WaveTrend Cross Strategy Filtered by Stochastic K
Code Freqtrade
Summary
This Freqtrade strategy combines WaveTrend line crosses with Stochastic K and a difference filter. It enters long when the first WaveTrend line crosses above its signal line and all three indicator values fall within tunable ranges. It exits on the reverse cross when the values meet corresponding sell ranges. The strategy uses 30-minute candles, configurable ROI targets, and a fixed stop loss; its buy and sell thresholds are exposed for hyperparameter optimization.
The comments report a 61-trade backtest with 16 wins, no draws, and 45 losses, alongside average and median profit figures that differ sharply. Those figures are presented without market, date range, or validation details, so they do not establish robustness. The indicator routine applies a min-max scaler to columns across the supplied dataframe, which can make signals dependent on the data window and may introduce look-ahead effects if future rows are included. An exception handler substitutes constant indicator values, potentially obscuring calculation failures.
Key ideas
- Long entries require an upward WaveTrend cross plus bounded WaveTrend, Stochastic K, and indicator-difference values.
- Long exits use a downward WaveTrend cross and separate bounded indicator ranges.
- The strategy exposes entry and exit thresholds for hyperparameter search and uses 30-minute candles.
- The reported sample has many losing trades, and the backtest lacks date-range and validation context.
- Scaling indicator columns across a dataframe may create window dependence or look-ahead risk.
Tags
Full text
# wtc.py
```py
# WTC Strategy: WTC(World Trade Center Tabriz)
# is the biggest skyscraper of Tabriz, city of Iran
# (What you want?it not enough for you?that's just it!)
# No, no, I'm kidding. It's also mean Wave Trend with Crosses
# algo by LazyBare(in TradingView) that I reduce it
# signals noise with dividing it to Stoch-RSI indicator.
# Also thanks from discord: @aurax for his/him
# request to making this strategy.
# hope you enjoy and get profit
# Author: @Mablue (Masoud Azizi)
# IMPORTANT: install sklearn befoure you run this strategy:
# pip install sklearn
# github: https://github.com/mablue/
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell --strategy wtc
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
from freqtrade.strategy import DecimalParameter
from freqtrade.strategy import IStrategy
from pandas import DataFrame
#
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from sklearn import preprocessing
# --------------------------------
# Add your lib to import here
class wtc(IStrategy):
################################ SETTINGS ################################
# 61 trades. 16/0/45 Wins/Draws/Losses.
# * Avg profit: 132.53%.
# Median profit: -12.97%.
# Total profit: 0.80921449 BTC ( 809.21Σ%).
# Avg duration 4 days, 7:47:00 min.
# Objective: -15.73417
# Config:
# "max_open_trades": 10,
# "stake_currency": "BTC",
# "stake_amount": 0.01,
# "tradable_balance_ratio": 0.99,
# "timeframe": "30m",
# "dry_run_wallet": 0.1,
INTERFACE_VERSION: int = 3
# Buy hyperspace params:
buy_params = {
"buy_max": 0.9609,
"buy_max0": 0.8633,
"buy_max1": 0.9133,
"buy_min": 0.0019,
"buy_min0": 0.0102,
"buy_min1": 0.6864,
}
# Sell hyperspace params:
sell_params = {
"sell_max": -0.7979,
"sell_max0": 0.82,
"sell_max1": 0.9821,
"sell_min": -0.5377,
"sell_min0": 0.0628,
"sell_min1": 0.4461,
}
minimal_roi = {
"0": 0.30873,
"569": 0.16689,
"3211": 0.06473,
"7617": 0
}
stoploss = -0.128
############################## END SETTINGS ##############################
timeframe = '30m'
buy_max = DecimalParameter(-1, 1, decimals=4, default=0.4393, space='buy')
buy_min = DecimalParameter(-1, 1, decimals=4, default=-0.4676, space='buy')
sell_max = DecimalParameter(-1, 1, decimals=4,
default=-0.9512, space='sell')
sell_min = DecimalParameter(-1, 1, decimals=4,
default=0.6519, space='sell')
buy_max0 = DecimalParameter(0, 1, decimals=4, default=0.4393, space='buy')
buy_min0 = DecimalParameter(0, 1, decimals=4, default=-0.4676, space='buy')
sell_max0 = DecimalParameter(
0, 1, decimals=4, default=-0.9512, space='sell')
sell_min0 = DecimalParameter(
0, 1, decimals=4, default=0.6519, space='sell')
buy_max1 = DecimalParameter(0, 1, decimals=4, default=0.4393, space='buy')
buy_min1 = DecimalParameter(0, 1, decimals=4, default=-0.4676, space='buy')
sell_max1 = DecimalParameter(
0, 1, decimals=4, default=-0.9512, space='sell')
sell_min1 = DecimalParameter(
0, 1, decimals=4, default=0.6519, space='sell')
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# WAVETREND
try:
ap = (dataframe['high']+dataframe['low'] + dataframe['close'])/3
esa = ta.EMA(ap, 10)
d = ta.EMA((ap - esa).abs(), 10)
ci = (ap - esa).div(0.0015 * d)
tci = ta.EMA(ci, 21)
wt1 = tci
wt2 = ta.SMA(np.nan_to_num(wt1), 4)
dataframe['wt1'], dataframe['wt2'] = wt1, wt2
stoch = ta.STOCH(dataframe, 14)
slowk = stoch['slowk']
dataframe['slowk'] = slowk
# print(dataframe.iloc[:, 6:].keys())
x = dataframe.iloc[:, 6:].values # returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
dataframe.iloc[:, 6:] = pd.DataFrame(x_scaled)
# print('wt:\t', dataframe['wt'].min(), dataframe['wt'].max())
# print('stoch:\t', dataframe['stoch'].min(), dataframe['stoch'].max())
dataframe['def'] = dataframe['slowk']-dataframe['wt1']
# print('def:\t', dataframe['def'].min(), "\t", dataframe['def'].max())
except:
dataframe['wt1'], dataframe['wt2'], dataframe['def'], dataframe['slowk'] = 0, 10, 100, 1000
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['wt1'], dataframe['wt2']))
& (dataframe['wt1'].between(self.buy_min0.value, self.buy_max0.value))
& (dataframe['slowk'].between(self.buy_min1.value, self.buy_max1.value))
& (dataframe['def'].between(self.buy_min.value, self.buy_max.value))
),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# print(dataframe['slowk']/dataframe['wt1'])
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['wt1'], dataframe['wt2']))
& (dataframe['wt1'].between(self.sell_min0.value, self.sell_max0.value))
& (dataframe['slowk'].between(self.sell_min1.value, self.sell_max1.value))
& (dataframe['def'].between(self.sell_min.value, self.sell_max.value))
),
'exit_long'] = 1
return dataframe
```Shown in full with attribution under the source's licence. Licence: GPL-3.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.