Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: self.model = CustomModel(self.config) self.model.bridge.start(dataframe, metadata)
Timeframe
N/A
Direction
Long Only
Stoploss
-5.0%
Trailing Stop
No
ROI
0m: 10.0%, 240m: -100.0%
Interface Version
N/A
Startup Candles
N/A
Indicators
9
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
import logging
from functools import reduce
import math
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.freqai.strategy_bridge import CustomModel
from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair
from freqtrade.strategy.interface import IStrategy
logger = logging.getLogger(__name__)
class Cenderawasih_freqai(IStrategy):
"""
Example strategy showing how the user connects their own
IFreqaiModel to the strategy. Namely, the user uses:
self.model = CustomModel(self.config)
self.model.bridge.start(dataframe, metadata)
to make predictions on their data. populate_any_indicators() automatically
generates the variety of features indicated by the user in the
canonical freqtrade configuration file under config['freqai'].
"""
minimal_roi = {"0": 0.1, "240": -1}
plot_config = {
"main_plot": {},
"subplots": {
"prediction": {"prediction": {"color": "blue"}},
"target_roi": {
"target_roi": {"color": "brown"},
},
"do_predict": {
"do_predict": {"color": "brown"},
},
},
}
process_only_new_candles = True
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
can_short = False
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
def bot_start(self):
self.model = CustomModel(self.config)
def populate_any_indicators(self, metadata, pair, df, tf, informative=None,
coin="", set_generalized_indicators=False):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
"""
with self.model.bridge.lock:
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
# informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
# informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
# informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
# informative[f"{coin}20sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"{coin}hma-period_{t}"] = tv_hma(informative, timeperiod=t)
# informative[f"%-{coin}close_over_20sma-period_{t}"] = (
# informative["close"] / informative[f"{coin}20sma-period_{t}"]
# )
informative[f"%-{coin}close_below_ema-period_{t}"] = (
informative["close"] / informative[f"{coin}ema-period_{t}"]
)
informative[f"%-{coin}close_below_hma-period_{t}"] = (
informative["close"] / informative[f"{coin}hma-period_{t}"]
)
# informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
# bollinger = qtpylib.bollinger_bands(
# qtpylib.typical_price(informative), window=t, stds=2.2
# )
# informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
# informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
# informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
# informative[f"%-{coin}bb_width-period_{t}"] = (
# informative[f"{coin}bb_upperband-period_{t}"]
# - informative[f"{coin}bb_lowerband-period_{t}"]
# ) / informative[f"{coin}bb_middleband-period_{t}"]
# informative[f"%-{coin}close-bb_lower-period_{t}"] = (
# informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
# )
# informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
# informative[f"%-{coin}relative_volume-period_{t}"] = (
# informative["volume"] / informative["volume"].rolling(t).mean()
# )
informative[f"%-{coin}rsi-period_14"] = ta.RSI(informative, timeperiod=14)
informative[f"%-{coin}rsi-period_4"] = ta.RSI(informative, timeperiod=4)
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
informative[f"%-{coin}raw_price"] = informative["close"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config["freqai"]
self.pair = metadata["pair"]
sgi = True
# the following loops are necessary for building the features
# indicated by the user in the configuration file.
# All indicators must be populated by populate_any_indicators() for live functionality
# to work correctly.
for tf in self.freqai_info["feature_parameters"]["include_timeframes"]:
dataframe = self.populate_any_indicators(
metadata,
self.pair,
dataframe.copy(),
tf,
coin=self.pair.split("/")[0] + "-",
set_generalized_indicators=sgi,
)
sgi = False
for pair in self.freqai_info["feature_parameters"]["include_corr_pairlist"]:
if metadata["pair"] in pair:
continue # do not include whitelisted pair twice if it is in corr_pairlist
dataframe = self.populate_any_indicators(
metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-"
)
# the model will return 4 values, its prediction, an indication of whether or not the
# prediction should be accepted, the target mean/std values from the labels used during
# each training period.
dataframe = self.model.bridge.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
if enter_long_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
] = (1, "long")
# enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
# if enter_short_conditions:
# df.loc[
# reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
# ] = (1, "short")
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
if exit_long_conditions:
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
# exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
# if exit_short_conditions:
# df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
return df
def get_ticker_indicator(self):
return int(self.config["timeframe"][:-1])
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, exit_reason: str, current_time, **kwargs,) -> bool:
entry_tag = trade.enter_tag
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode:
pair_dict = self.model.bridge.dd.pair_dict
else:
pair_dict = self.model.bridge.dd.follower_dict
with self.model.bridge.lock:
pair_dict[pair]["prediction" + entry_tag] = 0
if not follow_mode:
self.model.bridge.dd.save_drawer_to_disk()
else:
self.model.bridge.dd.save_follower_dict_to_disk()
return True
def tv_wma(df, length = 9) -> DataFrame:
"""
Source: Tradingview "Moving Average Weighted"
Pinescript Author: Unknown
Args :
dataframe : Pandas Dataframe
length : WMA length
field : Field to use for the calculation
Returns :
dataframe : Pandas DataFrame with new columns 'tv_wma'
"""
norm = 0
sum = 0
for i in range(1, length - 1):
weight = (length - i) * length
norm = norm + weight
sum = sum + df.shift(i) * weight
tv_wma = (sum / norm) if norm > 0 else 0
return tv_wma
def tv_hma(dataframe, length = 9, field = 'close') -> DataFrame:
"""
Source: Tradingview "Hull Moving Average"
Pinescript Author: Unknown
Args :
dataframe : Pandas Dataframe
length : HMA length
field : Field to use for the calculation
Returns :
dataframe : Pandas DataFrame with new columns 'tv_hma'
"""
h = 2 * tv_wma(dataframe[field], math.floor(length / 2)) - tv_wma(dataframe[field], length)
tv_hma = tv_wma(h, math.floor(math.sqrt(length)))
# dataframe.drop("h", inplace=True, axis=1)
return tv_hma