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WTHO.py
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WTHO.py
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from pandas import DataFrame
from functools import reduce
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
#Start Strategy
class WTHO(IStrategy):
minimal_roi = {
"0": 0.10
}
stoploss = -0.025
timeframe = '2h'
### hyper-opt parameters ###
# entry optizimation
max_epa = CategoricalParameter([-1, 0, 1, 3, 5, 10], default=1, space="buy", optimize=True)
# protections
cooldown_lookback = IntParameter(2, 48, default=5, space="protection", optimize=True)
stop_duration = IntParameter(12, 200, default=5, space="protection", optimize=True)
use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True)
# indicators
buy_esa_length = IntParameter(3, 20, default=10, optimize=True)
buy_d_length = IntParameter(3, 20, default=10, optimize=True)
buy_wt1_length = IntParameter(15, 50, default=21, optimize=True)
buy_wt2_length = IntParameter(3, 14, default=4, optimize=True)
mfi_length = IntParameter(6, 60, default=40, optimize=True)
mfi_sma_length = IntParameter(6, 60, default=15, optimize=True)
sma_length = IntParameter(10, 50, default=25, optimize=True)
# values
# buy
wt_os = IntParameter(-55, 1 , default=-50, space="buy", optimize=True)
wt_os_sma = IntParameter(-30, 50 , default=0, space="buy", optimize=True)
buy_wt_pc = IntParameter(-30, 10 , default=0, space="buy", optimize=True)
buy_mfi = IntParameter(0, 60 , default=50, space="buy", optimize=True)
buy_mfi_slope = IntParameter(-20, 20 , default=0, space="buy", optimize=True)
buy_sma_pc = IntParameter(-10, 10 , default=0, space="buy", optimize=True)
# sell
wt_ob = IntParameter(20, 55 , default=50, space="sell", optimize=True)
wt_ob_sma = IntParameter(-20, 55 , default=0, space="sell", optimize=True)
sell_wt_pc = IntParameter(-10, 30 , default=0, space="sell", optimize=True)
sell_mfi = IntParameter(40, 100 , default=70, space="sell", optimize=True)
sell_mfi_slope = IntParameter(-20, 20 , default=0, space="sell", optimize=True)
sell_sma_pc = IntParameter(-10, 10 , default=0, space="sell", optimize=True)
### entry opt.
@property
def max_entry_position_adjustment(self):
return self.max_epa.value
### protections ###
@property
def protections(self):
prot = []
prot.append({
"method": "CooldownPeriod",
"stop_duration_candles": self.cooldown_lookback.value
})
if self.use_stop_protection.value:
prot.append({
"method": "StoplossGuard",
"lookback_period_candles": 24 * 3,
"trade_limit": 4,
"stop_duration_candles": self.stop_duration.value,
"only_per_pair": False
})
return prot
### indicators ###
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""Generate all indicators used by the strategy"""
### WaveTrend using OHLC4 or HA close
ap = (0.25 * (dataframe['high'] + dataframe['low'] + dataframe["close"] + dataframe["open"]))
# find ema length for esa
for vale in self.buy_esa_length.range:
dataframe[f'esa_{vale}'] = ta.EMA(ap, timeperiod = vale)
