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--- backtest_w_python [2025/03/23 08:05] – bruno
+++ backtest_w_python [2025/03/27 17:02] (current) – [Sinon] bruno
@@ Line 185: / Line 185: @@
 ==== Advanced trend following RSI ====
-<code>
+<code python>
-# coding: utf-8
 # coding: utf-8
@@ Line 271: / Line 270: @@
-==== Sinon ====
-Un vieux code de 2022 (si pas plus). Chrypowatch n'existe plus, c'est dire :-)
-<code python>
-# coding: utf-8
-import requests
-import numpy as np
-import pandas as pd
-import talib
-url = 'https://api.cryptowat.ch/markets/kraken/btceur/ohlc'
-ohlc = requests.get(url).json()['result'][str(12*60*60)]
-columns = ['time','open','high','low','close','volume','count']
-df = pd.DataFrame(ohlc, columns=columns).astype(float)
-df = df.iloc[-1000:]
-df['RSI'] = talib.RSI(df['close'], timeperiod=14)
-#df['RSI'] = df.RSI.fillna(value=df.RSI.loc[14])
-df['long'] = talib.SMA(df.close, timeperiod=200)
-#df['long'] = df.long.fillna(value=df.long.loc[200])
-df['short'] = talib.SMA(df.close,timeperiod=14)
-#df['short'] = df.short.fillna(value=df.short.loc[14])
-df['trend'] = df.long < df.short
-# signal
-df['sig_in'] = (df.RSI > 60) & df.trend
-df['sig_out'] = (df.RSI < 40)# | 1-df.trend
-#df['sig_in'] = (df.RSI.shift() < 70) & (df.RSI > 70)
-#df['sig_out'] = (df.RSI.shift() > 30) & (df.RSI < 30)
-#df['sig_in'] = (df.RSI.shift() < 25) & (df.RSI > 25)
-#df['stoploss'] = df.low.rolling(5).min().where(df.sig_in==1).ffill()
-#df['sig_out'] = ((df.RSI.shift() > 75) & (df.RSI < 75)) | ((df.RSI.shift()>25) & (df.RSI<25)) | (df.close < df.stoploss)
-#df['signal'] = df.sig_in.where(df.sig_in).fillna(1-df.sig_out.where(df.sig_out)).ffill()
-#df['sig_out'].loc[0] = True
-#df['signal'] = (1-df.sig_out.where(df.sig_out)).fillna(df.sig_in.where(df.sig_in)).ffill()# * df.trend
-df['sig_0'] = df.sig_in.astype(int) - df.sig_out.astype(int)
-df['sig_1'] = df.sig_0.where(df.sig_0!=0).ffill()
-df['signal'] = df.sig_1 > 0
-# Rendements
-df['close'] = df.close.replace(to_replace=0, method='ffill')
-df['r_0'] = df.close / df.close.shift()
-df['r_strat'] = np.where(df.signal.shift(), df.r_0, 1)
-df['r_fee'] = np.where(df.signal.shift() + df.signal == 1, 1-0.0025, 1)
-# tronquage datafame
-#df = df.iloc[-700:]
-# Rendement cumulé
-df['R_net'] = (df.r_strat * df.r_fee).cumprod()
-# Graphiques
-from bokeh.plotting import figure,show
-from bokeh.layouts import column,row
-p1 = figure(height=300,width=800)
-p1.line(df.time,df.close)
-#p1.line(df.time,df.long,color='green')
-#p1.line(df.time,df.short,color='red')
-p2 =  figure(height=100,width=800,x_range=p1.x_range)
-p2.line(df.time,df.RSI)
-#p3_0 = figure(height=100,width=800,x_range=p1.x_range)
-#p3_0.line(df.time,df.trend)
-p3_1 = figure(height=100,width=800,x_range=p1.x_range)
-p3_1.line(df.time,df.sig_in,color='green')
-p3_2 = figure(height=100,width=800,x_range=p1.x_range)
-p3_2.line(df.time,df.sig_out,color='red')
-p3_3 = figure(height=100,width=800,x_range=p1.x_range)
-p3_3.line(df.time,df.sig_0)
-p3_3_2 = figure(height=100,width=800,x_range=p1.x_range)
-p3_3_2.line(df.time,df.sig_1)
-p3_4 = figure(height=100,width=800,x_range=p1.x_range)
-p3_4.line(df.time,df.signal)
-p4 = figure(height=150,width=800,x_range=p1.x_range)
-p4.line(df.time,df.r_0,color='lightgray')
-p4.line(df.time,df.r_strat)
-p4.line(df.time,df.r_fee,color='red')
-p5 = figure(height=300,width=800,x_range=p1.x_range)
-p5.line(df.time,df.r_0.cumprod(),color='lightgray')
-p5.line(df.time,df.r_strat.cumprod())
-p5.line(df.time,df.R_net,color='red')
-layout = column(p1,p2,p3_1,p3_2,p3_3,p3_3_2,p3_4,p4,p5)
-show(layout)
-</code>