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Report.py
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Report.py
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"""
Copyright (c) 2021 Guilherme Taborda Ribas All rights reserved.
Copyright (c) 2012-2013 Matplotlib Development Team; All Rights Reserved.
Copyright (c) 2017 NumPy developers.
Copyright (c) 2021, Israel Dryer. Revision b35a9984 .
Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team All rights reserved.
Copyright (c) 2001-2002 Enthought, Inc. 2003-2019, SciPy Developers. All rights reserved.
Copyright (c) 2017-2019 Ran Aroussi yfinance - market data downloader https://github.com/ranaroussi/yfinance
Copyright (c) 2012-2021, Michael L. Waskom All rights reserved.
Copyright (c) 2010-2021 by Alex Clark and contributors
This file is part of SclabMoneyMapBacktesting.
SclabMoneyMapBacktesting is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or any later version.
SclabMoneyMapBacktesting is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with SclabMoneyMapBacktesting. If not, see <http://www.gnu.org/licenses/>.
"""
import re
import pandas as pd
from Functions import *
from variables import all_signals
def get_prices(data):
# data = paper, period, timeframe
if len(data)==3:
import yfinance as yf
paper = yf.Ticker(data[0])
df = paper.history(period=data[1], interval=data[2])
# Round by paper info, because it was generatin error. yfinance doenst get alwys the same values
if 'currentPrice' in paper.info.keys():
rnd = len(str(paper.info['currentPrice']).split('.')[1])
df = df.round(rnd)
# Delete lines with NAn values
df.dropna(inplace=True)
df.reset_index(inplace=True)
df.rename(columns={'index':'Date','Datetime':'Date'}, inplace=True)
df['Date'] = pd.to_datetime(df['Date'])
df.sort_values(by='Date', inplace=True)
return df
else:
# data = file, codec, delimeter, date
df = pd.read_csv(data[0], encoding=data[1].lower(), sep=data[2].replace('"', '')).iloc[:,:6]#, data[3])
df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
# Delete lines with NAn values
df.dropna(inplace=True)
df.reset_index(drop=True, inplace=True)
df['Date'] = pd.to_datetime(df['Date'])
df.sort_values(by='Date', inplace=True)
return df
def calculate_setups(data, enter, quit):
df = get_prices(data)
enter_query = ''
for q in enter.split(' '):
if q:
if q=='[':
enter_query += '('
elif q==']':
enter_query += ')'
elif q=='&':
enter_query += '&'
elif q=='|':
enter_query += '|'
else:
par = re.search(r'\((.*?)\)',q).group(1)
func = q.replace('('+par+')', '')
if func in all_signals:
# Calculate functions
functions_dict[func](df, par)
enter_query+=' (`'+func+'('+par+')`) '
quit_query = ''
for q in quit.split(' '):
if q:
if q=='[':
quit_query += '('
elif q==']':
quit_query += ')'
elif q=='&':
quit_query += '&'
elif q=='|':
quit_query += '|'
else:
par = re.search(r'\((.*?)\)',q).group(1)
func = q.replace('('+par+')', '')
if func in all_signals:
# Calculate functions
functions_dict[func](df, par)
quit_query+=' (`'+func+'('+par+')`) '
df['Entrada_Compra'] = df.eval(enter_query)
df['Saida_Compra'] = df.eval(quit_query)
# # In future, in case different startegies for buy and sell could be applied
df['Entrada_Venda'] = df['Saida_Compra']
df['Saida_Venda'] = df['Entrada_Compra']
