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Stinkfist Strategy In-Depth Analysis

Strategy Number: #389 (389th of 465 strategies)
Strategy Type: Custom Indicators + Dynamic Position Sizing + Price Protection Mechanism
Timeframe: 5 minutes (5m) + 1-hour informative layer (1h)

I. Strategy Overview​

Stinkfist is a complex trend-following strategy based on multiple custom technical indicators, supporting dynamic position sizing and sophisticated price protection mechanisms. The strategy name originates from Tool's classic song, embodying the design philosophy of "deep digging" — finding trading opportunities through multi-layered technical analysis.

Core Features​

FeatureDescription
Buy Conditions2 independent buy signal sets (new positions + additions), can trigger independently
Sell ConditionsMulti-condition combined exits, including dynamic stop-loss and position management
Protection Mechanism3 protection parameter groups (price protection, timeout check, order confirmation)
Timeframe5m main timeframe + 1h informative timeframe
Dependenciesnumpy, talib, technical.indicators, qtpylib, arrow, cachetools, pandas
Sub-strategiesBTC-specific version (Stinkfist_BTC) and ETH-specific version (Stinkfist_ETH)

II. Strategy Configuration Analysis​

2.1 Basic Risk Parameters​

# ROI exit table
minimal_roi = {
    "0": 0.05,      # Immediate: 5% profit target
    "10": 0.025,    # After 10 minutes: 2.5% profit target
    "20": 0.015,    # After 20 minutes: 1.5% profit target
    "30": 0.01,     # After 30 minutes: 1% profit target
    "720": 0.005,   # After 12 hours: 0.5% profit target
    "1440": 0       # After 24 hours: close position
}

# Stop-loss setting
stoploss = -0.40    # 40% hard stop-loss

Design Philosophy:

  • Adopts a stepped ROI mechanism — the longer the position is held, the lower the profit target
  • 40% loose stop-loss gives the strategy sufficient room for volatility
  • Forced exit after 24 hours to avoid long-term entrapment

2.2 Order Type Configuration​

use_sell_signal = True         # Enable sell signals
sell_profit_only = True        # Only use sell signals when profitable
ignore_roi_if_buy_signal = True  # Ignore ROI when buy signal is active

2.3 Buy Parameters​

buy_params = {
    'inf-pct-adr': 0.80,  # Informative layer ADR percentage threshold
    'mp': 30              # Momentum Pinball threshold
}

III. Buy Conditions Detailed Analysis​

3.1 Protection Mechanisms (3 Groups)​

Each buy condition is equipped with independent protection parameter groups:

Protection TypeParameter DescriptionDefault Example
Price ProtectionOrder price vs. current price deviation checkReject if > 1%
Buy TimeoutUnfilled order checkCancel if price deviation > 1%
Sell TimeoutUnfilled order checkCancel if price deviation < 1%

3.2 Buy Condition Classification​

The strategy contains two independent buy logic sets:

Condition Group 1: New Position Buy (4 Conditions)​

# Logic
- Informative layer condition: close <= 3d_low + (0.80 * adr)
- Momentum Pinball: mp < 30
- Streak ROC: streak-roc > pcc-lowerband
- MA Cross: mac == 1

Condition Details:

Condition #IndicatorConditionMeaning
1Informative Layerclose <= 3d_low + (0.80 * adr)Price near 3-day low + 80% daily volatility range
2Momentum Pinballmp < 30Momentum indicator shows oversold condition
3Streak ROCstreak-roc > pcc-lowerbandRate of change above lower band
4MA Crossmac == 1Fast MA crosses above slow MA

Condition Group 2: Position Addition Buy (Dynamic Growth Logic)​

# Logic (only triggers when active trades exist)
- RMI trend upward: rmi-up-trend == 1
- Profit protection: current_profit > (peak_profit * profit_factor)
- RMI growth threshold: rmi-slow >= linear_growth(30, 70, 180, 720)

Dynamic Growth Function:

def linear_growth(self, start: float, end: float, 
                  start_time: int, end_time: int, trade_time: int) -> float:
    # Linear growth from start (30) to end (70)
    # Time range: 180-720 minutes
    # The longer held, the higher the threshold

3.3 Informative Layer Indicators Detailed​

The strategy uses 1-hour timeframe as informative layer for higher-dimensional trend judgment:

# Informative layer indicators
informative['1d_high'] = informative['close'].rolling(24).max()   # 24-hour high
informative['3d_low'] = informative['close'].rolling(72).min()     # 3-day low
informative['adr'] = informative['1d_high'] - informative['3d_low']  # Volatility range

