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--- compare_strategies_w_graph_3d [2025/03/02 23:27] – bruno
+++ compare_strategies_w_graph_3d [2025/03/14 11:20] (current) – bruno
@@ Line 7: / Line 7: @@
 # https://www.geeksforgeeks.org/plot-single-3d-point-on-top-of-plot_surface-in-python-matplotlib/
-import pandas as pd
-#df = kbt_init('XXBTZEUR',1440)
-df = pd.read_csv('XXBTZEUR_1440.csv')
-#df = df[int(-3*365):]
-def fct (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)
-# 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]):
-        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.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>
-<code python>
 import numpy as np
 import pandas as pd
 import talib as ta
-#df = pd.read_csv('https://raw.githubusercontent.com/brulint/backtesting/main/btceur-2h.csv')
+# Donwload data
-df = pd.read_csv('http://lintermans.all2all.org/cgi-bin/get-chart.php?chart=XXBTZEUR_1440.csv')
+# 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')
@@ Line 75: / Line 26: @@
     df['r_net'] = df['r_strat'] - df['r_fee']
     df['R_net'] = df['r_net'].cumsum()
-    print(df['R_net'].iloc[-1])
+    return np.exp(df['R_net'].iloc[-1])-1
-    return df['R_net'].iloc[-1]
 def fct2 (df, slow, fast):