Ichimoku
♡
Basics
mode: spot
timeframe: 5m
interface version: 3
Settings
stoploss: -0.1
has minimal roi
trailing
Indicators
Ichimoku
talib
technical
Concepts
trailing
trend_following
15 related strategies (⧉ identical code, ≈ similar name)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame from technical.indicators import ichimoku import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class Ichimoku(IStrategy): INTERFACE_VERSION = 3 '\n Ichimoku Strategy\n ' minimal_roi = {'0': 1} stoploss = -0.1 # Optimal timeframe for the strategy timeframe = '5m' # trailing stoploss trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.02 trailing_only_offset_is_reached = True # run "populate_indicators" only for new candle ta_on_candle = False # Experimental settings (configuration will overide these if set) use_exit_signal = True exit_profit_only = True ignore_roi_if_entry_signal = False # Optional order type mapping order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False} def informative_pairs(self): """ """ return [(f"{self.config['stake_currency']}/USDT", self.timeframe)] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ """ ichi = ichimoku(dataframe) dataframe['tenkan'] = ichi['tenkan_sen'] dataframe['kijun'] = ichi['kijun_sen'] dataframe['senkou_a'] = ichi['senkou_span_a'] dataframe['senkou_b'] = ichi['senkou_span_b'] dataframe['cloud_green'] = ichi['cloud_green'] dataframe['cloud_red'] = ichi['cloud_red'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ """ dataframe.loc[(dataframe['tenkan'].shift(1) < dataframe['kijun'].shift(1)) & (dataframe['tenkan'] > dataframe['kijun']) & (dataframe['cloud_red'] == True), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ """ dataframe.loc[(), 'exit_long'] = 1 return dataframe |
Strategy League — fixed backtest that feeds the ranking
⏳ queued — the fixed-params League backtest is running. Updates automatically.
Backtests — over a market period
Backtest this strategy over a chosen crypto-cycle period. These don't affect the League ranking, and need that period's candle data downloaded.
Log in or sign up to run backtests.
| Period | Range | Total % | Win % | Max DD | Trades | |
|---|---|---|---|---|---|---|
| 2020 · DeFi Summer & Pre-Halving Rally | 20200101-20210101 | not run | ||||
| 2021 · Institutional Bull Market | 20210101-20220101 | not run | ||||
| 2022 · Post-Bull Crash & Macro Tightening | 20220101-20230101 | not run | ||||
| 2023–2024 · Recovery & ETF Anticipation | 20230101-20250101 | not run | ||||
| 2025–2026 · Current Cycle | 20250101-20260101 | not run | ||||
Walk forward
Out-of-sample backtest on recent data · 33 pairs · 20260101-20260701.
Backtest trust check
no lookahead patterns · 1 thing(s) worth reviewing before trusting the numbers
| Line | Pattern | Detail | |
|---|---|---|---|
| 30 | review | unused_informative | informative_pairs() declares an extra timeframe, but nothing merges it into the dataframe (no merge_informative_pair, no @informative) -- that data is fetched and discarded, and any higher-timeframe filter you think is running isn't |
ran by Ron · took s
Lookahead analysis
freqtrade lookahead-analysis: detects strategies peeking at future candles.