Power Tower: A Five-Minute Crypto Candlestick Entry and Exit Strategy
Code Freqtrade
Summary
This Freqtrade strategy is designed for spot cryptocurrency trading on five-minute candles. It defines parameterized entry and exit rules using comparisons between recent closing prices and closes from earlier bars, with separate adjustable parameters for buying and selling. It also sets a return-on-investment schedule, a wide fixed stop loss, and no trailing stop. The code does not calculate additional indicators, despite importing indicator libraries.
The document reports a backtest summary of 67 trades, with 32 wins, 34 draws, and one loss, an average profit of 1.23%, and total profit of 81.51% in USDT. These figures are presented without the tested dates, exchange, fee assumptions, market regime, or validation method. The power comparisons in the rules may also behave unexpectedly because they exponentiate price values, so the reported results should not be treated as evidence of robustness or expected live performance.
Key ideas
- The strategy targets spot crypto markets using five-minute candles and does not enable short selling.
- Entry and exit signals compare recent closes with older closes raised to adjustable powers.
- The configuration includes a staged return target schedule, a fixed stop loss, and no trailing stop.
- The article reports backtest trade and profit statistics but omits key test conditions.
- The unusual exponent-based price comparisons make the implementation and reported results difficult to assess without further validation.
Tags
Full text
# PowerTower.py
```py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np
import pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IntParameter, IStrategy, merge_informative_pair)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib
class PowerTower(IStrategy):
# By: Masoud Azizi (@mablue)
# Power Tower is a complitly New Strategy(or Candlistic Pattern or Indicator) to finding strongly rising coins.
# much effective than "Three black Crows" but based on Idea of this candlestick pattern, but with different rules!
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 3
# Optimal timeframe for the strategy.
timeframe = '5m'
# Can this strategy go short?
can_short: bool = False
# $ freqtrade hyperopt -s PowerTower --hyperopt-loss SharpeHyperOptLossDaily
# "max_open_trades": 1,
# "stake_currency": "USDT",
# "stake_amount": 990,
# "dry_run_wallet": 1000,
# "trading_mode": "spot",
# "XMR/USDT","ATOM/USDT","FTM/USDT","CHR/USDT","BNB/USDT","ALGO/USDT","XEM/USDT","XTZ/USDT","ZEC/USDT","ADA/USDT",
# "CHZ/USDT","BTT/USDT","LUNA/USDT","VRA/USDT","KSM/USDT","DASH/USDT","COMP/USDT","CRO/USDT","WAVES/USDT","MKR/USDT",
# "DIA/USDT","LINK/USDT","DOT/USDT","YFI/USDT","UNI/USDT","FIL/USDT","AAVE/USDT","KCS/USDT","LTC/USDT","BSV/USDT",
# "XLM/USDT","ETC/USDT","ETH/USDT","BTC/USDT","XRP/USDT","TRX/USDT","VET/USDT","NEO/USDT","EOS/USDT","BCH/USDT",
# "CRV/USDT","SUSHI/USDT","KLV/USDT","DOGE/USDT","CAKE/USDT","AVAX/USDT","MANA/USDT","SAND/USDT","SHIB/USDT",
# "KDA/USDT","ICP/USDT","MATIC/USDT","ELON/USDT","NFT/USDT","ARRR/USDT","NEAR/USDT","CLV/USDT","SOL/USDT","SLP/USDT",
# "XPR/USDT","DYDX/USDT","FTT/USDT","KAVA/USDT","XEC/USDT"
# "method": "StaticPairList"
# 38/100: 67 trades. 32/34/1 Wins/Draws/Losses.
# Avg profit 1.23%. Median profit 0.00%.
# Total profit 815.05358020 USDT ( 81.51%).
# Avg duration 10:58:00 min. Objective: -9.86920
# ROI table:
minimal_roi = {
"0": 0.213,
"39": 0.048,
"56": 0.029,
"159": 0
}
# Stoploss:
stoploss = -0.288
# Trailing stop:
trailing_stop = False # value loaded from strategy
trailing_stop_positive = None # value loaded from strategy
trailing_stop_positive_offset = 0.0 # value loaded from strategy
trailing_only_offset_is_reached = False # value loaded from strategy
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Strategy parameters
buy_pow = DecimalParameter(0, 4, decimals=3, default=3.849, space="buy")
sell_pow = DecimalParameter(0, 4, decimals=3, default=3.798, space="sell")
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['close'].shift(0) > dataframe['close'].shift(2) ** self.buy_pow.value) &
(dataframe['close'].shift(1) > dataframe['close'].shift(3) ** self.buy_pow.value) &
(dataframe['close'].shift(2) > dataframe['close'].shift(4) ** self.buy_pow.value)
),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(
(dataframe['close'].shift(0) < dataframe['close'].shift(2) ** self.sell_pow.value) |
(dataframe['close'].shift(1) < dataframe['close'].shift(3) ** self.sell_pow.value) |
(dataframe['close'].shift(2) < dataframe['close'].shift(4) ** self.sell_pow.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.