Timeframe
5m
Direction
Long Only
Stoploss
-15.0%
Trailing Stop
No
ROI
0m: 1000.0%
Interface Version
3
Startup Candles
200
Indicators
3
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
from logging import FATAL
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import (
stoploss_from_open,
merge_informative_pair,
DecimalParameter,
IntParameter,
CategoricalParameter,
)
import technical.indicators as ftt
# NASOSv7
# source: https://strat.ninja/overview.php?strategy=NASOSv7
class NASOS7(IStrategy):
INTERFACE_VERSION = 3
# Buy hyperspace params:
buy_params = {
"base_nb_candles_buy": 8,
"ewo_high": 2.403,
"ewo_high_2": -5.585,
"ewo_low": -14.378,
"lookback_candles": 3,
"low_offset": 0.984,
"low_offset_2": 0.942,
"profit_threshold": 1.008,
"rsi_buy": 72,
}
# Sell hyperspace params:
sell_params = {
"base_nb_candles_sell": 16,
"high_offset": 1.084,
"high_offset_2": 1.401,
"pHSL": -0.15,
"pPF_1": 0.016,
"pPF_2": 0.024,
"pSL_1": 0.014,
"pSL_2": 0.022,
}
# ROI table:
minimal_roi = {"0": 10}
# Stoploss:
stoploss = -0.15
# SMAOffset
base_nb_candles_buy = IntParameter(
2, 20, default=buy_params["base_nb_candles_buy"], space="buy", optimize=True
)
base_nb_candles_sell = IntParameter(
2, 25, default=sell_params["base_nb_candles_sell"], space="sell", optimize=True
)
low_offset = DecimalParameter(
0.9, 0.99, default=buy_params["low_offset"], space="buy", optimize=False
)
low_offset_2 = DecimalParameter(
0.9, 0.99, default=buy_params["low_offset_2"], space="buy", optimize=False
)
high_offset = DecimalParameter(
0.95, 1.1, default=sell_params["high_offset"], space="sell", optimize=True
)
high_offset_2 = DecimalParameter(
0.99, 1.5, default=sell_params["high_offset_2"], space="sell", optimize=True
)
# Protection
fast_ewo = 50
slow_ewo = 200
lookback_candles = IntParameter(
1, 24, default=buy_params["lookback_candles"], space="buy", optimize=True
)
profit_threshold = DecimalParameter(
1.0, 1.03, default=buy_params["profit_threshold"], space="buy", optimize=True
)
ewo_low = DecimalParameter(
-20.0, -8.0, default=buy_params["ewo_low"], space="buy", optimize=False
)
ewo_high = DecimalParameter(
2.0, 12.0, default=buy_params["ewo_high"], space="buy", optimize=False
)
ewo_high_2 = DecimalParameter(
-6.0, 12.0, default=buy_params["ewo_high_2"], space="buy", optimize=False
)
rsi_buy = IntParameter(
50, 100, default=buy_params["rsi_buy"], space="buy", optimize=False
)
# trailing stoploss hyperopt parameters
# hard stoploss profit
pHSL = DecimalParameter(
-0.200,
-0.040,
default=-0.15,
decimals=3,
space="sell",
optimize=False,
load=True,
)
# profit threshold 1, trigger point, SL_1 is used
pPF_1 = DecimalParameter(
0.008, 0.020, default=0.016, decimals=3, space="sell", optimize=False, load=True
)
pSL_1 = DecimalParameter(
0.008, 0.020, default=0.014, decimals=3, space="sell", optimize=False, load=True
)
# profit threshold 2, SL_2 is used
pPF_2 = DecimalParameter(
0.040, 0.100, default=0.024, decimals=3, space="sell", optimize=False, load=True
)
pSL_2 = DecimalParameter(
0.020, 0.070, default=0.022, decimals=3, space="sell", optimize=False, load=True
)
# Trailing stop:
trailing_stop = False
# trailing_stop_positive = 0.001
# trailing_stop_positive_offset = 0.016
# trailing_only_offset_is_reached = True
# Sell signal
use_exit_signal = True
exit_profit_only = False
exit_profit_offset = 0.01
ignore_roi_if_entry_signal = False
# Optimal timeframe for the strategy
timeframe = "5m"
inf_1h = "1h"
process_only_new_candles = True
startup_candle_count = 200
def confirm_trade_exit(
self,
pair: str,
trade: Trade,
order_type: str,
amount: float,
rate: float,
time_in_force: str,
sell_reason: str,
current_time: datetime,
**kwargs,
) -> bool:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1]
if last_candle is not None:
if sell_reason in ["sell_signal"]:
if (last_candle["hma_50"] * 1.149 > last_candle["ema_100"]) and (
last_candle["close"] < last_candle["ema_100"] * 0.951
): # *1.2
return False
# slippage
try:
state = self.slippage_protection["__pair_retries"]
except KeyError:
state = self.slippage_protection["__pair_retries"] = {}
candle = dataframe.iloc[-1].squeeze()
slippage = (rate / candle["close"]) - 1
