Added some additional features

supertrend.py

@@ -1,37 +1,71 @@
+import os
 import ccxt
 import config
 import schedule
+import numpy as np
 import pandas as pd
-pd.set_option('display.max_rows', None)
-
 import warnings
+import pprint
+import json
+import time
+from datetime import datetime, date
+
+pd.set_option('display.max_rows', None)
 warnings.filterwarnings('ignore')
+start_st = datetime.fromtimestamp(time.time()).strftime('%Y%m%d-%H%M%S')
 
-import numpy as np
-from datetime import datetime
-import time
 
-exchange = ccxt.binanceus({
-    "apiKey": config.BINANCE_API_KEY,
-    "secret": config.BINANCE_SECRET_KEY
+# Set your global variables
+symbol = 'ETH/USDT'
+timeframe = '30m'
+limit = 100
+in_position = False
+quantity_buy_sell = 0.1
+
+
+exchange_id = 'binance'
+exchange_class = getattr(ccxt, exchange_id)
+exchange = exchange_class({
+    'apiKey': config.BINANCE_API_KEY,
+    'secret': config.BINANCE_SECRET_KEY,
+    'timeout': 50000,
+    'enableRateLimit': True,
 })
 
-def tr(data):
-    data['previous_close'] = data['close'].shift(1)
-    data['high-low'] = abs(data['high'] - data['low'])
-    data['high-pc'] = abs(data['high'] - data['previous_close'])
-    data['low-pc'] = abs(data['low'] - data['previous_close'])
 
-    tr = data[['high-low', 'high-pc', 'low-pc']].max(axis=1)
+def log_write(msg, start_st=start_st, df=False, json=False, print_it=True):
+    st = datetime.fromtimestamp(time.time()).strftime('%Y%m%d-%H%M%S')
+    path = os.path.dirname(os.path.realpath(__file__))
+    log = open('{}/supertrend-{}.log'.format(path, start_st), 'a')
+
+    if not df and not json:
+        entry = f'{st}: {msg}\n'
+    elif df:
+        entry = f'{st}\n{msg}\n'
+    elif json:
+    	entry = '{}\n{}\n'.format(st, pprint.pprint(json.loads(msg)))
+
+    log.write(entry)
 
+    if print_it:
+    	print(entry.strip())
+
+
+def tr(df):
+    df['previous_close'] = df['close'].shift(1)
+    df['high_minus_low'] = abs(df['high'] - df['low'])
+    df['high_minus_pc'] = abs(df['high'] - df['previous_close'])
+    df['low_minus_pc'] = abs(df['low'] - df['previous_close'])
+    tr = df[['high_minus_low', 'high_minus_pc', 'low_minus_pc']].max(axis=1)
     return tr
 
-def atr(data, period):
-    data['tr'] = tr(data)
-    atr = data['tr'].rolling(period).mean()
 
+def atr(df, period):
+    df['tr'] = tr(df)
+    atr = df['tr'].rolling(period).mean()
     return atr
 
+
 def supertrend(df, period=7, atr_multiplier=3):
     hl2 = (df['high'] + df['low']) / 2
     df['atr'] = atr(df, period)
@@ -41,65 +75,80 @@ def supertrend(df, period=7, atr_multiplier=3):
 
     for current in range(1, len(df.index)):
         previous = current - 1
-
         if df['close'][current] > df['upperband'][previous]:
             df['in_uptrend'][current] = True
         elif df['close'][current] < df['lowerband'][previous]:
             df['in_uptrend'][current] = False
         else:
             df['in_uptrend'][current] = df['in_uptrend'][previous]
-
             if df['in_uptrend'][current] and df['lowerband'][current] < df['lowerband'][previous]:
                 df['lowerband'][current] = df['lowerband'][previous]
-
             if not df['in_uptrend'][current] and df['upperband'][current] > df['upperband'][previous]:
                 df['upperband'][current] = df['upperband'][previous]
-
     return df
 
 
-in_position = False
-
 def check_buy_sell_signals(df):
-    global in_position
+    global in_position, symbol, quantity_buy_sell, log
+
+    log_write('Checking for buy and sell signals')
 
-    print("checking for buy and sell signals")
-    print(df.tail(5))
     last_row_index = len(df.index) - 1
     previous_row_index = last_row_index - 1
 
     if not df['in_uptrend'][previous_row_index] and df['in_uptrend'][last_row_index]:
-        print("changed to uptrend, buy")
         if not in_position:
-            order = exchange.create_market_buy_order('ETH/USD', 0.05)
-            print(order)
+            log_write('Changed to uptrend, buy')
+            order = exchange.create_market_buy_order(symbol, quantity_buy_sell)
+            log_write(order)
             in_position = True
+
+            print(df.tail(5))
+            log_write(df.tail(5), df=True)
+
         else:
-            print("already in position, nothing to do")
-    
+            log_write('Already in position, nothing to do')
+
     if df['in_uptrend'][previous_row_index] and not df['in_uptrend'][last_row_index]:
         if in_position:
-            print("changed to downtrend, sell")
-            order = exchange.create_market_sell_order('ETH/USD', 0.05)
+            log_write('Changed to downtrend, sell')
+            order = exchange.create_market_sell_order(symbol, quantity_buy_sell)
             print(order)
+            log_write(order)
             in_position = False
+
+            print(df.tail(5))
+            log_write(df.tail(5), df=True)
+
         else:
-            print("You aren't in position, nothing to sell")
+            log_write('You aren\'t in position, nothing to sell')
+
 
 def run_bot():
-    print(f"Fetching new bars for {datetime.now().isoformat()}")
-    bars = exchange.fetch_ohlcv('ETH/USDT', timeframe='1m', limit=100)
-    df = pd.DataFrame(bars[:-1], columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
-    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+    global symbol, timeframe, limit, log
 
-    supertrend_data = supertrend(df)
-
-    check_buy_sell_signals(supertrend_data)
+    msg = f'symbol: {symbol}, timeframe: {timeframe}, limit: {limit}, in_position: {in_position}, quantity_buy_sell: {quantity_buy_sell}'
+    log_write(msg)
 
+    log_write('Fetching new bars')
 
-schedule.every(10).seconds.do(run_bot)
+    df = None
+    try:
+        bars = exchange.fetch_ohlcv(symbol, timeframe=timeframe, limit=limit)
+        df = pd.DataFrame(bars[:-1], columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
+        df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+    except:
+        pass
+
+    if df is None:
+        log_write('Error fetching ohlcv-data from exchange')
+    else:
+        supertrend_data = supertrend(df)
+        check_buy_sell_signals(supertrend_data)
 
 
+schedule.every(10).seconds.do(run_bot)
+
 while True:
     schedule.run_pending()
     time.sleep(1)