IV. Sell Logic Detailed Analysis​

4.1 Dynamic Stop-Loss System​

The strategy adopts a dynamic stop-loss mechanism:

# Dynamic stop-loss threshold calculation
loss_cutoff = linear_growth(-0.03, 0, 0, 300, open_minutes)
# The longer held, the tighter the stop-loss (from -3% to 0%)

4.2 Sell Signal Combination​

Condition GroupTrigger ConditionDescription
Dynamic Stop-Losscurrent_profit < loss_cutoff and profit > stoplossStop-loss tightens over time
RMI Trend Reversalrmi-dn-trend == 1RMI trend turning downward
Profit State Judgmentpeak_profit > 0 then rmi-slow < 50, else rmi-slow < 10Distinguishes profit/loss states
Position ManagementComprehensive judgment when other positions existConsiders global position

4.3 Sell Signal Logic​

# Sell signal combination
conditions.append(
    (trade_data['current_profit'] < loss_cutoff) & 
    (trade_data['current_profit'] > self.stoploss) &  
    (dataframe['rmi-dn-trend'] == 1) &
    (dataframe['volume'].gt(0))
)
if trade_data['peak_profit'] > 0:
    conditions.append(dataframe['rmi-slow'] < 50)
else:
    conditions.append(dataframe['rmi-slow'] < 10)

V. Technical Indicator System​

5.1 Core Indicators​

Indicator CategorySpecific IndicatorPurpose
RMIRMI(21, 5) slow, RMI(8, 4) fastRelative Momentum Index, trend judgment
MA StreakMA Streak(4)Consecutive up/down count
PCCPercent Change Channel(20, 2)Percentage change channel
MACMoving Average Cross(20, 50)MA cross determination
ROC/RSIROC(1), RSI(ROC, 3)Momentum pinball indicator

5.2 Informative Timeframe Indicators (1h)​

The strategy uses 1-hour timeframe as informative layer:

  • 24-hour High: 1d_high = close.rolling(24).max()
  • 3-day Low: 3d_low = close.rolling(72).min()
  • Volatility Range: adr = 1d_high - 3d_low

5.3 Custom Indicator Implementation​

MA Streak (Consecutive Count)​

def ma_streak(self, dataframe: DataFrame, period: int = 4) -> Series:
    # Calculate consecutive up/down count
    # Positive = consecutive up, negative = consecutive down

Percent Change Channel​

def pcc(self, dataframe: DataFrame, period: int = 20, mult: int = 2):
    # Similar to Keltner channel but uses percentage change
    # Calculates upper, middle, lower bands

MA Cross​

def mac(self, dataframe: DataFrame, fast: int = 20, slow: int = 50) -> Series:
    # Fast EMA (20) and slow EMA (50) cross determination
    # Returns 1 (bullish) or -1 (bearish)

VI. Risk Management Features​

6.1 Price Protection Mechanism​

The strategy implements three layers of price protection:

def confirm_trade_entry(self, pair: str, order_type: str, 
                        amount: float, rate: float, ...) -> bool:
    # Entry price protection: reject when current price > order price * 1.01
    if current_price > rate * 1.01:
        return False
    return True

def check_buy_timeout(self, pair: str, trade: Trade, order: dict, ...) -> bool:
    # Buy timeout: cancel order when price deviation > 1%
    if current_price > order['price'] * 1.01:
        return True
    return False

def check_sell_timeout(self, pair: str, trade: Trade, order: dict, ...) -> bool:
    # Sell timeout: cancel order when price deviation < 1%
    if current_price < order['price'] * 0.99:
        return True
    return False

6.2 Global Position Management​

The strategy tracks all open trades for comprehensive judgment:

Data ItemDescription
active_tradeWhether current pair has active trade
current_profitCurrent profit ratio
peak_profitPeak profit
open_minutesPosition holding time (minutes)
other_tradesWhether there are other trades
avg_other_profitAverage profit of other trades
biggest_loserWhether current is the biggest losing trade
free_slotsRemaining available positions

6.3 Cache Mechanism​

custom_current_price_cache: TTLCache = TTLCache(maxsize=100, ttl=300)
# 5-minute price cache, reduces API calls

VII. Strategy Advantages and Limitations​

✅ Advantages​

  1. Multi-layer Protection Mechanism: Three layers — price protection, timeout check, order confirmation
  2. Dynamic Position Sizing Logic: Intelligent addition based on holding time and profit state
  3. Informative Layer Assisted Decision: 1-hour timeframe provides higher-dimensional trend judgment
  4. Sub-strategy Customization: BTC and ETH versions have independent optimized parameters
  5. Global Position Management: Considers comprehensive profit and risk of all positions

