to run this: freqtrade trade --strategy LitmusEntryRollClassificationStrategy --config user_data/strategies/config.LitmusEntryRollClassification.json --freqaimodel LitmusMultiTargetClassifier --verbose
Timeframe
N/A
Direction
Long & Short
Stoploss
-2.0%
Trailing Stop
Yes
ROI
0m: 100.0%, 1000m: -100.0%
Interface Version
N/A
Startup Candles
300
Indicators
11
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
from datetime import datetime
from feature_engine.creation import CyclicalFeatures
from freqtrade.strategy import (IStrategy, merge_informative_pair,
BooleanParameter, CategoricalParameter)
from functools import reduce
from freqtrade.litmus.label_helpers import nearby_extremes
from pandas import DataFrame
from technical import qtpylib
from typing import Optional
import logging
import pandas as pd
import talib.abstract as ta
import zigzag
logger = logging.getLogger(__name__)
class LitmusEntryRollClassificationStrategy(IStrategy):
"""
to run this:
freqtrade trade --strategy LitmusEntryRollClassificationStrategy
--config user_data/strategies/config.LitmusEntryRollClassification.json
--freqaimodel LitmusMultiTargetClassifier --verbose
"""
minimal_roi = {
"0": 1,
"1000": -1
}
plot_config = {
"main_plot": {},
"subplots": {
"do_predict": {
"do_predict": {"color": "brown"},
"DI_values": {"color": "grey"},
"DI_no_outlier_detected": {"color": "#1e5780"},
},
"Long": {
"minima_0": {"color": "PaleGreen"},
"long_entry_target": {"color": "ForestGreen"},
"maxima_1": {"color": "Salmon"},
"long_exit_target": {"color": "Crimson"},
},
"Short": {
"maxima_0": {"color": "PaleGreen"},
"short_entry_target": {"color": "ForestGreen"},
"minima_1": {"color": "Salmon"},
"short_exit_target": {"color": "Crimson"},
},
"Labels": {
"raw_peaks_1": {"color": "#ffffa3"},
"nearby_peaks_1": {"color": "#e0ce38"},
"raw_peaks_0": {"color": "#a47ebc"},
"nearby_peaks_0": {"color": "#700CBC"}
},
"Other": {
"total_time": {"color": "Pink"},
"num_trees_&target_0": {"color": "Orange"},
"num_trees_&target_1": {"color": "#65ceff"},
"maxima_0_mean": {"color": "grey"},
"maxima_1_mean": {"color": "grey"},
},
},
}
# Configs for hyperopt
std_entry = 2.5
std_exit = 1.5
# Hyperopt parameters
do_predict_enabled = BooleanParameter(
default=True, space="buy", optimize=True)
outlier_detected_enabled = BooleanParameter(
default=True, space="buy", optimize=True)
long_entry_std_mul = CategoricalParameter(
[0.75, 1, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0],
default=1.75, space="buy", optimize=True)
long_exit_std_mul = CategoricalParameter(
[0.75, 1, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0],
default=1.25, space="sell", optimize=True)
short_entry_std_mul = CategoricalParameter(
[0.75, 1, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0],
default=1.75, space="buy", optimize=True)
short_exit_std_mul = CategoricalParameter(
[0.75, 1, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0],
default=1.25, space="sell", optimize=True)
# Stop loss config
stoploss = -0.02
trailing_stop = True
"""trailing_stop_positive_offset = 0.02
trailing_stop_positive = 0.015
trailing_only_offset_is_reached = True"""
process_only_new_candles = True
use_exit_signal = True
startup_candle_count = 300
can_short = True
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 populate_any_indicators(
self, pair, df, tf, informative=None, 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.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
"""
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"%%-{pair}-rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%%-{pair}-mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%%-{pair}-adx-period_{t}"] = ta.ADX(informative, window=t)
informative[f"{pair}-sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"{pair}-ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{pair}-close_over_sma-period_{t}"] = (
informative["close"] / informative[f"{pair}-sma-period_{t}"]
)
informative[f"%-{pair}-mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{pair}-bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{pair}-bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{pair}-bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{pair}-bb_width-period_{t}"] = (