# find ema length for d
for vald in self.buy_d_length.range:
dataframe[f'd_{vald}'] = ta.EMA(abs(ap - dataframe[f'esa_{vale}']), timeperiod = vald)
dataframe['wave_ci'] = (ap-dataframe[f'esa_{vale}']) / (0.015 * dataframe[f'd_{vald}'])
# find t1 optimimum length
for valt1 in self.buy_wt1_length.range:
dataframe[f'wave_t1_{valt1}'] = ta.EMA(dataframe['wave_ci'], timeperiod = valt1)
# find t2 optimimum length
for valt2 in self.buy_wt2_length.range:
dataframe[f'wave_t2_{valt2}'] = ta.SMA(dataframe[f'wave_t1_{valt1}'], timeperiod = valt2)
dataframe["wave_t1_pc"] = round(
(dataframe[f'wave_t1_{valt1}'] - dataframe[f'wave_t1_{valt1}'].shift()) / abs(dataframe[f'wave_t1_{valt1}']) * 100, 2)
### Money Flow Index
for valmfi in self.mfi_length.range:
dataframe[f'mfi_{valmfi}'] = ta.MFI(dataframe, timeperiod = valmfi)
for valmfisma in self.mfi_sma_length.range:
dataframe[f'mfi_sma{valmfisma}'] = ta.SMA(dataframe[f'mfi_{valmfi}'], timeperiod = valmfisma)
dataframe['mfi_pc'] = round(
(dataframe[f'mfi_sma{valmfisma}'] - dataframe[f'mfi_sma{valmfisma}'].shift()) / abs(dataframe[f'mfi_sma{valmfisma}']) * 100, 2)
### SMA Decision Monitor OS - OB Levels Decision Monitor
# find optimum SMA length
for valsma in self.sma_length.range:
dataframe[f'sma{valsma}'] = ta.SMA(dataframe, timeperiod = valsma)
dataframe[f"sma_{valsma}pc"] = round(
(dataframe[f'sma{valsma}'] - dataframe[f'sma{valsma}'].shift()) / abs(dataframe[f'sma{valsma}']) * 100, 2)
# if dataframe[f'sma_{self.sma_length.value}pc'] > self.buy_sma_pc.value:
# dataframe['buy_os'] = self.wt_os_sma.value
# else:
# dataframe['buy_os']= self.wt_os.value
return dataframe
### buy logic ###
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# # Wave Trend
# dataframe.loc[
# (
# (dataframe[f'sma_{self.sma_length.value}pc'] >= self.buy_sma_pc.value) &
# (dataframe[f'wave_t1_{self.buy_wt1_length.value}'] <= self.wt_os.value ) &
# (dataframe[f'wave_t1_{self.buy_wt1_length.value}'] > dataframe[f'wave_t2_{self.buy_wt2_length.value}']) &
# (dataframe['wave_t1_pc'] >= self.buy_wt_pc.value) &
# (dataframe[f'mfi_sma{self.mfi_sma_length.value}'] <= self.buy_mfi.value ) &
# (dataframe['mfi_pc'] >= self.buy_mfi_slope.value ) &
# (dataframe['volume'] > 0)
# ),
# ['buy', 'buy_tag']] = (1, 'Bear')
# dataframe.loc[
# (
# (dataframe[f'sma_{self.sma_length.value}pc'] > self.buy_sma_pc.value) &
# (dataframe[f'wave_t1_{self.buy_wt1_length.value}'] <= self.wt_os_sma.value ) &
# (dataframe[f'wave_t1_{self.buy_wt1_length.value}'] > self.wt_os.value ) &
# (dataframe[f'wave_t1_{self.buy_wt1_length.value}'] > dataframe[f'wave_t2_{self.buy_wt2_length.value}']) &
# (dataframe['wave_t1_pc'] >= self.buy_wt_pc.value) &
# (dataframe[f'mfi_sma{self.mfi_sma_length.value}'] <= self.buy_mfi.value ) &
# (dataframe['mfi_pc'] >= self.buy_mfi_slope.value ) &
# (dataframe['volume'] > 0)
# ),
# ['buy', 'buy_tag']] = (1, 'Bull')
# conditions.append(dataframe[f'wave_t1_{self.buy_wt1_length.value}'] >= self.wt_os.value )
conditions.append(dataframe[f'wave_t1_{self.buy_wt1_length.value}'] > dataframe[f'wave_t2_{self.buy_wt2_length.value}'])
