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creslinux 2018-07-15 17:03:47 +00:00
parent 4e68362d46
commit a8b62a21cc
1 changed files with 120 additions and 33 deletions
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147
freqtrade/optimize/backtesting.py
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@ -190,7 +190,6 @@ class Backtesting(object):
return btr return btr
return None return None
@profile
def backtest(self, args: Dict) -> DataFrame: def backtest(self, args: Dict) -> DataFrame:
""" """
Implements backtesting functionality Implements backtesting functionality
@ -233,7 +232,13 @@ class Backtesting(object):
last_bslap_results = bslap_results last_bslap_results = bslap_results
bslap_results = last_bslap_results + bslap_pair_results bslap_results = last_bslap_results + bslap_pair_results
# Switch List of Trade Dicts (bslap_results) to Dataframe
# Fill missing, calculable columns, profit, duration , abs etc.
bslap_results_df = DataFrame(bslap_results, columns=BacktestResult._fields) bslap_results_df = DataFrame(bslap_results, columns=BacktestResult._fields)
bslap_results_df = self.vector_fill_results_table(bslap_results_df)
print(bslap_results_df.dtypes)
return bslap_results_df return bslap_results_df
########################### Original BT loop ########################### Original BT loop
@ -283,6 +288,69 @@ class Backtesting(object):
# return DataFrame.from_records(trades, columns=BacktestResult._fields) # return DataFrame.from_records(trades, columns=BacktestResult._fields)
######################## Original BT loop end ######################## Original BT loop end
def vector_fill_results_table(self, bslap_results_df: DataFrame):
"""
The Results frame contains a number of columns that are calculable
from othe columns. These are left blank till all rows are added,
to be populated in single vector calls.
Columns to be populated are:
- Profit
- trade duration
- profit abs
:param bslap_results Dataframe
:return: bslap_results Dataframe
"""
import pandas as pd
debug = True
# stake and fees
stake = self.config.get('stake_amount')
# TODO grab these from the environment, do not hard set
open_fee = 0.05
close_fee = 0.05
if debug:
print("Stake is,", stake, "the sum of currency to spend per trade")
print("The open fee is", open_fee, "The close fee is", close_fee)
if debug:
from pandas import set_option
set_option('display.max_rows', 5000)
set_option('display.max_columns', 8)
pd.set_option('display.width', 1000)
pd.set_option('max_colwidth', 40)
pd.set_option('precision', 12)
# Align with BT
bslap_results_df['open_time'] = pd.to_datetime(bslap_results_df['open_time'])
bslap_results_df['close_time'] = pd.to_datetime(bslap_results_df['close_time'])
# Populate duration
bslap_results_df['trade_duration'] = bslap_results_df['close_time'] - bslap_results_df['open_time']
if debug:
print(bslap_results_df[['open_time', 'close_time', 'trade_duration']])
## Spends, Takes, Profit, Absolute Profit
# Buy Price
bslap_results_df['buy_sum'] = stake * bslap_results_df['open_rate']
bslap_results_df['buy_fee'] = bslap_results_df['buy_sum'] * open_fee
bslap_results_df['buy_spend'] = bslap_results_df['buy_sum'] + bslap_results_df['buy_fee']
# Sell price
bslap_results_df['sell_sum'] = stake * bslap_results_df['close_rate']
bslap_results_df['sell_fee'] = bslap_results_df['sell_sum'] * close_fee
bslap_results_df['sell_take'] = bslap_results_df['sell_sum'] - bslap_results_df['sell_fee']
# profit_percent
bslap_results_df['profit_percent'] = bslap_results_df['sell_take'] / bslap_results_df['buy_spend'] - 1
# Absolute profit
bslap_results_df['profit_abs'] = bslap_results_df['sell_take'] - bslap_results_df['buy_spend']
if debug:
print("\n")
print(bslap_results_df[['buy_spend', 'sell_take', 'profit_percent', 'profit_abs']])
return bslap_results_df
def np_get_t_open_ind(self, np_buy_arr, t_exit_ind: int): def np_get_t_open_ind(self, np_buy_arr, t_exit_ind: int):
import utils_find_1st as utf1st import utils_find_1st as utf1st
""" """
@ -301,6 +369,7 @@ class Backtesting(object):
t_open_ind = t_open_ind + t_exit_ind # Align numpy index t_open_ind = t_open_ind + t_exit_ind # Align numpy index
return t_open_ind return t_open_ind
@profile
def backslap_pair(self, ticker_data, pair): def backslap_pair(self, ticker_data, pair):
import pandas as pd import pandas as pd
import numpy as np import numpy as np
@ -316,19 +385,10 @@ class Backtesting(object):
# Read Stop Loss Values and Stake # Read Stop Loss Values and Stake
stop = self.stop_loss_value stop = self.stop_loss_value
p_stop = (stop + 1) # What stop really means, e.g 0.01 is 0.99 of price p_stop = (stop + 1) # What stop really means, e.g 0.01 is 0.99 of price
stake = self.config.get('stake_amount')
# Set fees
# TODO grab these from the environment, do not hard set
# Fees
open_fee = 0.05
close_fee = 0.05
if debug: if debug:
print("Stop is ", stop, "value from stragey file") print("Stop is ", stop, "value from stragey file")
print("p_stop is", p_stop, "value used to multiply to entry price") print("p_stop is", p_stop, "value used to multiply to entry price")
print("Stake is,", stake, "the sum of currency to spend per trade")
print("The open fee is", open_fee, "The close fee is", close_fee)
if debug: if debug:
from pandas import set_option from pandas import set_option
@ -395,6 +455,12 @@ class Backtesting(object):
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 # buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
np_bslap = np.array(bslap[['buy', 'open', 'close', 'sell', 'high', 'low']]) np_bslap = np.array(bslap[['buy', 'open', 'close', 'sell', 'high', 'low']])