informative[f"{pair}-bb_upperband-period_{t}"]
- informative[f"{pair}-bb_lowerband-period_{t}"]
) / informative[f"{pair}-bb_middleband-period_{t}"]
informative[f"%-{pair}-close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{pair}-bb_lowerband-period_{t}"]
)
informative[f"%-{pair}-roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{pair}-relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
# Absolute Price Oscillator
informative[f"%-{pair}-apo-period_{t}"] = ta.APO(
informative["close"], fastperiod=int(t / 2), slowperiod=t, matype=0)
# PPO (Percentage Price Oscilator)
informative[f"%-{pair}-ppo-period_{t}"] = ta.PPO(
informative["close"], fastperiod=int(t / 2), slowperiod=t, matype=0)
# MACD (macd, macdsignal, macdhist)
_, _, macdhist = ta.MACD(
informative["close"], fastperiod=int(t / 2),
slowperiod=t, signalperiod=int(3 * t / 4))
informative[f"%-{pair}-macdhist-period_{t}"] = macdhist
# Average True Range
informative[f"%-{pair}-atr-period_{t}"] = ta.ATR(
informative["high"], informative["low"], informative["close"], timeperiod=t)
informative[f"%-{pair}-pct-change"] = informative["close"].pct_change()
informative[f"%-{pair}-raw_volume"] = informative["volume"]
informative[f"%-{pair}-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
df["hour_of_day"] = df["date"].dt.hour
cyclical_transform = CyclicalFeatures(
variables=["day_of_week", "hour_of_day"], max_values=None, drop_original=True
)
df = cyclical_transform.fit_transform(df)
# Zigzag min/max for pivot positions
for i, g in enumerate(self.freqai_info["labeling_parameters"]["zigzag_min_growth"]):
logger.info(f"Starting zigzag labeling method ({i})")
min_growth = self.freqai_info["labeling_parameters"]["zigzag_min_growth"][i]
peaks = zigzag.peak_valley_pivots(
df["close"].values, min_growth, -min_growth)
peaks[0] = 0 # Set first value of peaks = 0
peaks[-1] = 0 # Set last value of peaks = 0
name_map = {0: f"not_minmax_{i}", 1: f"maxima_{i}", -1: f"minima_{i}"}
# Shift target for benefit of hindsight predictions
target_offset = self.freqai_info["labeling_parameters"]["target_offset"][i]
df[f"raw_peaks_{i}"] = peaks
df[f"raw_peaks_{i}"] = df[f"raw_peaks_{i}"].shift(target_offset).fillna(value=0)
# Smear label to values nearby within threshold
nearby_threshold = self.freqai_info["labeling_parameters"]["nearby_threshold"][i]
df[f"nearby_peaks_{i}"] = nearby_extremes(
df[["close", f"raw_peaks_{i}"]],
threshold=nearby_threshold,
forward_pass=self.freqai_info["labeling_parameters"]["forward_pass"][i],
reverse_pass=self.freqai_info["labeling_parameters"]["reverse_pass"][i])
df[f"&target_{i}"] = df[f"nearby_peaks_{i}"].map(name_map)
df[f"real_peaks_{i}"] = peaks
"""# Cointegration features
df["%-compare-BTC-log-returns_3m"] = (
np.log(df[f"%-{coin}-raw_price_3m"]) - np.log(df["%-BTC-raw_price_3m"])
)
df["%%-compare-BTC-log-returns-zscore_3m"] = zscore(df["%-compare-BTC-log-returns_3m"])
"""
return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config["freqai"]
dataframe = self.freqai.start(dataframe, metadata, self)
# Long entry
dataframe["long_entry_target"] = (
dataframe["minima_0_mean"] +
dataframe["minima_0_std"] *
float(self.long_entry_std_mul.value)
)
# Long exit
dataframe["long_exit_target"] = (
dataframe["maxima_1_mean"] +
dataframe["maxima_1_std"] *
float(self.long_exit_std_mul.value)
)
# Short entry
dataframe["short_entry_target"] = (
dataframe["maxima_0_mean"] +
dataframe["maxima_0_std"] *
float(self.short_entry_std_mul.value)
)
# Short exit
dataframe["short_exit_target"] = (
dataframe["minima_1_mean"] +
dataframe["minima_1_std"] *
float(self.short_exit_std_mul.value)
)
# Dissimilarity Index (Rolling Calculation Method)
DI_window = self.freqai_info["feature_parameters"].get("DI_window", 100)
DI_std_mul = self.freqai_info["feature_parameters"].get("DI_std_mul", 1)
dataframe["DI_outliers"] = (
dataframe["DI_values"].rolling(DI_window).mean()
+ dataframe["DI_values"].rolling(DI_window).std() * DI_std_mul
)
dataframe["DI_no_outlier_detected"] = dataframe["DI_values"] < dataframe["DI_outliers"]
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
# Long Entry
conditions = []
if self.do_predict_enabled.value:
conditions.append(df["do_predict"] == 1)
if self.outlier_detected_enabled.value:
conditions.append(df["DI_no_outlier_detected"])
