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Apollo11.py
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Apollo11.py
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from datetime import datetime
from datetime import timedelta
from functools import reduce
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy
from pandas import DataFrame
def to_minutes(**timdelta_kwargs):
return int(timedelta(**timdelta_kwargs).total_seconds() / 60)
class Apollo11(IStrategy):
# Strategy created by Shane Jones https://twitter.com/shanejones
#
# Assited by a number of contributors https://github.com/shanejones/goddard/graphs/contributors
#
# Original repo hosted at https://github.com/shanejones/goddard
timeframe = "15m"
# Stoploss
stoploss = -0.16
startup_candle_count: int = 480
trailing_stop = False
use_custom_stoploss = True
use_sell_signal = False
# signal controls
buy_signal_1 = True
buy_signal_2 = True
buy_signal_3 = True
# ROI table:
minimal_roi = {
"0": 10, # This is 10000%, which basically disables ROI
}
# Indicator values:
# Signal 1
s1_ema_xs = 3
s1_ema_sm = 5
s1_ema_md = 10
s1_ema_xl = 50
s1_ema_xxl = 240
# Signal 2
s2_ema_input = 50
s2_ema_offset_input = -1
s2_bb_sma_length = 49
s2_bb_std_dev_length = 64
s2_bb_lower_offset = 3
s2_fib_sma_len = 50
s2_fib_atr_len = 14
s2_fib_lower_value = 4.236
s3_ema_long = 50
s3_ema_short = 20
s3_ma_fast = 10
s3_ma_slow = 20
@property
def protections(self):
return [
{
# Don't enter a trade right after selling a trade.
"method": "CooldownPeriod",
"stop_duration": to_minutes(minutes=0),
},
{
# Stop trading if max-drawdown is reached.
"method": "MaxDrawdown",
"lookback_period": to_minutes(hours=12),
"trade_limit": 20, # Considering all pairs that have a minimum of 20 trades
"stop_duration": to_minutes(hours=1),
"max_allowed_drawdown": 0.2, # If max-drawdown is > 20% this will activate
},
{
# Stop trading if a certain amount of stoploss occurred within a certain time window.
"method": "StoplossGuard",
"lookback_period": to_minutes(hours=6),
"trade_limit": 4, # Considering all pairs that have a minimum of 4 trades
"stop_duration": to_minutes(minutes=30),
"only_per_pair": False, # Looks at all pairs
},
{
# Lock pairs with low profits
"method": "LowProfitPairs",
"lookback_period": to_minutes(hours=1, minutes=30),
"trade_limit": 2, # Considering all pairs that have a minimum of 2 trades
"stop_duration": to_minutes(hours=15),
"required_profit": 0.02, # If profit < 2% this will activate for a pair
},
{
# Lock pairs with low profits
"method": "LowProfitPairs",
"lookback_period": to_minutes(hours=6),
"trade_limit": 4, # Considering all pairs that have a minimum of 4 trades
"stop_duration": to_minutes(minutes=30),
"required_profit": 0.01, # If profit < 1% this will activate for a pair
},
]
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Adding EMA's into the dataframe
dataframe["s1_ema_xs"] = ta.EMA(dataframe, timeperiod=self.s1_ema_xs)
dataframe["s1_ema_sm"] = ta.EMA(dataframe, timeperiod=self.s1_ema_sm)
dataframe["s1_ema_md"] = ta.EMA(dataframe, timeperiod=self.s1_ema_md)
dataframe["s1_ema_xl"] = ta.EMA(dataframe, timeperiod=self.s1_ema_xl)
dataframe["s1_ema_xxl"] = ta.EMA(dataframe, timeperiod=self.s1_ema_xxl)
s2_ema_value = ta.EMA(dataframe, timeperiod=self.s2_ema_input)
s2_ema_xxl_value = ta.EMA(dataframe, timeperiod=200)
dataframe["s2_ema"] = s2_ema_value - s2_ema_value * self.s2_ema_offset_input
dataframe["s2_ema_xxl_off"] = s2_ema_xxl_value - s2_ema_xxl_value * self.s2_fib_lower_value
