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BigPete Strategy Analysis

Strategy Number: #55
Strategy Type: BigZ04 Improved Version with Adaptive Trailing Stop
Timeframe: 5 minutes (5m) + Information Timeframe 1h

I. Strategy Overview​

BigPete is an improved quantitative trading strategy based on BigZ04, developed by Perkmeister. This strategy adds a custom trailing stop system on top of BigZ04, achieving more flexible risk-reward management. Its core design philosophy is to pursue larger profits in trend situations while controlling maximum drawdown.

Core Features​

FeatureConfiguration Value
Timeframe5 minutes (5m)
Information Timeframe1 hour (1h)
Take-Profit (ROI)0-30 min: 10%, 30-60 min: 5%, 60+ min: 2%
Stoploss-0.99 (disable default stoploss, use custom stoploss)
Trailing StopEnabled, adaptive trailing stop system
Number of Entry Conditions13 independent conditions
Recommended Number of Pairs2-4 (suggested)
FeaturesDynamic trailing stop + layered take-profit

II. Strategy Configuration Analysis​

2.1 Base Configuration​

timeframe = "5m"
inf_1h = "1h"
minimal_roi = {
    "0": 0.10,    # Immediate 10% profit on entry
    "30": 0.05,   # 5% after 30 minutes
    "60": 0.02    # 2% after 60 minutes
}
stoploss = -0.99  # Disable default stoploss
trailing_stop = True
trailing_stop_positive = 0.003
trailing_stop_positive_offset = 0.0187

2.2 Trailing Stop Parameters (Core Feature)​

# Hard stoploss threshold
pHSL = -0.08  # Forced stoploss if loss exceeds 8%

# Profit threshold 1 (triggers first-level trailing)
pPF_1 = 0.016  # 1.6% profit triggers
pSL_1 = 0.011  # Corresponding stoploss 1.1%

# Profit threshold 2 (triggers second-level trailing)
pPF_2 = 0.080  # 8% profit triggers
pSL_2 = 0.040  # Corresponding stoploss 4%

# Dynamic stoploss formula
# Profit > 8%: sl_profit = 4% + (current profit - 8%)
# Profit 1.6%-8%: sl_profit = 1.1% + (current profit - 1.6%) × ratio
# Profit < 1.6%: sl_profit = -8% (hard stoploss)

2.3 Key Parameters​

# Volume parameters
buy_volume_pump_1 = 0.1    # Volume ratio compared to 48-period mean
buy_volume_drop_1 = 5.4    # Volume contraction multiple

# RSI parameters
buy_rsi_1h_0 = 81.7        # High RSI (condition 0)
buy_rsi_1h_1 = 14.2        # Low RSI (conditions 1-4)
buy_rsi_0 = 11.2           # 5-minute RSI
buy_rsi_1 = 15.7
buy_rsi_2 = 11.3

# MACD parameters
buy_macd_1 = 0.05
buy_macd_2 = 0.03

III. Entry Conditions Details​

BigPete contains 13 independent entry conditions:

3.1 Condition 0: High RSI + Price Decline Pattern​

(dataframe["close"] > dataframe["ema_200"]) &
(dataframe["rsi"] < 11.2) &
((dataframe["close"] * 1.029 < dataframe["open"].shift(3)) | 
 (dataframe["close"] * 1.029 < dataframe["open"].shift(2)) |
 (dataframe["close"] * 1.029 < dataframe["open"].shift(1))) &
(dataframe["rsi_1h"] < 81.7)

Logic: Price above EMA200 but RSI extremely low (11.2), indicates although long-term bullish but short-term oversold. Significant decline within consecutive 3 days.

3.2 Condition 1: Lower Bollinger Band + Bearish Candle​

(dataframe["close"] > dataframe["ema_200"]) &
(dataframe["close"] > dataframe["ema_200_1h"]) &
(dataframe["close"] < dataframe["bb_lowerband"] * 0.999) &
(dataframe["rsi_1h"] < 67.8) &
(dataframe["open"] > dataframe["close"])

Logic: Price near lower Bollinger Band, bearish close, 1-hour RSI moderate.

3.3 Condition 2: Deep Lower Band​

(dataframe["close"] > dataframe["ema_200"]) &
(dataframe["close"] < dataframe["bb_lowerband"] * 1.01)

Logic: Looser lower band condition, just needs to approach Bollinger Band.

3.4 Condition 3: Above 1-hour EMA200 + RSI Oversold​

(dataframe["close"] > dataframe["ema_200_1h"]) &
(dataframe["close"] < dataframe["bb_lowerband"]) &
(dataframe["rsi"] < 35.6)

3.5 Condition 4: Extremely Low 1-hour RSI​

(dataframe["rsi_1h"] < 16.5) &
(dataframe["close"] < dataframe["bb_lowerband"])

3.6 Condition 5: MACD Golden Cross + Lower Bollinger Band​

(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"] * 0.05)) &
(dataframe["close"] < dataframe["bb_lowerband"])

3.7 Conditions 6-7: MACD Different Parameter Combinations​

Similar to condition 5, but using different RSI and MACD parameter thresholds.

