PowerTower: A Three-Candle Crypto Momentum Entry and Exit Rule
Summary
PowerTower is a long-only crypto strategy on a five-minute timeframe. Its entry condition compares each of the latest three closes with closes two and four candles earlier, using an adjustable exponent; the exit condition applies a corresponding less-than comparison with a separate exponent. The strategy defines a startup history of 30 candles, fixed return-on-investment thresholds at different holding durations, and a stop loss of 28.8%. Trailing stops are disabled.
The source includes reported backtest figures: 67 trades, with 32 wins, 34 draws, and one loss; average profit is listed as 1.23%, median profit as 0.00%, and total profit as 815.05358020 USDT (81.51%). These results are presented in code comments without a clear test period or enough detail to assess costs, data selection, or reproducibility. The power comparisons also depend on the price scale and are not normalized returns, limiting portability across assets or quote conventions. The document provides executable strategy logic, but no discussion of drawdown or independent validation supports the reported performance.
Key ideas
- The strategy is long-only and specifies a five-minute timeframe.
- Entry and exit signals compare recent closes with closes from earlier candles using tunable exponents.
- Risk settings include a 28.8% stop loss, time-dependent profit targets, and no trailing stop.
- The source comments report 67 trades and several profit statistics, but omit clear testing context and validation details.
- Exponentiating raw prices makes the rule sensitive to price scale and quote denomination.
Tags
Full text
# PowerTower
# PowerTower
## Source (GPL-3.0)
```python
# 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.