dataframe["s2_ema_xxl"] = ta.EMA(dataframe, timeperiod=200)
s2_bb_sma_value = ta.SMA(dataframe, timeperiod=self.s2_bb_sma_length)
s2_bb_std_dev_value = ta.STDDEV(dataframe, self.s2_bb_std_dev_length)
dataframe["s2_bb_std_dev_value"] = s2_bb_std_dev_value
dataframe["s2_bb_lower_band"] = s2_bb_sma_value - (s2_bb_std_dev_value * self.s2_bb_lower_offset)
s2_fib_atr_value = ta.ATR(dataframe, timeframe=self.s2_fib_atr_len)
s2_fib_sma_value = ta.SMA(dataframe, timeperiod=self.s2_fib_sma_len)
dataframe["s2_fib_lower_band"] = s2_fib_sma_value - s2_fib_atr_value * self.s2_fib_lower_value
s3_bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
dataframe["s3_bb_lowerband"] = s3_bollinger["lower"]
dataframe["s3_ema_long"] = ta.EMA(dataframe, timeperiod=self.s3_ema_long)
dataframe["s3_ema_short"] = ta.EMA(dataframe, timeperiod=self.s3_ema_short)
dataframe["s3_fast_ma"] = ta.EMA(dataframe["volume"] * dataframe["close"], self.s3_ma_fast) / ta.EMA(
dataframe["volume"], self.s3_ma_fast
)
dataframe["s3_slow_ma"] = ta.EMA(dataframe["volume"] * dataframe["close"], self.s3_ma_slow) / ta.EMA(
dataframe["volume"], self.s3_ma_slow
)
# Volume weighted MACD
dataframe["fastMA"] = ta.EMA(dataframe["volume"] * dataframe["close"], 12) / ta.EMA(dataframe["volume"], 12)
dataframe["slowMA"] = ta.EMA(dataframe["volume"] * dataframe["close"], 26) / ta.EMA(dataframe["volume"], 26)
dataframe["vwmacd"] = dataframe["fastMA"] - dataframe["slowMA"]
dataframe["signal"] = ta.EMA(dataframe["vwmacd"], 9)
dataframe["hist"] = dataframe["vwmacd"] - dataframe["signal"]
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# basic buy methods to keep the strategy simple
if self.buy_signal_1:
conditions = [
dataframe["vwmacd"] < dataframe["signal"],
dataframe["low"] < dataframe["s1_ema_xxl"],
dataframe["close"] > dataframe["s1_ema_xxl"],
qtpylib.crossed_above(dataframe["s1_ema_sm"], dataframe["s1_ema_md"]),
dataframe["s1_ema_xs"] < dataframe["s1_ema_xl"],
dataframe["volume"] > 0,
]
dataframe.loc[reduce(lambda x, y: x & y, conditions), ["buy", "buy_tag"]] = (1, "buy_signal_1")
if self.buy_signal_2:
conditions = [
qtpylib.crossed_above(dataframe["s2_fib_lower_band"], dataframe["s2_bb_lower_band"]),
dataframe["close"] < dataframe["s2_ema"],
dataframe["volume"] > 0,
]
dataframe.loc[reduce(lambda x, y: x & y, conditions), ["buy", "buy_tag"]] = (1, "buy_signal_2")
if self.buy_signal_3:
conditions = [
dataframe["low"] < dataframe["s3_bb_lowerband"],
dataframe["high"] > dataframe["s3_slow_ma"],
dataframe["high"] < dataframe["s3_ema_long"],
dataframe["volume"] > 0,
]
dataframe.loc[reduce(lambda x, y: x & y, conditions), ["buy", "buy_tag"]] = (1, "buy_signal_3")
if not all([self.buy_signal_1, self.buy_signal_2, self.buy_signal_3]):
dataframe.loc[(), "buy"] = 0
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# This is essentailly ignored as we're using strict ROI / Stoploss / TTP sale scenarios
dataframe.loc[(), "sell"] = 0
return dataframe
def custom_stoploss(
self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs
) -> float:
if current_profit > 0.2:
return 0.04
if current_profit > 0.1:
return 0.03
if current_profit > 0.06:
return 0.02
if current_profit > 0.03:
return 0.01
# Let's try to minimize the loss
if current_profit <= -0.10:
if trade.open_date_utc + timedelta(hours=60) < current_time:
# After 60H since buy
return current_profit / 1.75
if current_profit <= -0.08:
if trade.open_date_utc + timedelta(hours=120) < current_time:
# After 120H since buy
return current_profit / 1.70
return -1