# Build a numpy list of date-times.
# We use these when building the trade
# The rationale is to address a value from a pandas cell is thousands of
# times more expensive. Processing time went X25 when trying to use any data from pandas
np_bslap_dates = bslap['date'].values
loop: int = 0 # how many time around the loop loop: int = 0 # how many time around the loop
t_exit_ind = 0 # Start loop from first index t_exit_ind = 0 # Start loop from first index
t_exit_last = 0 # To test for exit t_exit_last = 0 # To test for exit
@ -660,6 +726,10 @@ class Backtesting(object):
Add trade to backtest looking results list of dicts Add trade to backtest looking results list of dicts
Loop back to look for more trades. Loop back to look for more trades.
""" """
if debug_timing:
t_t = f(st)
print("8a-IfEls", str.format('{0:.17f}', t_t))
st = s()
# Index will change if incandle stop or look back as close Open and Sell # Index will change if incandle stop or look back as close Open and Sell
if t_exit_type == 'stop': if t_exit_type == 'stop':
close_index: int = t_exit_ind + 1 close_index: int = t_exit_ind + 1
@ -668,39 +738,56 @@ class Backtesting(object):
else: else:
close_index: int = t_exit_ind + 1 close_index: int = t_exit_ind + 1
# Munge the date / delta (bt already date formats...just subract) if debug_timing:
trade_start = bslap.iloc[t_open_ind + 1]['date'] t_t = f(st)
trade_end = bslap.iloc[close_index]['date'] print("8b-Index", str.format('{0:.17f}', t_t))
# def __datetime(date_str): st = s()
# return datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S+00:00')
trade_mins = (trade_end - trade_start).total_seconds() / 60
# Profit ABS. # # Profit ABS.
# sumrecieved((rate * numTokens) * fee) - sumpaid ((rate * numTokens) * fee) # # sumrecieved((rate * numTokens) * fee) - sumpaid ((rate * numTokens) * fee)
sumpaid: float = (np_trade_enter_price * stake) # sumpaid: float = (np_trade_enter_price * stake)
sumpaid_fee: float = sumpaid * open_fee # sumpaid_fee: float = sumpaid * open_fee
sumrecieved: float = (np_trade_exit_price * stake) # sumrecieved: float = (np_trade_exit_price * stake)
sumrecieved_fee: float = sumrecieved * close_fee # sumrecieved_fee: float = sumrecieved * close_fee
profit_abs: float = sumrecieved - sumpaid - sumpaid_fee - sumrecieved_fee # profit_abs: float = sumrecieved - sumpaid - sumpaid_fee - sumrecieved_fee
if debug_timing:
t_t = f(st)
print("8d---ABS", str.format('{0:.17f}', t_t))
st = s()
# Build trade dictionary
## In general if a field can be calculated later from other fields leave blank here
## Its X(numer of trades faster) to calc all in a single vector than 1 trade at a time
# build trade dictionary
bslap_result["pair"] = pair bslap_result["pair"] = pair
bslap_result["profit_percent"] = (np_trade_exit_price - np_trade_enter_price) / np_trade_enter_price bslap_result["profit_percent"] = "1" # To be 1 vector calc across trades when loop complete
bslap_result["profit_abs"] = round(profit_abs, 15) bslap_result["profit_abs"] = "1" # To be 1 vector calc across trades when loop complete
bslap_result["open_time"] = trade_start bslap_result["open_time"] = np_bslap_dates[t_open_ind + 1] # use numpy array, pandas 20x slower
bslap_result["close_time"] = trade_end bslap_result["close_time"] = np_bslap_dates[close_index] # use numpy array, pandas 20x slower
bslap_result["open_index"] = t_open_ind + 2 # +1 between np and df, +1 as we buy on next. bslap_result["open_index"] = t_open_ind + 2 # +1 between np and df, +1 as we buy on next.
bslap_result["close_index"] = close_index bslap_result["close_index"] = close_index
bslap_result["trade_duration"] = trade_mins bslap_result["trade_duration"] = "1" # To be 1 vector calc across trades when loop complete
bslap_result["open_at_end"] = False bslap_result["open_at_end"] = False
bslap_result["open_rate"] = round(np_trade_enter_price, 15) bslap_result["open_rate"] = round(np_trade_enter_price, 15)
bslap_result["close_rate"] = round(np_trade_exit_price, 15) bslap_result["close_rate"] = round(np_trade_exit_price, 15)
#bslap_result["exit_type"] = t_exit_type bslap_result["exit_type"] = t_exit_type
if debug_timing:
t_t = f(st)
print("8e-trade", str.format('{0:.17f}', t_t))
st = s()
# Add trade dictionary to list # Add trade dictionary to list
bslap_pair_results.append(bslap_result) bslap_pair_results.append(bslap_result)
if debug: if debug:
print(bslap_pair_results) print(bslap_pair_results)
if debug_timing:
t_t = f(st)
print("8f--list", str.format('{0:.17f}', t_t))
st = s()
""" """
Loop back to start. t_exit_last becomes where loop Loop back to start. t_exit_last becomes where loop
will seek to open new trades from. will seek to open new trades from.