GodStra: Indicator-Comparison Entries and Exits on a 12-Hour Chart
Article Strategy library · Author: @Mablue (Masoud Azizi)
Summary
GodStra is a long-only Freqtrade strategy that calculates a broad set of technical indicators and compares selected indicator values to create entry and exit signals. Its configured entry checks whether the Ichimoku base trend value is below a volatility Keltner-channel value threshold; its exit compares the KST difference with a volume-based MFI value. The class also supports several comparison types, including thresholds and crossovers, though the supplied parameter sets use numeric comparisons.
The document includes a hyperoptimization report with nine trades and a reported eight wins, one loss, and 196.50% total profit. That result is a very small sample and is presented without the test market, date range, fees, or benchmark, so it does not establish out-of-sample performance. The strategy uses a 12-hour timeframe, a long holding horizon in its ROI schedule, a substantial fixed stop, and trailing exits. Its code also recommends an age filter and low concurrent trade count, but does not explain or validate those choices.
Key ideas
- The strategy generates signals by comparing selected technical-indicator columns or testing them against numeric thresholds.
- It calculates a broad collection of technical features before evaluating entry and exit conditions.
- The supplied configuration enters long using an Ichimoku and volatility-channel comparison and exits using a KST and MFI comparison.
- The reported optimization result is based on only nine trades and lacks enough test details to establish robustness.
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Full text
# GodStra
# GodStra
## Source (GPL-3.0)
```python
# GodStra Strategy
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
# {
# "method": "AgeFilter",
# "min_days_listed": 30
# },
# IMPORTANT: INSTALL TA BEFORE RUN(pip install ta)
# IMPORTANT: Use Smallest "max_open_trades" for getting best results inside config.json
# --- Do not remove these libs ---
import logging
from functools import reduce
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
# Add your lib to import here
# import talib.abstract as ta
import pandas as pd
from freqtrade.strategy import IStrategy
from numpy.lib import math
from pandas import DataFrame
# import talib.abstract as ta
from ta import add_all_ta_features
from ta.utils import dropna
# --------------------------------
class GodStra(IStrategy):
# 5/66: 9 trades. 8/0/1 Wins/Draws/Losses. Avg profit 21.83%. Median profit 35.52%. Total profit 1060.11476586 USDT ( 196.50Σ%). Avg duration 3440.0 min. Objective: -7.06960
# +--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------+
# | Best | Epoch | Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Objective |
# |--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------|
# | * Best | 1/500 | 11 | 2 1 8 | 5.22% | 280.74230393 USDT (57.40%) | 2,421.8 m | -2.85206 |
# | * Best | 2/500 | 10 | 7 0 3 | 18.76% | 983.46414442 USDT (187.58%) | 360.0 m | -4.32665 |
# | * Best | 5/500 | 9 | 8 0 1 | 21.83% | 1,060.11476586 USDT (196.50%) | 3,440.0 m | -7.0696 |
INTERFACE_VERSION: int = 3
# Buy hyperspace params:
buy_params = {
'buy-cross-0': 'volatility_kcc',
'buy-indicator-0': 'trend_ichimoku_base',
'buy-int-0': 42,
'buy-oper-0': '<R',
'buy-real-0': 0.06295
}
# Sell hyperspace params:
sell_params = {
'sell-cross-0': 'volume_mfi',
'sell-indicator-0': 'trend_kst_diff',
'sell-int-0': 98,
'sell-oper-0': '=R',
'sell-real-0': 0.8779
}
# ROI table:
minimal_roi = {
"0": 0.3556,
"4818": 0.21275,
"6395": 0.09024,
"22372": 0
}
# Stoploss:
stoploss = -0.34549
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.22673
trailing_stop_positive_offset = 0.2684
trailing_only_offset_is_reached = True
# Buy hypers
timeframe = '12h'
print('Add {\n\t"method": "AgeFilter",\n\t"min_days_listed": 30\n},\n to your pairlists in config (Under StaticPairList)')
def dna_size(self, dct: dict):
def int_from_str(st: str):
str_int = ''.join([d for d in st if d.isdigit()])
if str_int:
return int(str_int)
return -1 # in case if the parameter somehow doesn't have index
return len({int_from_str(digit) for digit in dct.keys()})
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Add all ta features
dataframe = dropna(dataframe)
dataframe = add_all_ta_features(
dataframe, open="open", high="high", low="low", close="close", volume="volume",
fillna=True)
# dataframe.to_csv("df.csv", index=True)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = list()
# /5: Cuz We have 5 Group of variables inside buy_param
for i in range(self.dna_size(self.buy_params)):
OPR = self.buy_params[f'buy-oper-{i}']
IND = self.buy_params[f'buy-indicator-{i}']
CRS = self.buy_params[f'buy-cross-{i}']
INT = self.buy_params[f'buy-int-{i}']
REAL = self.buy_params[f'buy-real-{i}']
DFIND = dataframe[IND]
DFCRS = dataframe[CRS]
if OPR == ">":
conditions.append(DFIND > DFCRS)
elif OPR == "=":
conditions.append(np.isclose(DFIND, DFCRS))
elif OPR == "<":
conditions.append(DFIND < DFCRS)
elif OPR == "CA":
conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
elif OPR == "CB":
conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
elif OPR == ">I":
conditions.append(DFIND > INT)
elif OPR == "=I":
conditions.append(DFIND == INT)
elif OPR == "<I":
conditions.append(DFIND < INT)
elif OPR == ">R":
conditions.append(DFIND > REAL)
elif OPR == "=R":
conditions.append(np.isclose(DFIND, REAL))
elif OPR == "<R":
conditions.append(DFIND < REAL)
print(conditions)
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = list()
for i in range(self.dna_size(self.sell_params)):
OPR = self.sell_params[f'sell-oper-{i}']
IND = self.sell_params[f'sell-indicator-{i}']
CRS = self.sell_params[f'sell-cross-{i}']
INT = self.sell_params[f'sell-int-{i}']
REAL = self.sell_params[f'sell-real-{i}']
DFIND = dataframe[IND]
DFCRS = dataframe[CRS]
if OPR == ">":
conditions.append(DFIND > DFCRS)
elif OPR == "=":
conditions.append(np.isclose(DFIND, DFCRS))
elif OPR == "<":
conditions.append(DFIND < DFCRS)
elif OPR == "CA":
conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
elif OPR == "CB":
conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
elif OPR == ">I":
conditions.append(DFIND > INT)
elif OPR == "=I":
conditions.append(DFIND == INT)
elif OPR == "<I":
conditions.append(DFIND < INT)
elif OPR == ">R":
conditions.append(DFIND > REAL)
elif OPR == "=R":
conditions.append(np.isclose(DFIND, REAL))
elif OPR == "<R":
conditions.append(DFIND < REAL)
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'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.