⚠️ Limitations​

  1. High Complexity: Many custom indicators, difficult to understand and debug
  2. Loose Stop-Loss: 40% stop-loss may lead to significant drawdowns
  3. Real-time Data Dependency: Requires frequent current price and orderbook data
  4. Position Addition Risk: Adding positions during trend reversal may amplify losses

VIII. Applicable Scenario Recommendations​

Market EnvironmentRecommended ConfigurationDescription
Volatile MarketDefault parametersCapture opportunities using volatility range indicators
Trending MarketETH versionEnable trailing stop to lock profits
Ranging MarketRaise mp thresholdReduce false signals
High-volatility AssetsBTC versionDisable sell signals, exit via ROI

IX. Applicable Market Environment Detailed Analysis​

Stinkfist is a deep-digging strategy. Based on its code architecture and multi-layer protection mechanism, it is best suited for high-volatility market environments, while performing poorly during one-sided crashes.

9.1 Strategy Core Logic​

  • Informative Layer Assistance: Uses 1-hour timeframe's 3-day low and volatility range to determine entry timing
  • Dynamic Position Addition: Intelligently adjusts addition strategy based on holding time and profit state
  • Global Perspective: Considers comprehensive performance of all positions for position management
  • Price Protection: Prevents unfavorable execution during sharp price fluctuations

9.2 Performance in Different Market Environments​

Market TypeRatingAnalysis
📈 Volatile Uptrend⭐⭐⭐⭐⭐Captures pullback buying opportunities using volatility range indicators
🔄 Ranging Volatility⭐⭐⭐⭐☆Strategy design intent, effective with informative layer judgment
📉 One-sided Decline⭐⭐☆☆☆Position addition logic may amplify losses
⚡ Low-volatility Sideways⭐⭐☆☆☆Lacks clear trend signals

9.3 Key Configuration Recommendations​

Configuration ItemRecommended ValueDescription
inf-pct-adr0.80-0.92Controls entry position aggressiveness
mp30-66Momentum threshold, lower = more conservative
stoploss-0.40Keep loose to accommodate volatility
Timeframe5m + 1hDo not modify easily

X. Important Note: The Cost of Complexity​

10.1 Learning Curve​

The strategy contains multiple custom indicators (RMI, MA Streak, PCC, MAC), requiring deep understanding of each indicator's meaning and interactions. Recommend thorough testing in backtest environment first.

10.2 Hardware Requirements​

Number of PairsMinimum MemoryRecommended Memory
1-5 pairs2 GB4 GB
5-20 pairs4 GB8 GB
20+ pairs8 GB16 GB

10.3 Backtest vs. Live Trading Differences​

The strategy relies on real-time price data and orderbook information:

  • Backtest environment may not fully simulate price protection logic
  • Position addition logic is more sensitive in live trading
  • Cache mechanism may perform differently in backtests

10.4 Manual Trading Recommendations​

If wanting to apply this strategy in manual trading:

  1. Focus on 3-day low + 80% daily volatility range as entry zone
  2. Use RMI and MA Cross to confirm trend
  3. Set dynamic stop-loss instead of fixed stop-loss
  4. Don't add positions blindly, wait for trend confirmation

XI. Summary​

Stinkfist is a complex deep-digging strategy that finds trading opportunities through multi-layered technical analysis and protection mechanisms. Its core value lies in:

  1. Informative Layer Assisted Decision: Uses 1-hour timeframe for higher-dimensional trend judgment
  2. Dynamic Risk Management: Adjusts strategy parameters based on holding time and profit state
  3. Global Position Perspective: Considers comprehensive performance of all positions rather than isolated judgment

For quantitative traders, this is a strategy suitable for volatile markets, but requires full understanding of its position addition logic and price protection mechanisms before live deployment. Recommend starting with single pair testing and gradually expanding to multi-pair scenarios.

XII. Sub-strategy Variants​

Stinkfist_BTC (BTC-specific Version)​

buy_params = {
    'inf-pct-adr': 0.91556,
    'mp': 66,
}
use_sell_signal = False  # Disable sell signals, rely on ROI exit

Characteristics: Higher entry threshold, disabled sell signals, more suitable for BTC's volatility characteristics.

Stinkfist_ETH (ETH-specific Version)​

buy_params = {
    'inf-pct-adr': 0.81628,
    'mp': 40,
}
trailing_stop = True
trailing_stop_positive = 0.014
trailing_stop_positive_offset = 0.022
trailing_only_offset_is_reached = False
use_sell_signal = False

Characteristics: Enables trailing stop, parameters optimized for ETH market conditions.