This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/
Timeframe
N/A
Direction
Long Only
Stoploss
-2.0%
Trailing Stop
No
ROI
0m: 1000.0%
Interface Version
3
Startup Candles
N/A
Indicators
5
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame # noqa
from datetime import datetime, timedelta # noqa
from typing import Optional, Union # noqa
from freqtrade.persistence import Order, Trade
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# from user_data.strategies.utils import crossed_above_each_hour, crossed_below_each_hour
class MagicalStrategy3(IStrategy):
"""
This is a strategy template to get you started.
More information in https://www.freqtrade.io/en/latest/strategy-customization/
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the methods: populate_indicators, populate_entry_trend, populate_exit_trend
You should keep:
- timeframe, minimal_roi, stoploss, trailing_*
"""
########## Configurable params ########## # TODO: move these into config.json
# Target volatility
target_volatility = 0.05
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.02
# timeframe for the strategy.
timeframe_num = 1
timeframe_unit = "h"
timeframe = f"{timeframe_num}{timeframe_unit}"
if timeframe_unit == "h":
n_candles_per_day = 24 // timeframe_num
elif timeframe_unit == "m":
n_candles_per_day = 24 * 60 // timeframe_num
else:
n_candles_per_day = 0
# How long (in minutes or seconds) the bot will wait for an unfilled order
unfilledtimeout = {
"entry": 60,
"exit": 60,
"exit_timeout_count": 0,
"unit": "minutes"
}
#########################################
###### Do not change values below ######
########################################
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 3
# Can this strategy go short?
can_short: bool = False
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {"0": 10.0}
# Trailing stoploss
trailing_stop = False
# trailing_only_offset_is_reached = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# To allow partial sell
position_adjustment_enable = True
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the config.
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 100
# Strategy parameters
# buy_rsi = IntParameter(10, 40, default=30, space="buy")
# sell_rsi = IntParameter(60, 90, default=70, space="sell")
# Optional order type mapping.
order_types = {
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'entry': 'gtc',
'exit': 'gtc'
}
########################################
@property
def plot_config(self):
return {
# Main plot indicators (Moving averages, ...)
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
},
'subplots': {
# Subplots - each dict defines one additional plot
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
}
}
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
for _tp in [3, 5, 10, 20]:
dataframe[f"daily_sma{_tp}"] = None
for _h in range(0, 24, self.timeframe_num):
each_hour_df = dataframe.loc[dataframe["date"].dt.hour==_h]
dataframe.loc[dataframe["date"].dt.hour==_h, f"daily_sma{_tp}"] = ta.SMA(each_hour_df[f"close"], timeperiod=_tp)
dataframe["high_ytd"] = dataframe["high"].rolling(self.n_candles_per_day).max()
dataframe["low_ytd"] = dataframe["low"].rolling(self.n_candles_per_day).min()
dataframe["volatility_ytd"] = (dataframe["high_ytd"] - dataframe["low_ytd"]) / dataframe["close"]
dataframe["volatility"] = None # SMA(5) of volatility_ytd
for _h in range(0, 24, self.timeframe_num):
each_hour_df = dataframe.loc[dataframe["date"].dt.hour==_h]
dataframe.loc[dataframe["date"].dt.hour==_h, "volatility"] = ta.SMA(each_hour_df["volatility_ytd"], timeperiod=5)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the entry signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with entry columns populated
"""
# dataframe.loc[
# (
# (dataframe["close"] > dataframe["daily_sma3"])
# &
# (dataframe["close"] > dataframe["daily_sma5"])
# &
# (dataframe["close"] > dataframe["daily_sma10"])
# &
# (dataframe["close"] > dataframe["daily_sma20"])
# )
# &
# (
# (crossed_above_each_hour(dataframe["close"], dataframe["daily_sma3"]))
# |
# (crossed_above_each_hour(dataframe["close"], dataframe["daily_sma5"]))
# |
# (crossed_above_each_hour(dataframe["close"], dataframe["daily_sma10"]))
# |
# (crossed_above_each_hour(dataframe["close"], dataframe["daily_sma20"]))
# )
# &
# (dataframe["volume"] > 0),
# "enter_long"
# ] = 1
for _h in range(0, 24, self.timeframe_num):
each_hour_df = dataframe.loc[dataframe["date"].dt.hour==_h]
enter_series = (
(
(each_hour_df["close"] > each_hour_df["daily_sma3"])
&
(each_hour_df["close"] > each_hour_df["daily_sma5"])
&
(each_hour_df["close"] > each_hour_df["daily_sma10"])
&
(each_hour_df["close"] > each_hour_df["daily_sma20"])
)
&
(
(qtpylib.crossed_above(each_hour_df["close"], each_hour_df["daily_sma3"]))
|
(qtpylib.crossed_above(each_hour_df["close"], each_hour_df["daily_sma5"]))
|
(qtpylib.crossed_above(each_hour_df["close"], each_hour_df["daily_sma10"]))
|
(qtpylib.crossed_above(each_hour_df["close"], each_hour_df["daily_sma20"]))
)
&
(each_hour_df["volume"] > 0)
)
enter_index = enter_series[enter_series].index
dataframe.loc[enter_index, "enter_long"] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the exit signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with exit columns populated
"""
# Do nothing as we use custom_exit()!!
return dataframe
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
timeframe_delta = timedelta(
hours=(self.timeframe_num if self.timeframe_unit == "h" else 0),
minutes=(self.timeframe_num if self.timeframe_unit == "m" else 0),
)
order_time = trade.open_date - timeframe_delta
if current_time.hour == order_time.hour: # trade.open_date.hour: # order_time.hour:
each_hour_df = dataframe.loc[dataframe["date"].dt.hour==(order_time - timeframe_delta).hour]
exit_signal = (
(
# (qtpylib.crossed_below(each_hour_df["close"], each_hour_df["daily_sma3"]))
# |
(qtpylib.crossed_below(each_hour_df["close"], each_hour_df["daily_sma5"]))
|
(qtpylib.crossed_below(each_hour_df["close"], each_hour_df["daily_sma10"]))
|
(qtpylib.crossed_below(each_hour_df["close"], each_hour_df["daily_sma20"]))
)
&
(each_hour_df["volume"] > 0)
).iloc[-1]
if exit_signal:
return "custom_exit"
return None
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float:
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
proposed_stake = (
(self.target_volatility / current_candle["volatility"])
/ len(self.config["exchange"]["pair_whitelist"])
/ 24
* max_stake
)
return proposed_stake