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First cut, Bslap

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science project replacement for freqtrade backtest analysis

- appprox 300-500x quicker to execute

- fixes stop on close take close price bug in FT
Bslap is configurable but by default stops are triggerd on
low and pay stop price

Not implimented dynamic stops or roi
This commit is contained in:
creslinux 2018-07-14 22:54:23 +00:00
parent bd46b4faf3
commit 90e3c38757
1 changed files with 463 additions and 32 deletions
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493
freqtrade/optimize/backtesting.py
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@ -21,6 +21,8 @@ from freqtrade.configuration import Configuration
from freqtrade.exchange import Exchange from freqtrade.exchange import Exchange
from freqtrade.misc import file_dump_json from freqtrade.misc import file_dump_json
from freqtrade.persistence import Trade from freqtrade.persistence import Trade
from profilehooks import profile
from freqtrade.strategy.resolver import IStrategy, StrategyResolver
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -67,6 +69,8 @@ class Backtesting(object):
self.exchange = Exchange(self.config) self.exchange = Exchange(self.config)
self.fee = self.exchange.get_fee() self.fee = self.exchange.get_fee()
self.stop_loss_value = self.analyze.strategy.stoploss
@staticmethod @staticmethod
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]: def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
""" """
@ -186,6 +190,7 @@ 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
@ -201,55 +206,481 @@ class Backtesting(object):
realistic: do we try to simulate realistic trades? (default: True) realistic: do we try to simulate realistic trades? (default: True)
:return: DataFrame :return: DataFrame
""" """
headers = ['date', 'buy', 'open', 'close', 'sell'] headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
processed = args['processed'] processed = args['processed']
max_open_trades = args.get('max_open_trades', 0) max_open_trades = args.get('max_open_trades', 0)
realistic = args.get('realistic', False) realistic = args.get('realistic', False)
trades = [] trades = []
trade_count_lock: Dict = {} trade_count_lock: Dict = {}
########################### Call out BSlap instead of using FT
bslap_results: list = []
last_bslap_resultslist = []
for pair, pair_data in processed.items(): for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
ticker_data = self.populate_sell_trend( ticker_data = self.populate_sell_trend(
self.populate_buy_trend(pair_data))[headers].copy() self.populate_buy_trend(pair_data))[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle bslap_pair_results = self.backslap_pair(ticker_data, pair)
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
ticker_data.drop(ticker_data.head(1).index, inplace=True) last_bslap_results = bslap_results
bslap_results = last_bslap_results + bslap_pair_results
bslap_results_df = DataFrame(bslap_results)
print(bslap_results_df.dtypes())
# Convert from Pandas to list for performance reasons return bslap_results_df
# (Looping Pandas is slow.) ########################### Original BT loop
ticker = [x for x in ticker_data.itertuples()] # for pair, pair_data in processed.items():
# pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
#
# ticker_data = self.populate_sell_trend(
# self.populate_buy_trend(pair_data))[headers].copy()
#
# # to avoid using data from future, we buy/sell with signal from previous candle
# ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
# ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
#
# ticker_data.drop(ticker_data.head(1).index, inplace=True)
#
# # Convert from Pandas to list for performance reasons
# # (Looping Pandas is slow.)
# ticker = [x for x in ticker_data.itertuples()]
#
# lock_pair_until = None
# for index, row in enumerate(ticker):
# if row.buy == 0 or row.sell == 1:
# continue # skip rows where no buy signal or that would immediately sell off
#
# if realistic:
# if lock_pair_until is not None and row.date <= lock_pair_until:
# continue
# if max_open_trades > 0:
# # Check if max_open_trades has already been reached for the given date
# if not trade_count_lock.get(row.date, 0) < max_open_trades:
# continue
#
# trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
#
# trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
# trade_count_lock, args)
#
#
# if trade_entry:
# lock_pair_until = trade_entry.close_time
# trades.append(trade_entry)
# else:
# # Set lock_pair_until to end of testing period if trade could not be closed
# # This happens only if the buy-signal was with the last candle
# lock_pair_until = ticker_data.iloc[-1].date
#
# return DataFrame.from_records(trades, columns=BacktestResult._fields)
######################## Original BT loop end
lock_pair_until = None def np_get_t_open_ind(self, np_buy_arr, t_exit_ind: int):
for index, row in enumerate(ticker): import utils_find_1st as utf1st
if row.buy == 0 or row.sell == 1: """
continue # skip rows where no buy signal or that would immediately sell off The purpose of this def is to return the next "buy" = 1
after t_exit_ind.
if realistic: t_exit_ind is the index the last trade exited on
if lock_pair_until is not None and row.date <= lock_pair_until: or 0 if first time around this loop.
