Timeframe
5m
Direction
Long Only
Stoploss
-4.0%
Trailing Stop
No
ROI
0m: 1000.0%
Interface Version
2
Startup Candles
200
Indicators
3
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
from freqtrade.strategy.interface import IStrategy
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 datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter
# NASMAO
# source: -
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
class NASMAO(IStrategy):
INTERFACE_VERSION = 2
# Buy hyperspace params:
buy_params = {
"base_nb_candles_buy": 42,
"ewo_high": 8.642,
"ewo_high_2": 0.968,
"ewo_low": -8.197,
"low_offset": 0.984,
"low_offset_2": 0.986,
"rsi_buy": 46,
}
# Sell hyperspace params:
sell_params = {
"base_nb_candles_sell": 79,
"high_offset": 1.002,
"high_offset_2": 1.038,
}
# ROI table:
minimal_roi = {"0": 10}
# Stoploss:
stoploss = -0.04
# SMAOffset
base_nb_candles_buy = IntParameter(
5, 80, default=buy_params["base_nb_candles_buy"], space="buy", optimize=True
)
base_nb_candles_sell = IntParameter(
5, 80, 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=True
)
low_offset_2 = DecimalParameter(
0.9, 0.99, default=buy_params["low_offset_2"], space="buy", optimize=True
)
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
ewo_low = DecimalParameter(
-20.0, -8.0, default=buy_params["ewo_low"], space="buy", optimize=True
)
ewo_high = DecimalParameter(
2.0, 12.0, default=buy_params["ewo_high"], space="buy", optimize=True
)
ewo_high_2 = DecimalParameter(
-6.0, 12.0, default=buy_params["ewo_high_2"], space="buy", optimize=True
)
rsi_buy = IntParameter(
30, 70, default=buy_params["rsi_buy"], space="buy", optimize=True
)
# Trailing stop:
trailing_stop = False
# trailing_stop_positive = 0.005
# trailing_stop_positive_offset = 0.03
# 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
slippage_protection = {"retries": 3, "max_slippage": -0.02}
buy_signals = {}
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 populate_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.copy()
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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
)
)
),
["buy", "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)
),
["buy", "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
)
)
),
["buy", "buy_tag"],
] = (1, "ewolow")
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), "sell"] = 1
return dataframe