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OBOnly.py
93 lines (70 loc) · 2.79 KB
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OBOnly.py
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import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import timeframe_to_prev_date
import os
from pandas import DataFrame
from datetime import datetime, timedelta
from freqtrade.data.converter import order_book_to_dataframe
class OBOnly(IStrategy):
INTERFACE_VERSION = 2
minimal_roi = {
"0": 0.01,
"10": 0.007,
"15": 0.0035,
"25": 0.002,
}
stoploss = -0.01 # effectively disabled.
timeframe = '1h'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 50
ob_history={}
ob_delta_bid=0.005
ob_delta_ask=0.02
ob_ratio=1.
ob_blend=1
ob_log=True
def confirm_trade_entry(self, pair: str, order_type: str, amount: float,
rate: float, time_in_force: str, current_time, **kwargs) -> bool:
ob = self.dp.orderbook(pair,1000)
ob_dp=order_book_to_dataframe(ob['bids'],ob['asks'])
mid_price=(ob_dp['bids'][0]+ob_dp['asks'][0])/2
bid_cut = mid_price - mid_price*self.ob_delta_bid
ask_cut = mid_price + mid_price*self.ob_delta_ask
bid_side=ob_dp[ob_dp['bids']>bid_cut]['b_sum']
ask_side=ob_dp[ob_dp['asks']<ask_cut]['a_sum']
if ask_side.count() == 1000 or bid_side.count() == 1000:
return False
ask_side=ask_side.tail(1).item()
bid_side=bid_side.tail(1).item()
r=bid_side/ask_side
self.ob_history[pair]=(1.0-self.ob_blend)*self.ob_history.get(pair.replace("BUSD","USDT"),0)+self.ob_blend*r
if( self.ob_history[pair]> self.ob_ratio):
if self.ob_log:
dp_dir = "depth/"+pair+"/"
try:
os.makedirs(dp_dir)
except OSError:
pass
ob_dp.to_csv(dp_dir+"/"+current_time.strftime("buy_%m_%d_%Y_%H_%M")+".csv")
f=open("log.dry.log", "a+")
f.write(f"{str(current_time)} - buying {pair} {self.ob_history[pair]} {r} {rate} \n")
f.close()
self.ob_history[pair]=0
return True
return False
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe [
'buy'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe[
'sell'
] = 0
return dataframe