Cluc4werk Strategy In-Depth Analysis

Strategy ID: #88 (8th in Batch 9) Strategy Type: Bollinger Bands + ROCR Trend Filter Timeframe: 1 Minute (1m)

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I. Strategy Overview

Cluc4werk is a short-cycle trading strategy based on Bollinger Bands and ROCR (Rate of Change Ratio) indicators. The strategy's core feature is using the 1-hour ROCR indicator for trend filtering, while combining a dual Bollinger Band system (40-period and 20-period) to capture short-term trading opportunities.

Key Features

Feature	Description
Buy Conditions	2 modes (BinHV variant + Cluc variant)
Sell Conditions	Price breaks Bollinger middle band
Protection	ROCR trend filter + Hard stop-loss
Timeframe	1 Minute (main) + 1 Hour (informative)
Dependencies	TA-Lib, technical, qtpylib, numpy
Special Features	Multi-timeframe analysis, Dual Bollinger Band system

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II. Strategy Configuration Analysis

2.1 Core Risk Parameters

# ROI Exit Table
minimal_roi = {
    "0": 0.015,    # Immediate exit: 1.5% profit
    "20": 0.005,   # After 20 minutes: 0.5% profit
    "30": 0.001,   # After 30 minutes: 0.1% profit
}

# Stop-Loss Setting
stoploss = -0.01  # -1% hard stop-loss

# Trailing Stop Configuration
trailing_stop = False  # Not enabled

# Exit Signal Configuration
use_exit_signal = True
exit_profit_only = True
ignore_roi_if_entry_signal = True

Design Philosophy:

• Multi-Tier ROI: 3-tier decreasing ROI; exit threshold lowers as holding time extends
• Tight Stop-Loss: -1% hard stop-loss; relatively tight
• Exit Signal Only: exit_profit_only = True; exits only when profitable

2.2 Timeframe Configuration

timeframe = '1m'  # Main timeframe: 1 minute

# Informative Timeframe
def informative_pairs(self):
    pairs = self.dp.current_whitelist()
    informative_pairs = [(pair, '1h') for pair in pairs]
    return informative_pairs

Description:

• Main timeframe: 1 minute (short-term trading)
• Informative timeframe: 1 hour (trend filtering)

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III. Entry Conditions Details

3.1 ROCR Trend Filter

# 1-hour period ROCR filter
dataframe['rocr_1h'].gt(0.65)

Meaning: 1-hour period ROCR (168 periods) greater than 0.65 indicates 1-hour timeframe is in an uptrend.

3.2 Buy Conditions - Mode 1 (BinHV Variant)

(
    dataframe['lower'].shift().gt(0) &
    dataframe['bbdelta'].gt(dataframe['close'] * 0.006) &
    dataframe['closedelta'].gt(dataframe['close'] * 0.013) &
    dataframe['tail'].lt(dataframe['bbdelta'] * 0.968) &
    dataframe['close'].lt(dataframe['lower'].shift()) &
    dataframe['close'].le(dataframe['close'].shift())
)

Logic:

• lower.shift() > 0: Bollinger lower band valid (not NaN)
• bbdelta > close * 0.006: Bollinger bandwidth > 0.6%
• closedelta > close * 0.013: Price change > 1.3%
• tail < bbdelta * 0.968: Lower shadow < 96.8% of bandwidth
• close < lower.shift(): Price < previous candle's Bollinger lower band
• close <= close.shift(): Price not higher than previous candle's close

3.3 Buy Conditions - Mode 2 (Cluc Variant)

(
    (dataframe['close'] < dataframe['ema_slow']) &
    (dataframe['close'] < 0.013 * dataframe['bb_lowerband']) &
    (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 28))
)

Logic:

• close < ema_slow: Price < EMA50 (weak trend)
• close < 0.013 * bb_lowerband: Price < Bollinger lower band × 0.013 (extremely low price)
• volume < volume_mean_slow.shift(1) * 28: Volume < 30-day average × 28 (ultra-low volume)

3.4 Combined Buy Logic

dataframe.loc[
    (
        dataframe['rocr_1h'].gt(0.65)  # 1-hour trend filter
    ) &
    (
        # Mode 1 OR Mode 2
        (Mode 1 conditions) | (Mode 2 conditions)
    ),
    'buy'
] = 1

Key Point:

• Must satisfy 1-hour ROCR > 0.65 (trend filter)
• Satisfy either of the two buy modes to enter

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IV. Exit Logic Details

4.1 Sell Conditions

(
    (qtpylib.crossed_above(dataframe['close'], dataframe['bb_middleband'])) &
    (dataframe['volume'] > 0)
)

Logic:

• crossed_above(close, bb_middleband): Price crosses above Bollinger middle band
• volume > 0: Volume confirmation

Combined Meaning: Sell when price breaks above Bollinger middle band with volume confirmation.

4.2 ROI Exit Mechanism

Time	Minimum Profit
0 minutes	1.5%
20 minutes	0.5%
30 minutes	0.1%

4.3 Exit Signal Configuration

use_exit_signal = True       # Enable exit signal
exit_profit_only = True      # Exit only when profitable
ignore_roi_if_entry_signal = True  # Ignore ROI if new entry signal

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V. Technical Indicator System

5.1 Core Indicators

Indicator Category	Specific Indicator	Parameter	Purpose
Volatility Indicator	Bollinger Bands	40 periods, 2x std dev	BinHV variant
Volatility Indicator	Bollinger Bands	20 periods, 2x std dev	Cluc variant
Trend Indicator	EMA	50 periods	Price trend judgment
Momentum Indicator	ROCR	28 periods (1m) / 168 periods (1h)	Trend filter
Volume	Volume MA	30 periods	Volume filter

5.2 Custom Bollinger Band Function

def bollinger_bands(stock_price, window_size, num_of_std):
    rolling_mean = stock_price.rolling(window=window_size).mean()
    rolling_std = stock_price.rolling(window=window_size).std()
    lower_band = rolling_mean - (rolling_std * num_of_std)
    return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)

Description: Custom 40-period Bollinger Band calculation for BinHV variant.

