CBPete9 Strategy Analysis

Strategy ID: Batch 08
Strategy Type: Multi-Condition Oversold Reversal + Fast Exit
Timeframe: 5 minutes (primary) | 1 hour (informational)

---

I. Strategy Overview

CBPete9 (Combined Bin H Cluc And MAD V9) is an optimized trading strategy derived from ilya's CombinedBinHClucAndMADV9. The strategy's core design philosophy is capturing short-term rebound opportunities while controlling drawdowns, using multi-dimensional technical indicator combinations to implement a "buy low, sell fast" trading logic.

Core Attribute	Value
Strategy Name	CBPete9
Author	ilya (original), based on iterativ's CombinedBinHAndCluc series
Timeframe	5 minutes
Informational Timeframe	1 hour
Minimal ROI	0.028 (0 min) → 0.018 (10 min) → 0.005 (40 min)
Stop-Loss	-0.99 (substantially disabled, uses custom stop-loss)
Trailing Stop	Enabled
Buy Signals	10 independent conditions
Sell Signal	Price breaks Bollinger Band middle band 1.01×

Core Design Philosophy:

• Minimize drawdowns as much as possible
• Buy when price has fallen significantly (counter-trend/left-side trading)
• Sell quickly (release capital for next trade)
• Soft-check market uptrend (uptrend confirmation)
• Hard-check market decline (downtrend confirmation)
• Custom stop-loss mechanism to prevent single catastrophic losses

---

II. Strategy Configuration Analysis

2.1 Timeframe Configuration

timeframe = '5m'      # Primary trading: 5-minute candles
inf_1h = '1h'         # Informational: 1-hour candles (for trend filtering)

2.2 Take-Profit and Stop-Loss Configuration

minimal_roi = {
    "0": 0.028,      # Immediately available 2.8% profit
    "10": 0.018,     # After 10 minutes: 1.8% profit
    "40": 0.005,     # After 40 minutes: 0.5% profit
}

stoploss = -0.99     # Base stop-loss set to -99%, substantially disabled

Design Intent: The strategy does not rely on traditional stop-loss, but implements finer risk control through a custom stop-loss function. The minimal ROI curve indicates the strategy expects to complete trades within a short period (40 minutes).

2.3 Trailing Stop Configuration

trailing_stop = True
trailing_only_offset_is_reached = True
trailing_stop_positive = 0.003        # Activates when profit reaches 1.87%
trailing_stop_positive_offset = 0.0187

2.4 Order Type Configuration

order_types = {
    'entry': 'market',
    'exit': 'market',
    'stoploss': 'market',
    'stoploss_on_exchange': False
}

All use market orders for immediate execution.

---

III. Entry Conditions Details

CBPete9 contains 10 independent buy conditions, combined through Boolean "OR" logic. Meeting any one condition triggers a buy signal.

Condition 1: Dual Timeframe EMA Filter + Bollinger Band Rebound

(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['close'] < dataframe['bb_lowerband'] * 0.99) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4) &
(dataframe['volume'] < dataframe['volume'].shift() * 4) &
(dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2))

Logic: Price is in an uptrend (dual-timeframe EMA confirmed), but briefly touched the Bollinger Band lower band forming a rebound point, with volume showing a "first surge then contract" accumulation pattern.

Condition 2: Single Timeframe EMA + Bollinger Band Extreme Oversold

(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] < dataframe['bb_lowerband'] * 0.982) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4) &
(dataframe['volume'] < dataframe['volume'].shift() * 4)

Logic: Compared to Condition 1, relaxes the 1-hour EMA requirement but requires price closer to the Bollinger Band lower band (0.982×).

Condition 3: 1-Hour Trend Confirmation + RSI Oversold

(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['rsi'] < 14.2) &
(dataframe['volume'] < dataframe['volume'].shift() * 4)

Logic: 1-hour confirms uptrend, 5-minute shows RSI extreme oversold (<14.2), forming high-probability short-term rebound entry.

Condition 4: 1-Hour RSI Extreme Oversold

(dataframe['rsi_1h'] < 16.5) &
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['volume'] < dataframe['volume'].shift() * 4)

Logic: Uses 1-hour RSI to determine if the overall market is in an extreme oversold state.

Condition 5: Dual Timeframe EMA Trend + MACD Golden Cross

(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > dataframe['open'] * 0.02) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > dataframe['open']/100) &
(dataframe['close'] < dataframe['bb_lowerband'])

Logic: Multi-timeframe trend confirmation with MACD golden cross forms a "trend + momentum" dual-confirmation entry.

Condition 6: MACD Strong Golden Cross

(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > dataframe['open'] * 0.03) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > dataframe['open']/100) &
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['volume'] < dataframe['volume'].shift() * 4)

Logic: Compared to Condition 5, places greater emphasis on MACD momentum strength (difference requirement increased from 0.02 to 0.03).

