A collection of Freqtrade & FreqAI snippets, all in one place. Mostly collected from the Freqtrade Discord and FreqAI Discord - now all into one place. While I’ve been trying to credit where due, things might be missing. If you believe anything here is your work and credits should be given, let me know.
Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or webUI. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
Freqtrade Discord | FreqAI Discord | Documentation | Github
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Custom Trailing Stoploss
Credit: @github/perkmeister
# Hyperopt Parameters
# hard stoploss profit
pHSL = DecimalParameter(-0.200, -0.040, default=-0.10, decimals=3, space='sell', optimize=True, load=True)
# profit threshold 1, trigger point, SL_1 is used
pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', optimize=True, load=True)
pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', optimize=True, load=True)
# profit threshold 2, SL_2 is used
pPF_2 = DecimalParameter(0.040, 0.100, default=0.070, decimals=3, space='sell', optimize=True, load=True)
pSL_2 = DecimalParameter(0.020, 0.070, default=0.030, decimals=3, space='sell', optimize=True, load=True)
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# hard stoploss profit
HSL = self.pHSL.value
PF_1 = self.pPF_1.value
SL_1 = self.pSL_1.value
PF_2 = self.pPF_2.value
SL_2 = self.pSL_2.value
# For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
# between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
# rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
if (current_profit > PF_2):
sl_profit = SL_2 + (current_profit - PF_2)
elif (current_profit > PF_1):
sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
else:
sl_profit = HSL
# Only for hyperopt invalid return
if (sl_profit >= current_profit):
return -0.99
return stoploss_from_open(sl_profit, current_profit)Detect Pullback
Credit: @github/just-nilux
def detect_pullback(df: DataFrame, periods=30, method='pct_outlier'):
"""
Pullback & Outlier Detection
Know when a sudden move and possible reversal is coming
Method 1: StDev Outlier (z-score)
Method 2: Percent-Change Outlier (z-score)
Method 3: Candle Open-Close %-Change
outlier_threshold - Recommended: 2.0 - 3.0
df['pullback_flag']: 1 (Outlier Up) / -1 (Outlier Down)
"""
if method == 'stdev_outlier':
outlier_threshold = 2.0
df['dif'] = df['close'] - df['close'].shift(1)
df['dif_squared_sum'] = (df['dif']**2).rolling(window=periods + 1).sum()
df['std'] = np.sqrt((df['dif_squared_sum'] - df['dif'].shift(0)**2) / (periods - 1))
df['z'] = df['dif'] / df['std']
df['pullback_flag'] = np.where(df['z'] >= outlier_threshold, 1, 0)
df['pullback_flag'] = np.where(df['z'] <= -outlier_threshold, -1, df['pullback_flag'])
if method == 'pct_outlier':
outlier_threshold = 2.0
df["pb_pct_change"] = df["close"].pct_change()
df['pb_zscore'] = qtpylib.zscore(df, window=periods, col='pb_pct_change')
df['pullback_flag'] = np.where(df['pb_zscore'] >= outlier_threshold, 1, 0)
df['pullback_flag'] = np.where(df['pb_zscore'] <= -outlier_threshold, -1, df['pullback_flag'])
if method == 'candle_body':
pullback_pct = 1.0
df['change'] = df['close'] - df['open']
df['pullback'] = (df['change'] / df['open']) * 100
df['pullback_flag'] = np.where(df['pullback'] >= pullback_pct, 1, 0)