5.3 Multi-Timeframe Analysis

Strategy uses 1-hour ROCR indicator for trend filtering:

# 1-minute ROCR
dataframe['rocr'] = ta.ROCR(dataframe, timeperiod=28)

# 1-hour ROCR
inf_tf = '1h'
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
informative['rocr'] = ta.ROCR(informative, timeperiod=168)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)

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VI. Risk Management Features

6.1 Tight Stop-Loss

stoploss = -0.01  # -1%

Description: -1% tight stop-loss; suitable for short-term trading.

6.2 ROCR Trend Filter

dataframe['proch'].gt(0.65)

Function:

• Only buy when 1-hour uptrend confirmed
• Avoid counter-trend trading
• Reduce false signals

6.3 ROI Exit Mechanism

Time	Minimum Profit
0 minutes	1.5%
20 minutes	0.5%
30 minutes	0.1%

Strategy: Quick accumulation of small profits; suitable for high-frequency trading.

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VII. Strategy Pros & Cons

Advantages

1. ROCR Trend Filter: Uses 1-hour ROCR to filter; avoids counter-trend trades
2. Dual Bollinger Bands: 40-period + 20-period; covers different time dimensions
3. Tight Stop-Loss: -1% stop-loss; controls single-trade loss
4. Quick Exit: 1.5% initial ROI; quickly accumulates profits
5. Multi-Timeframe: Combines 1-minute and 1-hour analysis

Limitations

1. Medium Complexity: Dual Bollinger Bands + multi-timeframe; requires experience to debug
2. Parameter Sensitive: ROCR threshold 0.65 may need per-market adjustment
3. High Trading Frequency: 1-minute timeframe may lead to overtrading
4. Trend Dependent: ROCR filter may miss trend start opportunities
5. Informative Timeframe Delay: 1-hour ROCR may lag

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VIII. Applicable Scenarios

Market Environment	Recommended Configuration	Notes
Ranging Market	Adjust ROCR threshold	Lower ROCR threshold increases signals
Uptrend	Default configuration	ROCR filter effective
Downtrend	Pause trading	ROCR filter may fail
High Volatility	Adjust stop-loss	1% stop-loss may be too tight
Low Volatility	Adjust ROI	May need to lower ROI threshold

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IX. Applicable Market Environment Details

Cluc4werk is a strategy based on the "Bollinger Bands + ROCR Trend Filter" core philosophy.

9.1 Core Strategy Logic

• ROCR Trend Filter: 1-hour ROCR > 0.65; ensures uptrend
• Dual Bollinger Bands: 40-period + 20-period; covers different time dimensions
• Price Breakout: Sell when price breaks Bollinger middle band

9.2 Performance in Different Market Environments

Market Type	Rating	Analysis
Uptrend	Four Stars	ROCR filter effective; trend-following
Wide-range Ranging	Three Stars	Bollinger signals frequent; may overtrade
Downtrend	Two Stars	ROCR filter may fail
Fast Fluctuation	Four Stars	1-minute framework responsive

9.3 Key Configuration Suggestions

Configuration	Suggested Value	Notes
Number of trading pairs	10-20	Higher signal frequency
Maximum open trades	2-4	Control risk
Position mode	Fixed position	Recommend fixed position
Timeframe	1m	Mandatory requirement

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X. Important Notes: The Cost of Complexity

10.1 Medium Learning Curve

Strategy code is ~80 lines; requires understanding:

• Bollinger Band calculation
• ROCR indicator principle
• Multi-timeframe analysis

10.2 Low Hardware Requirements

Single timeframe calculation is light:

Number of Pairs	Minimum RAM	Recommended RAM
10-20 pairs	512MB	1GB
20-40 pairs	1GB	2GB

10.3 Multi-Timeframe Notes

Strategy uses 1-hour informative timeframe:

• Requires historical 1-hour data
• Data delay may affect signal accuracy
• Needs stable data source for live trading

10.4 Manual Trading Suggestions

Manual traders can reference this strategy's approach:

• Observe both 1-minute and 1-hour charts simultaneously
• Use ROCR to confirm trend direction
• Set 1.5% take-profit and 1% stop-loss

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XI. Summary

Cluc4werk is a meticulously designed Bollinger Band + ROCR trend filter strategy, with core value in:

1. ROCR Trend Filter: 1-hour ROCR > 0.65; ensures trend-following trades
2. Dual Bollinger Bands: 40-period + 20-period; covers different time dimensions
3. Tight Stop-Loss: -1% stop-loss; controls single-trade loss
4. Quick Exit: 1.5% initial ROI; suitable for high-frequency trading
5. Multi-Timeframe: Combines 1-minute and 1-hour analysis

For quantitative traders, this is an excellent Bollinger Band + trend filter template. Recommendations:

• Use as an entry case for learning multi-timeframe analysis
• Understand ROCR indicator application methods
• Note trading frequency may be high; configure reasonably
• Sufficient testing before live trading; pay attention to slippage and fees