Condition 7: 1-Hour RSI Oversold + MACD Golden Cross

(dataframe['rsi_1h'] < 15.0) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > dataframe['open'] * 0.02) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > dataframe['open']/100) &
(dataframe['volume'] < dataframe['volume'].shift() * 4) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4)

Logic: Combines 1-hour RSI oversold and MACD golden cross for "long-cycle oversold + short-cycle momentum reversal" dual entry.

Condition 8: Dual Timeframe RSI Oversold

(dataframe['rsi_1h'] < 20.0) &
(dataframe['rsi'] < 28.0) &
(dataframe['volume'] < dataframe['volume'].shift() * 4) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4)

Logic: Both 5-minute and 1-hour RSI are in oversold territory simultaneously (<28 and <20), suggesting the market may be at a short-term bottom.

Condition 9: 1-Hour RSI Moderate Oversold + 5-Minute RSI Extreme Oversold

(dataframe['rsi_1h'] < 35.0) &
(dataframe['rsi'] < 10.0) &
(dataframe['volume'] < dataframe['volume'].shift() * 4) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4)

Logic: Allows 1-hour RSI in a relatively moderate oversold zone (<35), but requires 5-minute RSI extremely oversold (<10), suitable for fast rebound scenarios.

Condition 10: Trend Reversal Combined Signal

(dataframe['close'] < dataframe['sma_5']) &
(dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) &
(dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
(dataframe['rsi'] < dataframe['rsi_1h'] - 43.276) &
(dataframe['volume'] > 0)

Logic: Complex trend reversal signal combining SSL Channel, EMA crossovers, and RSI divergence.

---

IV. Exit Logic Details

4.1 Main Sell Condition

dataframe.loc[
    (
        (dataframe['close'] > dataframe['bb_middleband'] * 1.01) &
        (dataframe['volume'] > 0)
    ),
    'sell'
] = 1

Design Philosophy: The strategy's core philosophy is "sell quickly." When price rebounds to 1% above the Bollinger Band middle band, immediately take profit and exit, freeing capital for the next trade.

---

V. Risk Management Highlights

5.1 Custom Stop-Loss Mechanism

def custom_stoploss(self, pair, trade, current_time, current_rate, current_profit, **kwargs):
    # Profit state: trailing stop
    if current_profit > 0:
        return 0.99

    # Loss state: time-based stop
    trade_time_50 = current_time - timedelta(minutes=50)

    if trade_time_50 > trade.open_date_utc:
        try:
            number_of_candle_shift = int((trade_time_50 - trade.open_date_utc).total_seconds() / 300)
            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            candle = dataframe.iloc[-number_of_candle_shift].squeeze()

            # Price still falling: stop-loss
            if current_rate * 1.015 < candle['open']:
                return 0.01  # Stop-loss exit
        except IndexError:
            return 0.01

    return 0.99

Logic:

1. When profitable (current_profit > 0): Returns 0.99, essentially not disabling trailing stop, letting trailing stop rules manage it
2. After holding over 50 minutes and at a loss:
   • Checks if price continued falling during holding period
   • If current price is 1.5% below the opening price 50 minutes ago, immediately stop-loss
   • This is the strategy's core mechanism to "prevent catastrophic drops"

---

VI. Strategy Pros & Cons

Strengths

1. Multi-Dimensional Signal Filtering: 10 independent buy conditions, verifying signals from multiple dimensions including trend, momentum, and volume
2. Dual-Period Analysis: Combines 5-minute and 1-hour periods, capturing short-term opportunities while filtering counter-trend trades
3. Fast Trading: Completes trades within 40 minutes, improving capital utilization
4. Custom Stop-Loss: Specifically designed for "continued decline" scenarios, avoiding long-term holding in losing positions
5. No Optimization Needed: Pre-set parameters ready to use, reducing the usage barrier

Weaknesses

1. High Trading Frequency: Preset 2–4 simultaneous positions may generate high trading fees
2. Conservative Profit Targets: Maximum 2.8% profit target may be frequently stopped out in ranging markets
3. Simple Sell Logic: Only relies on Bollinger Band middle band for exits, may miss significant rallies
4. Volume Dependency: Multiple conditions rely on volume contraction confirmation, may fail in low-liquidity markets
5. Parameter Sensitivity: Some parameters (RSI thresholds, volume ratios) need market-specific adjustment

---

VII. Summary

CBPete9 is a meticulously designed short-term trading strategy with the core philosophy of "capturing short-term rebound opportunities while controlling drawdowns".

Its main features:

1. 10 Independent Buy Conditions: Multi-dimensional signal verification including trend, momentum, and volume
2. Dual-Period Analysis: Combines 5-minute and 1-hour periods for sensitivity and robustness
3. Custom Stop-Loss: Specifically designed for "continued decline" scenarios, effectively controlling risk
4. Fast Trading: Completes trades within 40 minutes, high capital efficiency
5. No Optimization Needed: Pre-set parameters ready to use

Risk Warning:

• High trading frequency may generate significant fees
• May underperform in one-sided trending markets
• Strategy complexity is high; requires thorough understanding before personalizing

---

This document is auto-generated based on the CBPete9 strategy source code.