Candlestick Pattern Entries

Code Freqtrade

Summary

This Freqtrade strategy computes TA-Lib candlestick pattern indicators and enters a long position when a selected pattern returns a chosen signal value. Its example parameters select the high-wave pattern and a negative signal, while the daily timeframe makes the setup intended for slower trading. The exit signal is unimplemented, so positions rely on the configured return targets, stop loss, and trailing stop.

The document reports one hyperparameter search result with trade counts, win and loss counts, profit figures, and average duration. Those figures are presented without a market, date range, asset universe, benchmark, or validation method, so they do not establish that the strategy will generalize. The signal values and pattern selection are configurable, but the excerpt offers no rules for choosing among patterns or preventing overfitting.

Key ideas

The strategy calculates all TA-Lib pattern-recognition indicators for each candle.
A configurable candlestick pattern and signal value trigger long entries.
The example uses daily candles and specifies profit targets, a stop loss, and a trailing stop.
No explicit exit signal is defined in the strategy code.
The reported optimization result lacks context needed to assess out-of-sample performance.

Tags

Technical indicators Backtesting Risk management Equities

Full text

# PatternRecognition.py


```py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401

# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IStrategy, IntParameter)

# --------------------------------
# Add your lib to import here
import talib
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.util import resample_to_interval, resampled_merge


class PatternRecognition(IStrategy):
    # Pattern Recognition Strategy
    # By: @Mablue
    # freqtrade hyperopt -s PatternRecognition --hyperopt-loss SharpeHyperOptLossDaily -e 1000
    #

    # 173/1000:    510 trades. 408/14/88 Wins/Draws/Losses. Avg profit   2.35%. Median profit   5.60%. Total profit 5421.34509618 USDT ( 542.13%). Avg duration 7 days, 11:54:00 min. Objective: -1.60426


    INTERFACE_VERSION: int = 3
    # Buy hyperspace params:
    buy_params = {
        "buy_pr1": "CDLHIGHWAVE",
        "buy_vol1": -100,
    }

    # ROI table:
    minimal_roi = {
        "0": 0.936,
        "5271": 0.332,
        "18147": 0.086,
        "48152": 0
    }

    # Stoploss:
    stoploss = -0.288

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.032
    trailing_stop_positive_offset = 0.084
    trailing_only_offset_is_reached = True

    # Optimal timeframe for the strategy.
    timeframe = '1d'
    prs = talib.get_function_groups()['Pattern Recognition']

    # # Strategy parameters
    buy_pr1 = CategoricalParameter(prs, default=prs[0], space="buy")
    buy_vol1 = CategoricalParameter([-100,100], default=0, space="buy")



    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        for pr in self.prs:
            dataframe[pr] = getattr(ta, pr)(dataframe)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe[self.buy_pr1.value]==self.buy_vol1.value)
                # |(dataframe[self.buy_pr2.value]==self.buy_vol2.value)
            ),
            'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
            #     (dataframe[self.sell_pr1.value]==self.sell_vol1.value)|
            #     (dataframe[self.sell_pr2.value]==self.sell_vol2.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.

Daily Candlestick Pattern Entries with Configurable Risk Controls

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