conditions.append(dataframe['wave_t1_pc'] >= self.buy_wt_pc.value)
# Money Flow Index
conditions.append(dataframe[f'mfi_sma{self.mfi_sma_length.value}'] <= self.buy_mfi.value )
conditions.append(dataframe['mfi_pc'] >= self.buy_mfi_slope.value )
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'enter_long'] = 1
return dataframe
### sell logic ###
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# # Wave Trend
# dataframe.loc[
# (
# (dataframe[f'sma_{self.sma_length.value}pc'] <= self.sell_sma_pc.value) &
# (dataframe[f'wave_t1_{self.buy_wt1_length.value}'] >= self.wt_ob.value ) &
# (dataframe[f'wave_t1_{self.buy_wt1_length.value}'] < dataframe[f'wave_t2_{self.buy_wt2_length.value}']) &
# (dataframe['wave_t1_pc'] <= self.sell_wt_pc.value) &
# (dataframe[f'mfi_sma{self.mfi_sma_length.value}'] >= self.sell_mfi.value ) &
# (dataframe['mfi_pc'] <= self.sell_mfi_slope.value ) &
# (dataframe['volume'] > 0)
# ),
# ['buy', 'buy_tag']] = (1, 'Bull')
# dataframe.loc[
# (
# (dataframe[f'sma_{self.sma_length.value}pc'] < self.sell_sma_pc.value) &
# (dataframe[f'wave_t1_{self.buy_wt1_length.value}'] <= self.wt_ob.value ) &
# (dataframe[f'wave_t1_{self.buy_wt1_length.value}'] >= self.wt_ob_sma.value ) &
# (dataframe[f'wave_t1_{self.buy_wt1_length.value}'] < dataframe[f'wave_t2_{self.buy_wt2_length.value}']) &
# (dataframe['wave_t1_pc'] <= self.sell_wt_pc.value) &
# (dataframe[f'mfi_sma{self.mfi_sma_length.value}'] >= self.sell_mfi.value ) &
# (dataframe['mfi_pc'] <= self.sell_mfi_slope.value ) &
# (dataframe['volume'] > 0)
# ),
# ['buy', 'buy_tag']] = (1, 'Bear')
# #Wave Trend
# if (dataframe['sma_pc'] < self.sell_sma_pc.value) and (dataframe[f'wave_t1_{self.sell_wt1_length.value}'] < self.wt_ob.value ):
# conditions.append(dataframe[f'wave_t1_{self.self_wt1_length.value}'] >= self.wt_ob_sma.value )
# else:
# conditions.append(dataframe[f'wave_t1_{self.buy_wt1_length.value}'] <= self.wt_ob.value)
conditions.append(dataframe[f'wave_t2_{self.buy_wt2_length.value}'] <= dataframe[f'wave_t1_{self.buy_wt1_length.value}'])
conditions.append(dataframe['wave_t1_pc'] <= self.sell_wt_pc.value)
# Money Flow Index
conditions.append(dataframe[f'mfi_sma{self.mfi_sma_length.value}'] >= self.sell_mfi.value )
conditions.append(dataframe['mfi_pc'] <= self.sell_mfi_slope.value )
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'exit_long'] = 1
return dataframe
### REV1
# +--------+-------------+----------+------------------+--------------+-------------------------------+------------------+-------------+-------------------------------+
# | Best | Epoch | Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Objective | Max Drawdown (Acct) |
# |--------+-------------+----------+------------------+--------------+-------------------------------+------------------+-------------+-------------------------------|
# | * Best | 