return df
def backtesting(data, enter, quit, longOp, shortOp, daytradeOp, quantity, fees, entry_price, entry_day, quit_price, quit_day):
df = calculate_setups(data, enter, quit)
df['Date'] = pd.to_datetime(df['Date'])
preco_entrada = []
preco_saida = []
data_entrada = []
data_saida = []
tipo_op = []
dia_listaIN = []
dia_listaOUT = []
total_dias = len(df.index)
dia = df.index[0]
pos_aberta = False
while dia < total_dias - entry_day:
# Test for long strategy
if (df.loc[dia, 'Entrada_Compra']) and longOp:
tipo_op.append('Comprada')
dia_listaIN.append(dia)
dia = dia + entry_day
data_entrada.append(df.loc[dia,'Date'])
preco_entrada.append(-df.loc[dia, entry_price])
pos_aberta = True
while dia < total_dias - quit_day: # se não tiver dias suficientes tem que sair
if daytradeOp:
if data_entrada[-1].date() != df.loc[dia,'Date'].date():
dia_listaOUT.append(dia)
# dia = dia + quit_day
data_saida.append(df.loc[dia,'Date'])
preco_saida.append(df.loc[dia, quit_price])
pos_aberta = False
break
if df.loc[dia, 'Saida_Compra']:
dia_listaOUT.append(dia)
dia = dia + quit_day
data_saida.append(df.loc[dia,'Date'])
preco_saida.append(df.loc[dia, quit_price])
pos_aberta = False
break
dia = dia + 1
# Test for short strategy
elif (df.loc[dia, 'Entrada_Venda']) and shortOp:
tipo_op.append('Vendida')
dia_listaIN.append(dia)
dia = dia + entry_day
data_entrada.append(df.loc[dia,'Date'])
preco_entrada.append(df.loc[dia, entry_price])
pos_aberta = True
while dia < total_dias - quit_day: # se não tiver dias suficientes tem que sair
if daytradeOp:
if data_entrada[-1].date() != df.loc[dia,'Date'].date():
dia_listaOUT.append(dia)
# dia = dia + quit_day
data_saida.append(df.loc[dia,'Date'])
preco_saida.append(-df.loc[dia, quit_price])
pos_aberta = False
break
if df.loc[dia, 'Saida_Venda']:
dia_listaOUT.append(dia)
dia = dia + quit_day
data_saida.append(df.loc[dia,'Date'])
preco_saida.append(-df.loc[dia, quit_price])
pos_aberta = False
break
dia = dia + 1
else:
dia = dia + 1
if pos_aberta:
data_entrada = data_entrada[:-1]
tipo_op = tipo_op[:-1]
preco_entrada = preco_entrada[:-1]
return pd.DataFrame( data={'Data Entrada':data_entrada, 'Data Saida':data_saida, 'Tipo':tipo_op, 'Quantidade':[quantity for q in tipo_op],
'Custos':[fees for q in tipo_op],'Preco Entrada':preco_entrada, 'Preco Saida':preco_saida}), df
def report(result, df):
result.loc[:, 'Variacao'] = result['Quantidade']*(result['Preco Entrada']+result['Preco Saida']) - (2*result['Custos'])
cond1 = [(result['Tipo']=='Comprada'), (result['Tipo']=='Vendida')]
esc1 = [100*( (result['Preco Saida'].abs()-result['Custos']) - (result['Preco Entrada'].abs()+result['Custos']) ) / ( result['Preco Entrada'].abs()+result['Custos'] ),
100*( (result['Preco Entrada'].abs()-result['Custos']) - (result['Preco Saida'].abs()+result['Custos']) ) / ( result['Preco Entrada'].abs()-result['Custos'])]
result['Variacao(%)'] = np.select(cond1, esc1, default=None)
cond2 = [result['Variacao']>0, result['Variacao']<0]
esc2 = ['Lucro', 'Prejuizo']
result['L/P'] = np.select(cond2, esc2, default='Neutro')
report_dict = {}
# Fazer testes de existencia de colunas Lucro, Prejuizo, Neutro
total = result.groupby('L/P')['Variacao'].sum()
qnt = result.groupby('L/P').size()
med = result.groupby('L/P')['Variacao'].mean()