if slippage < self.slippage_protection["max_slippage"]:
pair_retries = state.get(pair, 0)
if pair_retries < self.slippage_protection["retries"]:
state[pair] = pair_retries + 1
return False
state[pair] = 0
return True
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, "1h") for pair in pairs]
return informative_pairs
def informative_1h_indicators(
self, dataframe: DataFrame, metadata: dict
) -> DataFrame:
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_1h = self.dp.get_pair_dataframe(
pair=metadata["pair"], timeframe=self.inf_1h
)
return informative_1h
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Calculate all ma_buy values
for val in self.base_nb_candles_buy.range:
dataframe[f"ma_buy_{val}"] = ta.EMA(dataframe, timeperiod=val)
# Calculate all ma_sell values
for val in self.base_nb_candles_sell.range:
dataframe[f"ma_sell_{val}"] = ta.EMA(dataframe, timeperiod=val)
dataframe["hma_50"] = qtpylib.hull_moving_average(dataframe["close"], window=50)
dataframe["ema_100"] = ta.EMA(dataframe, timeperiod=100)
dataframe["sma_9"] = ta.SMA(dataframe, timeperiod=9)
# Elliot
dataframe["EWO"] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
# RSI
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
dataframe["rsi_fast"] = ta.RSI(dataframe, timeperiod=4)
dataframe["rsi_slow"] = ta.RSI(dataframe, timeperiod=20)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
informative_1h = self.informative_1h_indicators(dataframe, metadata)
dataframe = merge_informative_pair(
dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True
)
# The indicators for the normal (5m) timeframe
dataframe = self.normal_tf_indicators(dataframe, metadata)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dont_buy_conditions = []
dont_buy_conditions.append(
(
# don't buy if there isn't 3% profit to be made
(
dataframe["close_1h"].rolling(self.lookback_candles.value).max()
< (dataframe["close"] * self.profit_threshold.value)
)
)
)
dataframe.loc[
(
(dataframe["rsi_fast"] < 35)
& (
dataframe["close"]
< (
dataframe[f"ma_buy_{self.base_nb_candles_buy.value}"]
* self.low_offset.value
)
)
& (dataframe["EWO"] > self.ewo_high.value)
& (dataframe["rsi"] < self.rsi_buy.value)
& (dataframe["volume"] > 0)
& (
dataframe["close"]
< (
dataframe[f"ma_sell_{self.base_nb_candles_sell.value}"]
* self.high_offset.value
)
)
),
["enter_long", "buy_tag"],
] = (1, "ewo1")
dataframe.loc[
(
(dataframe["rsi_fast"] < 35)
& (
dataframe["close"]
< (
dataframe[f"ma_buy_{self.base_nb_candles_buy.value}"]
* self.low_offset_2.value
)
)
& (dataframe["EWO"] > self.ewo_high_2.value)
& (dataframe["rsi"] < self.rsi_buy.value)
& (dataframe["volume"] > 0)
& (
dataframe["close"]
< (
dataframe[f"ma_sell_{self.base_nb_candles_sell.value}"]
* self.high_offset.value
)
)
& (dataframe["rsi"] < 25)
),
["enter_long", "buy_tag"],
] = (1, "ewo2")
dataframe.loc[
(
(dataframe["rsi_fast"] < 35)
& (
dataframe["close"]
< (
dataframe[f"ma_buy_{self.base_nb_candles_buy.value}"]
* self.low_offset.value
)
)
& (dataframe["EWO"] < self.ewo_low.value)
& (dataframe["volume"] > 0)
& (
dataframe["close"]
< (
dataframe[f"ma_sell_{self.base_nb_candles_sell.value}"]
* self.high_offset.value
)
)
),
["enter_long", "buy_tag"],
] = (1, "ewolow")
if dont_buy_conditions:
for condition in dont_buy_conditions:
dataframe.loc[condition, "enter_long"] = 0
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
(
(dataframe["close"] > dataframe["sma_9"])
& (
dataframe["close"]
> (
dataframe[f"ma_sell_{self.base_nb_candles_sell.value}"]
* self.high_offset_2.value
)
)
& (dataframe["rsi"] > 50)
& (dataframe["volume"] > 0)
& (dataframe["rsi_fast"] > dataframe["rsi_slow"])
)
| (
(dataframe["close"] < dataframe["hma_50"])
& (
dataframe["close"]
> (
dataframe[f"ma_sell_{self.base_nb_candles_sell.value}"]
* self.high_offset.value
)
)
& (dataframe["volume"] > 0)
& (dataframe["rsi_fast"] > dataframe["rsi_slow"])
)
)
if conditions:
dataframe.loc[reduce(lambda x, y: x | y, conditions), "exit_long"] = 1
return dataframe
def EWO(dataframe, ema_length=5, ema2_length=35):
df = dataframe.copy()
ema1 = ta.EMA(df, timeperiod=ema_length)
ema2 = ta.EMA(df, timeperiod=ema2_length)
emadif = (ema1 - ema2) / df["low"] * 100
return emadif