5/25000 | 23 | 14 2 7 | -1.45% | -37.280 USDT (-3.73%) | 26 days 19:34:00 | 37.2803 | 61.865 USDT (6.04%) |
# | * Best | 9/25000 | 20 | 17 1 2 | 5.88% | 120.874 USDT (12.09%) | 15 days 15:00:00 | -120.874 | 28.842 USDT (2.67%) |
# | Best | 41/25000 | 23 | 17 4 2 | 5.43% | 130.297 USDT (13.03%) | 24 days 11:13:00 | -130.297 | 22.262 USDT (1.93%) |
# | Best | 83/25000 | 26 | 20 3 3 | 5.74% | 156.108 USDT (15.61%) | 17 days 05:05:00 | -156.108 | 30.312 USDT (2.67%) |
# | Best | 114/25000 | 23 | 19 2 2 | 7.32% | 179.738 USDT (17.97%) | 17 days 02:52:00 | -179.738 | 28.499 USDT (2.50%) |
# | Best | 135/25000 | 25 | 21 2 2 | 7.41% | 196.791 USDT (19.68%) | 16 days 09:36:00 | -196.791 | 28.446 USDT (2.50%) |
# | Best | 159/25000 | 23 | 20 2 1 | 8.36% | 204.810 USDT (20.48%) | 14 days 03:39:00 | -204.81 | 28.842 USDT (2.51%) |
# | Best | 263/25000 | 24 | 21 2 1 | 8.97% | 230.471 USDT (23.05%) | 13 days 17:30:00 | -230.471 | 28.842 USDT (2.51%) |
# | Best | 312/25000 | 24 | 22 1 1 | 9.90% | 257.428 USDT (25.74%) | 12 days 22:45:00 | -257.428 | 11.006 USDT (0.87%) |
# | Best | 947/25000 | 22 | 21 1 0 | 11.60% | 275.747 USDT (27.57%) | 10 days 21:16:00 | -275.747 | -- |
# | Best | 2184/25000 | 23 | 22 1 0 | 11.32% | 282.151 USDT (28.22%) | 11 days 00:31:00 | -282.151 | -- |
# | Best | 9234/25000 | 26 | 25 0 1 | 10.49% | 297.134 USDT (29.71%) | 8 days 13:37:00 | -297.134 | 0.260 USDT (0.02%) |
# Best result:
# 9234/25000: 26 trades. 25/0/1 Wins/Draws/Losses. Avg profit 10.49%. Median profit 12.28%. Total profit 297.13362921 USDT ( 29.71%). Avg duration 8 days, 13:37:00 min. Objective: -297.13363
# # Buy hyperspace params:
# buy_params = {
# "buy_d_length": 3,
# "buy_esa_length": 10,
# "buy_mfi": 53,
# "buy_mfi_slope": 0,
# "buy_wt1_length": 26,
# "buy_wt2_length": 14,
# "buy_wt_pc": -6,
# "wt_os": -47,
# }
# # Sell hyperspace params:
# sell_params = {
# "sell_mfi": 45,
# "sell_mfi_slope": 0,
# "sell_wt_pc": 30,
# "wt_ob": 46,
# }
# # ROI table: # value loaded from strategy
# minimal_roi = {
# "0": 0.258,
# "6213": 0.123,
# "20752": 0.052,
# "51551": 0
# }
# # Stoploss:
# stoploss = -0.281 # value loaded from strategy
# # Trailing stop:
# trailing_stop = True # value loaded from strategy
# trailing_stop_positive = 0.327 # value loaded from strategy
# trailing_stop_positive_offset = 0.348 # value loaded from strategy
# trailing_only_offset_is_reached = True # value loaded from strategy
# no zones - 6h 900 days
# 2022-11-18 20:44:37,399 - freqtrade.data.history.idatahandler - WARNING - QNT/USDT, spot, 6h, data starts at 2021-02-07 12:00:00
# 2022-11-18 20:44:37,419 - freqtrade.optimize.backtesting - INFO - Loading data from 2020-05-31 00:00:00 up to 2022-10-31 00:00:00 (883 days).
# 2022-11-18 20:44:37,419 - freqtrade.optimize.hyperopt - INFO - Dataload complete. Calculating indicators
# 2022-11-18 20:44:38,590 - freqtrade.optimize.hyperopt - INFO - Hyperopting with data from 2020-05-31 00:00:00 up to 2022-10-31 00:00:00 (883 days)..