continue """
if max_open_trades > 0: t_open_ind: int
# Check if max_open_trades has already been reached for the given date
if not trade_count_lock.get(row.date, 0) < max_open_trades:
continue
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1 # Create a view on our buy index starting after last trade exit
# Search for next buy
np_buy_arr_v = np_buy_arr[t_exit_ind:]
t_open_ind = utf1st.find_1st(np_buy_arr_v, 1, utf1st.cmp_equal)
t_open_ind = t_open_ind + t_exit_ind # Align numpy index
return t_open_ind
trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:], def backslap_pair(self, ticker_data, pair):
trade_count_lock, args) import pandas as pd
import numpy as np
import timeit
import utils_find_1st as utf1st
if trade_entry: stop = self.stop_loss_value
lock_pair_until = trade_entry.close_time p_stop = (-stop + 1) # What stop really means, e.g 0.01 is 0.99 of price
trades.append(trade_entry)
### backslap debug wrap
debug_2loops = False # only loop twice, for faster debug
debug_timing = False # print timing for each step
debug = False # print values, to check accuracy
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)
def s():
st = timeit.default_timer()
return st
def f(st):
return (timeit.default_timer() - st)
#### backslap config
"""
A couple legacy Pandas vars still used for pretty debug output.
If have debug enabled the code uses these fields for dataframe output
Ensure bto, sto, sco are aligned with Numpy values next
to align debug and actual. Options are:
buy - open - close - sell - high - low - np_stop_pri
"""
bto = buys_triggered_on = "close"
sto = stops_triggered_on = "low" ## Should be low, FT uses close
sco = stops_calculated_on = "np_stop_pri" ## should use np_stop_pri, FT uses close
'''
Numpy arrays are used for 100x speed up
We requires setting Int values for
buy stop triggers and stop calculated on
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
'''
np_buy: int = 0
np_open: int = 1
np_close: int = 2
np_sell: int = 3
np_high: int = 4
np_low: int = 5
np_stop: int = 6
np_bto: int = np_close # buys_triggered_on - should be close
np_bco: int = np_open # buys calculated on - open of the next candle.
np_sto: int = np_low # stops_triggered_on - Should be low, FT uses close
np_sco: int = np_stop # stops_calculated_on - Should be stop, FT uses close
#
### End Config
pair: str = pair
loop: int = 1
#ticker_data: DataFrame = ticker_dfs[t_file]
bslap: DataFrame = ticker_data
# Build a single dimension numpy array from "buy" index for faster search
# (500x faster than pandas)
np_buy_arr = bslap['buy'].values
np_buy_arr_len: int = len(np_buy_arr)
# use numpy array for faster searches in loop, 20x faster than pandas
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
np_bslap = np.array(bslap[['buy', 'open', 'close', 'sell', 'high', 'low']])
loop: int = 0 # how many time around the loop
t_exit_ind = 0 # Start loop from first index
t_exit_last = 0 # To test for exit
st = s() # Start timer for processing dataframe
if debug:
print('Processing:', pair)
# Results will be stored in a list of dicts
bslap_pair_results: list = []
bslap_result: dict = {}
while t_exit_ind < np_buy_arr_len:
loop = loop + 1
if debug or debug_timing:
print("----------------------------------------------------------- Loop", loop, pair)
if debug_2loops:
if loop == 4:
break
'''
Dev phases
Phase 1
1) Manage buy, sell, stop enter/exit
a) Find first buy index
b) Discover first stop and sell hit after buy index
c) Chose first intance as trade exit
Phase 2
2) Manage dynamic Stop and ROI Exit
a) Create trade slice from 1
b) search within trade slice for dynamice stop hit
c) search within trade slice for ROI hit
'''
'''
Finds index for first buy = 1 flag, use .values numpy array for speed
Create a slice, from first buy index onwards.
Slice will be used to find exit conditions after trade open
'''
if debug_timing:
st = s()
t_open_ind = self.np_get_t_open_ind(np_buy_arr, t_exit_ind)
if debug_timing:
t_t = f(st)
print("0-numpy", str.format('{0:.17f}', t_t))
st = s()
'''
Calculate np_t_stop_pri (Trade Stop Price) based on the buy price
As stop in based on buy price we are interested in buy
- Buys are Triggered On np_bto, typically the CLOSE of candle
- Buys are Calculated On np_bco, default is OPEN of the next candle.
as we only see the CLOSE after it has happened.