df['pullback_flag'] = np.where(df['pullback'] <= -pullback_pct, -1, df['pullback_flag'])
return dfSMI Trend
Credit: @github/just-nilux
def smi_trend(df: DataFrame, k_length=9, d_length=3, smoothing_type='EMA', smoothing=10):
"""
Stochastic Momentum Index (SMI) Trend Indicator
SMI > 0 and SMI > MA: (2) Bull
SMI < 0 and SMI > MA: (1) Possible Bullish Reversal
SMI > 0 and SMI < MA: (-1) Possible Bearish Reversal
SMI < 0 and SMI < MA: (-2) Bear
Returns:
pandas.Series: New feature generated
"""
ll = df['low'].rolling(window=k_length).min()
hh = df['high'].rolling(window=k_length).max()
diff = hh - ll
rdiff = df['close'] - (hh + ll) / 2
avgrel = rdiff.ewm(span=d_length).mean().ewm(span=d_length).mean()
avgdiff = diff.ewm(span=d_length).mean().ewm(span=d_length).mean()
smi = np.where(avgdiff != 0, (avgrel / (avgdiff / 2) * 100), 0)
if smoothing_type == 'SMA':
smi_ma = ta.SMA(smi, timeperiod=smoothing)
elif smoothing_type == 'EMA':
smi_ma = ta.EMA(smi, timeperiod=smoothing)
elif smoothing_type == 'WMA':
smi_ma = ta.WMA(smi, timeperiod=smoothing)
elif smoothing_type == 'DEMA':
smi_ma = ta.DEMA(smi, timeperiod=smoothing)
elif smoothing_type == 'TEMA':
smi_ma = ta.TEMA(smi, timeperiod=smoothing)
else:
raise ValueError("Choose an MA Type: 'SMA', 'EMA', 'WMA', 'DEMA', 'TEMA'")
conditions = [
(np.greater(smi, 0) & np.greater(smi, smi_ma)), # (2) Bull
(np.less(smi, 0) & np.greater(smi, smi_ma)), # (1) Possible Bullish Reversal
(np.greater(smi, 0) & np.less(smi, smi_ma)), # (-1) Possible Bearish Reversal
(np.less(smi, 0) & np.less(smi, smi_ma)) # (-2) Bear
]
smi_trend = np.select(conditions, [2, 1, -1, -2])
return smi, smi_ma, smi_trendSMI Kernel Regression
Credit: hpis / @github/just-nilux
dataframe['smi'], dataframe['k_smi'], dataframe['k_smi_down'], dataframe['k_smi_up'] = calculate_smi_kernel(dataframe, x_0=5)
def calculate_smi_kernel(df: DataFrame,
_K: int = 10,
h: float = 8.0,
rw: float = 8.0,
x_0: int = 5, # Anything higher than 5 doesn't lead to better results imo
osint: int = 40,
obint: int = 40,
_col: str = 'close'):
def kernel_regression(_src):
weights = [(1 + (i**2 / (h**2 * 2 * rw)))**(-rw) for i in range(len(_src))]
weighted_sum = sum([val*weight for val, weight in zip(_src[x_0:], weights)])
return weighted_sum / sum(weights)
# Estimations
df['highestHigh'] = df[_col].rolling(window=_K).max()
df['lowestLow'] = df[_col].rolling(window=_K).min()
df['highestLowestRange'] = df['highestHigh'] - df['lowestLow']
df['relativeRange'] = df[_col] - (df['highestHigh'] + df['lowestLow']) / 2
df['smi'] = 200 * (df['relativeRange'].rolling(window=_K).apply(kernel_regression, raw=True) /
df['highestLowestRange'].rolling(window=_K).apply(kernel_regression, raw=True))
df['k_smi'] = df['smi'].rolling(window=_K).apply(kernel_regression, raw=True)
df['k_smi_down'] = (df['smi'] < obint) & (df['smi'].shift(1) >= obint)
df['k_smi_up'] = (df['smi'] > -osint) & (df['smi'].shift(1) <= -osint)
return df['smi'], df['k_smi'], df['k_smi_down'], df['k_smi_up']Example Producer-Consumer Strategy & Config
freqtrade_consumer_producer_strategies
Credit: @github/matthieu-hm
Consumer Strategy with QuestDB Dataframe Storage
freqtrade_questdb
Credit: @github/just-nilux
Freqtrade Analysis Notebook
Freqtrade Analysis Notebook
Credit: @github/froggleston
Dynamic Pairlist for FreqAI
Dynamic Pairlist for FreqAI
Credit: @github/mrzdev