3.8 Conditions 8-9: Dual RSI Oversold​

(dataframe["rsi_1h"] < threshold) &
(dataframe["rsi"] < threshold)

3.9 Condition 10: 1-hour Oversold + MACD Reversal​

(dataframe["rsi_1h"] < 31.3) &
(dataframe["close_1h"] < dataframe["bb_lowerband_1h"]) &
(dataframe["hist"] > 0) &
(dataframe["hist"].shift(2) < 0) &
(dataframe["rsi"] < 40.5)

3.10 Condition 11: Volume Narrow Range Oscillation​

# Consecutive 10 candles range < 1%
((dataframe["high"] - dataframe["low"]) < dataframe["open"] / 100)

3.11 Condition 12: False Breakout Pattern​

(dataframe["close"] < dataframe["bb_lowerband"] * 0.993) &
(dataframe["low"] < dataframe["bb_lowerband"] * 0.985) &
(dataframe["close"].shift() > dataframe["bb_lowerband"])

IV. Exit Logic Explained​

4.1 Custom Trailing Stoploss (Core)​

BigPete's biggest feature is its adaptive trailing stop system:

def custom_stoploss(current_profit):
    HSL = -0.08  # Hard stoploss -8%
    PF_1 = 0.016  # 1.6% profit threshold
    SL_1 = 0.011  # 1.1% corresponding stoploss
    PF_2 = 0.080  # 8% profit threshold
    SL_2 = 0.040  # 4% corresponding stoploss
    
    if current_profit > PF_2:
        # Profit > 8%: stoploss line moves up, lock more profits
        sl_profit = SL_2 + (current_profit - PF_2)
    elif current_profit > PF_1:
        # Profit 1.6%-8%: linear interpolation
        sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
    else:
        # Profit < 1.6%: use hard stoploss -8%
        sl_profit = HSL
    
    return stoploss_from_open(sl_profit, current_profit)

Logic Interpretation:

  • Profit < 1.6%: Maximum loss 8% (hard stoploss protection)
  • Profit 1.6%-8%: Stoploss line moves up, 1.1% → 4%
  • Profit > 8%: Stoploss line follows profit increase, lock most profits

4.2 Profit Protection Mechanism Diagram​

Profit:   -8%   0%    1.6%   4%    8%    12%   16%   20%
          |-----|-----|------|-----|-----|-----|-----|
Stoploss: -8%  -8%   -8%   -1.1% -4%   -8%  -12%  -16%
                 (Hard)    (Dynamic Rise)

4.3 ROI Time Take-Profit​

minimal_roi = {
    "0": 0.10,    # 10% profit on entry!
    "30": 0.05,   # 5% after 30 minutes
    "60": 0.02    # 2% after 60 minutes
}

Note: Due to using trailing stop, ROI mainly plays role in early stage.

V. Technical Indicator System​

5.1 5-Minute Cycle Indicators​

Indicator NameParametersUsage
EMA200200Long-term trend judgment
EMA12/2612,26MACD calculation
SMA55Short-term trend
RSI14Momentum
Bollinger Bands20,2Overbought/oversold
ATR14Volatility

5.2 1-Hour Cycle Indicators​

Indicator NameParametersUsage
SMA5050Mid-term trend
SMA200200Long-term trend
RSI141-hour momentum
Bollinger Bands20,2Overbought/oversold

VI. Risk Management Features​

6.1 Adaptive Stoploss System​

BigPete's core innovation is dynamically adjusting stoploss line based on profit:

  1. Loss Phase (profit < 1.6%):

    • Hard stoploss -8%
    • Allow certain floating loss room

  2. Initial Profit Phase (profit 1.6%-8%):

    • Stoploss line moves up to -1.1% to -4%
    • Protect existing profits

  3. Large Profit Phase (profit > 8%):

    • Stoploss line follows increase
    • Always lock at least 4% profit

6.2 Differences from BigZ04​

FeatureBigZ04BigPete
Stoploss TypeFixed time stoplossAdaptive trailing stoploss
Hard Stoploss-10%-8%
Trailing StopNoneYes
Profit TargetLadder ROI10% first target

VII. Strategy Pros & Cons​

7.1 Advantages​

  1. Flexible Stoploss: Automatically adjusts based on profit, controllable risk
  2. Trend Following: Can capture more profits in big trends
  3. Multi-Condition Coverage: 13 conditions cover various patterns
  4. Dual Verification: 5-minute + 1-hour timeframe

7.2 Limitations​

  1. Complex Parameters: Multiple threshold parameters difficult to optimize
  2. High Target: 10% first target may cause some trades to fail
  3. Volatility Sensitive: Trailing stop may be triggered by normal fluctuations

VIII. Applicable Scenarios​

  • Pullbacks in strong trends
  • High volatility markets
  • Major cryptocurrency pairs
  • Low volatility sideways
  • Violently volatile junk coins
  • Scenarios requiring quick trading

IX. Applicable Market Environments Explained​

9.1 Ideal Environment​

  1. Clear trends: Single-sided rise or decline followed by pullback
  2. Normal volatility: Daily volatility 5-15%
  3. High liquidity: Major coins

9.2 Warning Environments​

  • ⚠️ Sideways range (high trading frequency, low win rate)
  • ⚠️ Sharp decline situations (hard stoploss will trigger)
  • ⚠️ Extreme volatility (trailing stop too narrow)

X. Important Reminders: The Cost of Complexity​

10.1 Cost of Trailing Stop​

Although trailing stop can protect profits, it also has costs:

  1. Normal Pullback Trigger: Normal fluctuations in trends may trigger stoploss
  2. Parameter Sensitivity: PF_1, PF_2 and other parameters need fine adjustment
  3. Complexity Increase: Difficult to understand actual P&L sources

10.2 Recommendations​

  1. Observe First: Run with default values for 2 weeks
  2. Record Trades: Analyze which stoplosses were "correct"
  3. Adjust Cautiously: Only change one parameter at a time

XI. Summary​

BigPete is an enhanced version of BigZ04, achieving smarter risk management through adaptive trailing stop system:

  • ✅ 13 entry conditions, cover various patterns
  • ✅ Trailing stop, protect profits
  • ✅ 8% hard stoploss, control maximum loss
  • ⚠️ Complex parameters, need time to understand
  • ⚠️ 10% first target relatively high

Suitable for: Traders with certain experience, pursuing steady profit growth.

This document is based on BigPete.py code auto-generated