Differences

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--- compare_strategies_w_graph_3d [2024/08/19 06:07] – created bruno
+++ compare_strategies_w_graph_3d [2025/03/14 11:20] (current) – bruno
@@ Line 1: / Line 1: @@
 <code python>
 # coding: utf-8
-# %load 3d.py
+# use Kissbacktest:
-# %load 3d.py
+# %load https://github.com/brulint/kissbacktest/raw/main/kissbacktest.py
 # https://matplotlib.org/stable/gallery/mplot3d/surface3d.html
 # https://www.geeksforgeeks.org/plot-single-3d-point-on-top-of-plot_surface-in-python-matplotlib/
-#df = kbt_init('XXBTZEUR',1440)
+import numpy as np
-df = pd.read_csv('XXBTZEUR_1440.csv')
+import pandas as pd
-#df = df[int(-3*365):]
+import talib as ta
+# Donwload data
+# https://support.kraken.com/hc/en-us/articles/360047124832
+df = pd.read_csv('XBTEUR_1440.csv', names=['time', 'open', 'high', 'low', 'close', 'volume', 'count'])
+df = df[int(-10.5*365):]
+df['close'] = df.close.replace(to_replace=0, method='ffill')
 def fct (df, slow, fast):
     df['slow'] = ta.EMA(df.close, timeperiod=slow)
     df['fast'] = ta.EMA(df.close, timeperiod=fast)
-    df['signal'] = df.slow < df.fast
+    df['position'] = df.slow < df.fast
+    df['r_hodl'] = np.log( df['close'] / df['close'].shift() )
+    df['r_strat'] = np.where(df['position'].shift(), df['r_hodl'], 0)
+    df['r_fee'] = np.where(df['position'] != df['position'].shift(), 0.0025, 0)
+    df['r_net'] = df['r_strat'] - df['r_fee']
+    df['R_net'] = df['r_net'].cumsum()
+    return np.exp(df['R_net'].iloc[-1])-1
+def fct2 (df, slow, fast):
+    df['slow'] = ta.EMA(df.close, timeperiod=slow)
+    df['fast'] = ta.EMA(df.close, timeperiod=fast)
+    df['position'] = df.slow < df.fast
     df = kbt_compute(df)
     return df['R_net'].iloc[-1]
 SLOW = np.arange(5, 500, 5)
 FAST = np.arange(5, 500, 5)
 SLOW, FAST = np.meshgrid(SLOW, FAST)
-Z = np.zeros_like(SLOW)
+Z = np.zeros_like(SLOW).astype(float)
 # https://blog.finxter.com/applying-functions-to-python-meshgrid-5-effective-techniques/
 for i in range(SLOW.shape[0]):
     for j in range(SLOW.shape[1]):
+#        print(fct (df,SLOW[i,j], FAST[i,j]))
         Z[i, j] = fct (df, SLOW[i, j], FAST[i, j])
         print(f"{SLOW[i,j]}, {FAST[i,j]} -> {Z[i,j]}                 ",end="\r")
 import matplotlib.pyplot as plt
 from matplotlib import cm
 from mpl_toolkits.mplot3d import axes3d
 fig = plt.figure()
 ax = fig.add_subplot(projection='3d')
 # Plot a basic wireframe.
 ax.plot_surface(SLOW, FAST, Z, cmap=cm.coolwarm)
-ax.view_init(elev=20, azim=115, roll=0)
+ax.view_init(elev=35, azim=115, roll=0)
 ax.set_xlabel('SLOW')
 ax.set_ylabel('FAST')
 ax.set_zlabel('Z')
 ax.set_title('Single Point in 3D')
 # https://numpy.org/doc/stable/reference/generated/numpy.argmax.html#numpy.argmax
 ind = np.unravel_index(np.argmax(Z, axis=None), Z.shape)
 print(ind, SLOW[ind], FAST[ind], Z[ind])
 plt.show()
 </code>