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returns_vs_log_returns [2025/03/14 15:16] – brunoreturns_vs_log_returns [2025/03/14 15:33] (current) – bruno
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 df['logr_strat'] = np.where(df['position'].shift() == 1, df['logr_hodl'], 0) df['logr_strat'] = np.where(df['position'].shift() == 1, df['logr_hodl'], 0)
  
-df['r_fee'] = np.where(df['position'] != df['position'].shift(), 1/1.0025, 1) +df['R_strat'] = df['r_strat'].cumprod() 
-df['logr_fee'] = np.where(df['position'] != df['position'].shift(), -0.0025, 0)+df['logR_strat'] = np.exp( df['logr_strat'].cumsum() )
  
-df['r_net'] = df['r_strat'] * df['r_fee'] +df['r_fee'] = np.where(df['position'] != df['position'].shift(), 1.0025, 1) 
-df['logr_net'] = df['logr_strat'] + df['logr_fee']+df['logr_fee'] = np.where(df['position'] != df['position'].shift(), np.log(1.0025), 0) 
 + 
 +df['r_net'] = df['r_strat'] / df['r_fee'] 
 +df['logr_net'] = df['logr_strat'] - df['logr_fee']
  
 df['R_net'] = df['r_net'].cumprod() df['R_net'] = df['r_net'].cumprod()
 df['logR_net'] = np.exp( df['logr_net'].cumsum() ) df['logR_net'] = np.exp( df['logr_net'].cumsum() )
 </code> </code>
returns_vs_log_returns.txt · Last modified: 2025/03/14 15:33 by bruno