The assumption is we have bought at first available price, the OPEN
'''
np_t_stop_pri = (np_bslap[t_open_ind + 1, np_bco] * p_stop)
if debug_timing:
t_t = f(st)
print("1-numpy", str.format('{0:.17f}', t_t))
st = s()
"""
1)Create a View from our open trade forward
The view is our search space for the next Stop or Sell
We use a numpy view:
Using a numpy for speed on views, 1,000 faster than pandas
Pandas cannot assure it will always return a view, copies are
3 orders of magnitude slower
The view contains columns:
buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
"""
np_t_open_v = np_bslap[t_open_ind:]
if debug_timing:
t_t = f(st)
print("2-numpy", str.format('{0:.17f}', t_t))
st = s()
'''
Find first stop index after Trade Open:
First index in np_t_open_v (numpy view of bslap dataframe)
Using a numpy view a orders of magnitude faster
where [np_sto] (stop tiggered on variable: "close", "low" etc) < np_t_stop_pri
'''
np_t_stop_ind = utf1st.find_1st(np_t_open_v[:, np_sto],
np_t_stop_pri,
utf1st.cmp_smaller) \
+ t_open_ind
if debug_timing:
t_t = f(st)
print("3-numpy", str.format('{0:.17f}', t_t))
st = s()
'''
Find first sell index after trade open
First index in t_open_slice where ['sell'] = 1
'''
# Use numpy array for faster search for sell
# Sell uses column 3.
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
# Numpy searches 25-35x quicker than pandas on this data
np_t_sell_ind = utf1st.find_1st(np_t_open_v[:, np_sell],
1, utf1st.cmp_equal) \
+ t_open_ind
if debug_timing:
t_t = f(st)
print("4-numpy", str.format('{0:.17f}', t_t))
st = s()
'''
Determine which was hit first stop or sell, use as exit
STOP takes priority over SELL as would be 'in candle' from tick data
Sell would use Open from Next candle.
So in a draw Stop would be hit first on ticker data in live
'''
if np_t_stop_ind <= np_t_sell_ind:
t_exit_ind = np_t_stop_ind # Set Exit row index
t_exit_type = 'stop' # Set Exit type (sell|stop)
np_t_exit_pri = np_sco # The price field our STOP exit will use
else: else:
# Set lock_pair_until to end of testing period if trade could not be closed # move sell onto next candle, we only look back on sell
# This happens only if the buy-signal was with the last candle # will use the open price later.
lock_pair_until = ticker_data.iloc[-1].date t_exit_ind = np_t_sell_ind # Set Exit row index
t_exit_type = 'sell' # Set Exit type (sell|stop)
np_t_exit_pri = np_open # The price field our SELL exit will use
if debug_timing:
t_t = f(st)
print("5-logic", str.format('{0:.17f}', t_t))
st = s()
if debug:
'''
Print out the buys, stops, sells
Include Line before and after to for easy
Human verification
'''
# Combine the np_t_stop_pri value to bslap dataframe to make debug
# life easy. This is the currenct stop price based on buy price_
# Don't care about performance in debug
# (add an extra column if printing as df has date in col1 not in npy)
bslap['np_stop_pri'] = np_t_stop_pri
# Buy
print("=================== BUY ", pair)
print("Numpy Array BUY Index is:", t_open_ind)
print("DataFrame BUY Index is:", t_open_ind + 1, "displaying DF \n")
print("HINT, BUY trade should use OPEN price from next candle, i.e ", t_open_ind + 2, "\n")
op_is = t_open_ind - 1 # Print open index start, line before
op_if = t_open_ind + 3 # Print open index finish, line after
print(bslap.iloc[op_is:op_if], "\n")
print(bslap.iloc[t_open_ind + 1]['date'])
# Stop - Stops trigger price sto, and price received sco. (Stop Trigger|Calculated On)
print("=================== STOP ", pair)
print("Numpy Array STOP Index is:", np_t_stop_ind)
print("DataFrame STOP Index is:", np_t_stop_ind + 1, "displaying DF \n")
print("First Stop Index after Trade open in candle", np_t_stop_ind + 1, ": \n",
str.format('{0:.17f}', bslap.iloc[np_t_stop_ind][sto]),
"is less than", str.format('{0:.17f}', np_t_stop_pri))
print("Tokens will be sold at rate:", str.format('{0:.17f}', bslap.iloc[np_t_stop_ind][sco]))
print("HINT, STOPs should close in-candle, i.e", np_t_stop_ind + 1,
": As live STOPs are not linked to O-C times")
st_is = np_t_stop_ind - 1 # Print stop index start, line before
st_if = np_t_stop_ind + 2 # Print stop index finish, line after
print(bslap.iloc[st_is:st_if], "\n")
# Sell
print("=================== SELL ", pair)
print("Numpy Array SELL Index is:", np_t_sell_ind)
print("DataFrame SELL Index is:", np_t_sell_ind + 1, "displaying DF \n")
print("First Sell Index after Trade open in in candle", np_t_sell_ind + 1)
print("HINT, if exit is SELL (not stop) trade should use OPEN price from next candle",
np_t_sell_ind + 2, "\n")
sl_is = np_t_sell_ind - 1 # Print sell index start, line before
sl_if = np_t_sell_ind + 3 # Print sell index finish, line after
print(bslap.iloc[sl_is:sl_if], "\n")
# Chosen Exit (stop or sell)
print("=================== EXIT ", pair)
print("Exit type is :", t_exit_type)
# print((bslap.iloc[t_exit_ind], "\n"))
print("trade exit price field is", np_t_exit_pri, "\n")
'''
Trade entry is always the next candles "open" price
We trigger on close, so cannot see that till after
its closed.