med_percentual = result.groupby('L/P')['Variacao(%)'].mean()
report_dict['total_op'] = len(result.index)
if 'Lucro' in total.index:
report_dict['lucro_liq'] = round(total['Lucro'], 2)
else:
report_dict['lucro_liq'] = 0
if 'Prejuizo' in total.index:
report_dict['prejuizo_liq'] = round(total['Prejuizo'], 2)
else:
report_dict['prejuizo_liq'] = 0
if 'Lucro' in qnt.index:
report_dict['qnt_lucro'] = qnt['Lucro']
else:
report_dict['qnt_lucro'] = 0
if 'Prejuizo' in qnt.index:
report_dict['qnt_prejuizo'] = qnt['Prejuizo']
else:
report_dict['qnt_prejuizo'] = 0
if 'Neutro' in qnt.index:
report_dict['qnt_neutro'] = qnt['Neutro']
else:
report_dict['qnt_neutro'] = 0
if len(result.index) == 0:
report_dict['percentual_lucro'] = 0
else:
report_dict['percentual_lucro'] = round(report_dict['qnt_lucro']/len(result.index), 2)
if report_dict['prejuizo_liq'] == 0:
report_dict['fator_lucro'] = 0
else:
report_dict['fator_lucro'] = round(report_dict['lucro_liq']/report_dict['prejuizo_liq'], 2)
report_dict['ganho_maximo'] = round(result['Variacao'].max(), 2)
report_dict['ganho_maximo(%)'] = round(result['Variacao(%)'].max(), 2)
report_dict['perda_maxima'] = round(result['Variacao'].min(), 2)
report_dict['perda_maxima(%)'] = round(result['Variacao(%)'].min(), 2)
if 'Lucro' in med.index:
report_dict['media_ganhos'] = round(med['Lucro'], 2)
else:
report_dict['media_ganhos'] = 0
if 'Prejuizo' in med.index:
report_dict['media_perdas'] = round(med['Prejuizo'], 2)
else:
report_dict['media_perdas'] = 0
if 'Lucro' in med_percentual.index:
report_dict['media_ganhos(%)'] = round(med_percentual['Lucro'], 2)
else:
report_dict['media_ganhos(%)'] = 0
if 'Prejuizo' in med_percentual.index:
report_dict['media_perdas(%)'] = round(med_percentual['Prejuizo'], 2)
else:
report_dict['media_perdas(%)'] = 0
if report_dict['media_perdas'] == 0:
report_dict['ganhos_por_perdas'] = 0
else:
report_dict['ganhos_por_perdas'] = abs(round(report_dict['media_ganhos'] / report_dict['media_perdas'], 2))
report_dict['retorno'] = round( (report_dict['qnt_lucro']*report_dict['media_ganhos']) + (report_dict['qnt_prejuizo']*report_dict['media_perdas']), 2)
report_dict['retorno(%)'] = round( (report_dict['qnt_lucro']*report_dict['media_ganhos(%)']) + (report_dict['qnt_prejuizo']*report_dict['media_perdas(%)']), 2)
result['evolucao_saldo'] = result['Variacao'].cumsum()
result['evolucao_saldo_max'] = result['evolucao_saldo'].cummax()
result['drawdowns'] = result['evolucao_saldo_max'] - result['evolucao_saldo']
report_dict['drawdown_max'] = result['drawdowns'].max()
if report_dict['drawdown_max'] == 0:
report_dict['retorno_percentual'] = 0
else:
report_dict['retorno_percentual'] = round( 100*report_dict['retorno']/report_dict['drawdown_max'], 2 )
report_dict['drawdown_max'] = round(report_dict['drawdown_max'], 2)
fimDD = result['drawdowns'].idxmax()
inicioDD = result['evolucao_saldo'].iloc[:fimDD].idxmax()
report_dict['saldo_inicioDD'] = result['evolucao_saldo'].iloc[inicioDD]
report_dict['saldo_fimDD'] = result['evolucao_saldo'].iloc[fimDD]
report_dict['inicio_drawdown'] = result['Data Entrada'].iloc[inicioDD]
report_dict['fim_drawdown'] = result['Data Entrada'].iloc[fimDD]
report_dict['df_report'] = result
report_dict['df_cotation'] = df
return report_dict