM] Aslam: pm2 start profit_reporter.py --name daily_report --cron "0 21 * * *"
[10/31, 12:52 PM] Aslam: import threading
from profit_reporter import calculate_daily_profit
def schedule_daily_report():
calculate_daily_profit()
threading.Timer(86400, schedule_daily_report).start() # 24h interval
if name == "main":
# existing bot setup...
schedule_daily_report()
run_bot(TRADING_SYMBOL, TIMEFRAME, TRADE_AMOUNT, USE_TESTNET)