The exception to this is a STOP which is calculated in candle
'''
if debug_timing:
t_t = f(st)
print("6-depra", str.format('{0:.17f}', t_t))
st = s()
## use numpy view "np_t_open_v" for speed. Columns are
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
# exception is 6 which is use the stop value.
np_trade_enter_price = np_bslap[t_open_ind + 1, np_open]
if t_exit_type == 'stop':
if np_t_exit_pri == 6:
np_trade_exit_price = np_t_stop_pri
else:
np_trade_exit_price = np_bslap[t_exit_ind, np_t_exit_pri]
if t_exit_type == 'sell':
np_trade_exit_price = np_bslap[t_exit_ind + 1, np_t_exit_pri]
if debug_timing:
t_t = f(st)
print("7-numpy", str.format('{0:.17f}', t_t))
st = s()
if debug:
print("//////////////////////////////////////////////")
print("+++++++++++++++++++++++++++++++++ Trade Enter ")
print("np_trade Enterprice is ", str.format('{0:.17f}', np_trade_enter_price))
print("--------------------------------- Trade Exit ")
print("Trade Exit Type is ", t_exit_type)
print("np_trade Exit Price is", str.format('{0:.17f}', np_trade_exit_price))
print("//////////////////////////////////////////////")
# Loop control - catch no closed trades.
if debug:
print("---------------------------------------- end of loop", loop - 1,
" Dataframe Exit Index is: ", t_exit_ind)
print("Exit Index Last, Exit Index Now Are: ", t_exit_last, t_exit_ind)
if t_exit_last >= t_exit_ind:
"""
When last trade exit equals index of last exit, there is no
opportunity to close any more trades.
Break loop and go on to next pair.
TODO
add handing here to record none closed open trades
"""
if debug:
print(bslap_pair_results)
break
else:
"""
Add trade to backtest looking results list of dicts
Loop back to look for more trades.
"""
# Index will change if incandle stop or look back as close Open and Sell
if t_exit_type == 'stop':
close_index: int = t_exit_ind + 1
elif t_exit_type == 'sell':
close_index: int = t_exit_ind + 2
else:
close_index: int = t_exit_ind + 1
# Munge the date / delta
start_date: str = bslap.iloc[t_open_ind + 1]['date']
end_date: str = bslap.iloc[close_index]['date']
def __datetime(date_str):
return datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S+00:00')
# trade_start = __datetime(start_date)
# trade_end = __datetime(end_date)
# trade_mins = (trade_end - trade_start).total_seconds() / 60
# build trade dictionary
bslap_result["pair"] = pair
bslap_result["profit_percent"] = ( np_trade_exit_price - np_trade_enter_price) // np_trade_enter_price * 100
bslap_result["profit_abs"] = ""
bslap_result["open_time"] = start_date
bslap_result["close_time"] = end_date
bslap_result["open_index"] = t_open_ind + 1
bslap_result["close_index"] = close_index
# bslap_result["trade_duration"] = trade_mins
bslap_result["open_at_end"] = False
bslap_result["open_rate"] = str.format('{0:.10f}', np_trade_enter_price)
bslap_result["close_rate"] = str.format('{0:.10f}', np_trade_exit_price)
bslap_result["exit_type"] = t_exit_type
# Add trade dictionary to list
bslap_pair_results.append(bslap_result)
if debug:
print(bslap_pair_results)
"""
Loop back to start. t_exit_last becomes where loop
will seek to open new trades from.
Push index on 1 to not open on close
"""
t_exit_last = t_exit_ind + 1
if debug_timing:
t_t = f(st)
print("8", str.format('{0:.17f}', t_t))
# Send back List of trade dicts
return bslap_pair_results
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None: def start(self) -> None:
""" """