# 2022-11-18 20:44:38,672 - freqtrade.optimize.hyperopt - INFO - Found 4 CPU cores. Let's make them scream!
# 2022-11-18 20:44:38,672 - freqtrade.optimize.hyperopt - INFO - Number of parallel jobs set as: -1
# 2022-11-18 20:44:38,672 - freqtrade.optimize.hyperopt - INFO - Using estimator ET.
# 2022-11-18 20:44:38,685 - freqtrade.optimize.hyperopt - INFO - Effective number of parallel workers used: 4
# +--------+-----------+----------+------------------+--------------+-------------------------------+-----------------+-------------+-------------------------------+
# | Best | Epoch | Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Objective | Max Drawdown (Acct) |
# |--------+-----------+----------+------------------+--------------+-------------------------------+-----------------+-------------+-------------------------------|
# | * Best | 2/2500 | 1 | 0 1 0 | 0.00% | -- | 5 days 12:00:00 | -0 | -- |
# | * Best | 7/2500 | 1 | 1 0 0 | 21.36% | 8.474 USDT (0.85%) | 1 days 18:00:00 | -8.4744 | -- |
# | * Best | 10/2500 | 258 | 59 3 196 | 0.51% | 49.312 USDT (4.93%) | 1 days 19:08:00 | -49.3118 | 124.140 USDT (11.03%) |
# | * Best | 13/2500 | 204 | 133 38 33 | 1.35% | 105.040 USDT (10.50%) | 7 days 17:46:00 | -105.04 | 100.048 USDT (8.38%) |
# | Best | 358/2500 | 511 | 295 133 83 | 1.31% | 224.726 USDT (22.47%) | 8 days 07:41:00 | -224.726 | 712.145 USDT (39.88%) |
# [Epoch 2500 of 2500 (100%)] || | [Time: 7:56:09, Elapsed Time: 7:56:09]
# 2022-11-19 04:40:54,230 - freqtrade.optimize.hyperopt - INFO - 2500 epochs saved to '/home/core/freqtrade/freqtrade/user_data/hyperopt_results/strategy_WTHO_2022-11-18_20-44-36.fthypt'.
# 2022-11-19 04:40:54,306 - freqtrade.resolvers.iresolver - WARNING - Could not import /home/core/freqtrade/freqtrade/user_data/strategies/BB_RPB_TSL_SMA_Tranz_1.py due to 'No module named 'finta''
# 2022-11-19 04:40:54,307 - freqtrade.optimize.hyperopt_tools - INFO - Dumping parameters to /home/core/freqtrade/freqtrade/user_data/strategies/WTHO.json
# Best result:
# 358/2500: 511 trades. 295/133/83 Wins/Draws/Losses. Avg profit 1.31%. Median profit 7.58%. Total profit 224.72565930 USDT ( 22.47%). Avg duration 8 days, 7:41:00 min. Objective: -224.72566
# # Buy hyperspace params:
# buy_params = {
# "buy_d_length": 12,
# "buy_esa_length": 6,
# "buy_mfi": 52,
# "buy_mfi_slope": -1,
# "buy_sma_pc": -3,
# "buy_wt1_length": 24,
# "buy_wt2_length": 4,
# "buy_wt_pc": -21,
# "max_epa": 5,
# "wt_os": -22,
# "wt_os_sma": -25,
# }
# # Sell hyperspace params:
# sell_params = {
# "sell_mfi": 75,
# "sell_mfi_slope": -18,
# "sell_sma_pc": 1,
# "sell_wt_pc": 12,
# "wt_ob": 39,
# "wt_ob_sma": 25,
# }
# # Protection hyperspace params:
# protection_params = {
# "cooldown_lookback": 25,
# "stop_duration": 142,
# "use_stop_protection": True,
# }
# # ROI table:
# minimal_roi = {
# "0": 0.327,
# "1717": 0.263,
# "4302": 0.076,
# "11290": 0
# }
# # Stoploss:
# stoploss = -0.31
# # Trailing stop:
# trailing_stop = True
# trailing_stop_positive = 0.261
# trailing_stop_positive_offset = 0.355
# trailing_only_offset_is_reached = True