conditions.append(
qtpylib.crossed_above(
df["minima_0"],
df["minima_0_mean"] + df["minima_0_std"] * float(self.long_entry_std_mul.value)
)
)
if conditions:
df.loc[
reduce(lambda x, y: x & y, conditions), ["enter_long", "enter_tag"]
] = (1, "minima_entry")
# Short Entry
conditions = []
if self.do_predict_enabled.value:
conditions.append(df["do_predict"] == 1)
if self.outlier_detected_enabled.value:
conditions.append(df["DI_no_outlier_detected"])
conditions.append(
qtpylib.crossed_above(
df["minima_0"],
df["minima_0_mean"] + df["minima_0_std"] * float(self.short_entry_std_mul.value)
)
)
if conditions:
df.loc[
reduce(lambda x, y: x & y, conditions), ["enter_short", "enter_tag"]
] = (1, "maxima_entry")
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
# Long Exit
conditions = []
conditions.append(
qtpylib.crossed_above(
df["maxima_1"],
df["maxima_1_mean"] + df["maxima_1_std"] * float(self.long_exit_std_mul.value)
)
)
if conditions:
df.loc[
reduce(lambda x, y: x & y, conditions), ["exit_long", "exit_tag"]
] = (1, "maxima_exit")
# Short Exit
conditions = []
conditions.append(
qtpylib.crossed_above(
df["minima_1"],
df["minima_1_mean"] + df["minima_1_std"] * float(self.short_exit_std_mul.value)
)
)
if conditions:
df.loc[
reduce(lambda x, y: x & y, conditions), ["exit_short", "exit_tag"]
] = (1, "minima_exit")
return df
def get_ticker_indicator(self):
return int(self.config["timeframe"][:-1])
@property
def protections(self):
return [
{
"method": "StoplossGuard",
"lookback_period": 60,
"trade_limit": 1,
"stop_duration": 15,
"required_profit": 0.0,
"only_per_pair": True,
"only_per_side": True
},
{
"method": "LowProfitPairs",
"lookback_period": 60,
"trade_limit": 2,
"stop_duration": 60,
"required_profit": 0.005,
"only_per_pair": True,
"only_per_side": True
},
{
"method": "MaxDrawdown",
"lookback_period": 120,
"trade_limit": 10,
"stop_duration": 60,
"max_allowed_drawdown": 0.05
}
]
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str],
side: str, **kwargs) -> float:
"""
Customize leverage for each new trade. This method is only called in futures mode.
:param pair: Pair that's currently analyzed
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
:param proposed_leverage: A leverage proposed by the bot.
:param max_leverage: Max leverage allowed on this pair
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:return: A leverage amount, which is between 1.0 and max_leverage.
"""
fixed_leverage = self.freqai_info.get("fixed_leverage", 0)
if fixed_leverage > 0:
return fixed_leverage
else:
return 1.0
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
side: str, **kwargs) -> bool:
"""open_trades = Trade.get_trades(trade_filter=Trade.is_open.is_(True))
# Balance longs vs shorts to help protect against black swan event
max_open_trades = self.config.get("max_open_trades", 0)
if max_open_trades > 0:
num_shorts, num_longs = 0, 0
for trade in open_trades:
if trade.enter_tag == "short":
num_shorts += 1
elif trade.enter_tag == "long":
num_longs += 1
if side == "long" and num_longs >= max_open_trades / 2.0:
return False
if side == "short" and num_shorts >= max_open_trades / 2.0:
return False"""
# Prevent taking trades that have already moved too far in predicted direction
"""df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = df.iloc[-1].squeeze()
if side == "long":
if rate > (last_candle["close"] * (1 + 0.0025)):
logger.info(f"Trade entry blocked (long) for {pair}")
return False
else:
if rate < (last_candle["close"] * (1 - 0.0025)):
logger.info(f"Trade entry blocked (short) for {pair}")
return False"""
return True
"""use_custom_stoploss = False
def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = df.iloc[-1].squeeze()
# Long Exit
if last_candle["maxima_1"] > last_candle["long_exit_target"]:
# Tighten stop loss under latest close
return 0.02
# Short Exit
if last_candle["minima_1"] > last_candle["short_exit_target"]:
return 0.02
# Otherwise keep current stoploss
return -1"""
"""def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
leverage: float, entry_tag: str, side: str,
**kwargs) -> float:
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
bid = self.wallets.get_available_stake_amount() * current_candle["missed_long_entry"]
return bid
"""