customized backtesting

Author: GitHub-Valie

backtesting/custom backtest/backtesting.py

@@ -0,0 +1,156 @@
+# Pseudo code
+
+# Import data from "data" folder
+import json
+from pprint import pprint
+from datetime import datetime
+import pandas as pd
+from ta.trend import EMAIndicator
+import matplotlib.pyplot as plt
+
+# Load json file
+with open('data\BTCUSDT_1m_future.json') as json_file:
+    data = json.load(json_file)
+    # pprint(data[-10:]) # Explore data
+    # pprint(data[:10])
+
+print(
+    'Start date: ', datetime.fromtimestamp(
+        float(data[0][0]) / 1000
+    ),
+    '\nEnd date: ', datetime.fromtimestamp(
+        float(data[-1][0]) / 1000
+    ),
+    '\nLength: ', len(data)
+)
+
+# Process json file
+closes = []
+for candle in data:
+    closes.append(float(candle[4]))
+
+print(
+    'Max: ', max(closes),
+    '\nMin: ', min(closes)
+)
+
+df = pd.DataFrame(
+    data = closes,
+    columns = ['close']
+)
+
+# Build technical indicators
+emas_Used = [3, 5, 8, 10, 12, 15, 30, 35, 40, 45, 50, 60]
+
+for x in emas_Used:
+    ema = x
+    indicator_ema = EMAIndicator(
+        close=df['close'],
+        window=x,
+        fillna=False
+    )
+    df['ema_' + str(ema)] = indicator_ema.ema_indicator()
+# print(df.tail())
+
+# Strategy
+position = 0
+open_long = []
+open_short = []
+close_long = []
+close_short = []
+pnl = []
+fees = []
+
+for i in df.index:
+    c_min = min(
+        df['ema_3'][i], 
+        df['ema_5'][i], 
+        df['ema_8'][i], 
+        df['ema_10'][i], 
+        df['ema_12'][i],
+        df['ema_15'][i],
+    )
+    c_max = max(
+        df['ema_30'][i], 
+        df['ema_35'][i], 
+        df['ema_40'][i], 
+        df['ema_45'][i], 
+        df['ema_50'][i],
+        df['ema_60'][i],
+    )
+    price = df['close'][i]
+
+    if position == 0:
+
+        # Entry point when short term emas > long term emas
+        if c_min > c_max:
+            print(i, ': GO LONG')
+            position = 1
+            open_long.append(price)
+
+        # Short condition is met
+        elif c_max > c_min:
+            print(i, ': GO SHORT')
+            position = -1
+            open_short.append(price)
+
+        else:
+            pass
+
+    elif position == 1:
+
+        # Short condition is met
+        if c_max > c_min:
+            print(i, ': CLOSE LONG AND GO SHORT')
+            position = -1
+            close_long.append(price)
+            open_short.append(price)
+            fees.append(close_long[-1] * 0.01)
+            pnl.append(open_long[-1] - close_long[-1])
+
+        else:
+            pass
+
+    elif position == -1:
+
+        # Long condition is met
+        if c_min > c_max:
+            print(i, ': CLOSE SHORT AND GO LONG')
+            position = 1
+            close_short.append(price)
+            open_long.append(price)
+            pnl.append(close_short[-1] - open_short[-1])
+            fees.append(close_short[-1] * 0.01)
+
+        else:
+            pass
+
+    else:
+        print('Error')
+
+print(
+    'net pnl: {} \nfees: {}'.format(
+        round(sum(pnl) - sum(fees), 4),
+        round(sum(fees), 4)
+    )
+)
+
+# Visualize data (last 24 hours)
+# df.iloc[-1440:].plot(
+#     color = [
+#         '#1ab1cd',
+#         '#6fff00',
+#         '#7cff00',
+#         '#a3ff00',
+#         '#d6ff00',
+#         '#f0ff00',
+#         '#ffdb00',
+#         '#ffb400',
+#         '#ff9a00',
+#         '#ff6700',
+#         '#ff4000',
+#         '#ff2700',
+#         '#ff0000'
+#     ]
+# )
+# plt.show()