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Schism2MM Strategy Deep Dive

Strategy Number: #377 (377th of 465 strategies)
Strategy Type: Multi-condition Rebound Trading + Dynamic Position Addition + Smart Stop-loss System
Timeframe: 5 minutes (5m) + 1 hour (1h information layer)

1. Strategy Overview​

Schism2MM is a complex trend rebound strategy based on Relative Momentum Index (RMI) and Local Extrema Detection. Its core feature is using the scipy signal processing library to identify local price troughs, combined with multi-dimensional indicator filtering for precise entry during trend pullbacks. The strategy supports dynamic position addition and is equipped with a linear growth stop-loss threshold system based on holding time.

Core Features​

FeatureDescription
Buy Conditions2 independent buy logic sets (new position + add position), with local extrema detection
Sell ConditionsDynamic stop-loss + trend reversal signal + portfolio profit management
Protection Mechanisms4-layer protection (stop-loss, order timeout, entry confirmation, price protection)
TimeframeMain: 5m + Information: 1h
Dependenciesnumpy, talib, qtpylib, arrow, scipy.signal, cachetools, technical

2. Strategy Configuration Analysis​

2.1 Basic Risk Parameters​

# ROI exit table
minimal_roi = {
    "0": 0.025,    # Take 2.5% profit immediately
    "10": 0.015,   # After 10 minutes: 1.5%
    "20": 0.01,    # After 20 minutes: 1%
    "30": 0.005,   # After 30 minutes: 0.5%
    "120": 0       # After 120 minutes: no profit requirement
}

# Stop-loss setting
stoploss = -0.10  # 10% fixed stop-loss

Design Rationale:

  • Uses decreasing ROI table - longer holding means lower profit requirements
  • After 120 minutes, profit requirement is abandoned, relying entirely on sell signals
  • Combined with ignore_roi_if_buy_signal = True, ROI is not triggered when buy signal is valid

2.2 Order Type Configuration​

use_sell_signal = False      # Don't use standard sell signals
sell_profit_only = True      # Only sell when profitable
ignore_roi_if_buy_signal = True  # Ignore ROI when buy signal is valid

2.3 Buy Parameters​

buy_params = {
    'inf-pct-adr': 0.95,    # Price range: 3-day low + 95%*average daily range
    'inf-rsi': 65,          # 1-hour RSI threshold
    'mp': 53,               # Momentum ping-pong indicator threshold
    'rmi-fast': 41,         # Fast RMI threshold
    'rmi-slow': 33          # Slow RMI threshold
}

3. Buy Conditions Detailed Analysis​

3.1 Core Indicator System​

The strategy uses the following technical indicators:

IndicatorPeriodPurpose
RMI-Slow21 period, momentum 5Trend direction determination
RMI-Fast8 period, momentum 4Fast momentum capture
ROC6 periodRate of change calculation
MP6-period RSI of ROCMomentum ping-pong indicator
RSI (1h)14 periodInformation layer trend confirmation
ADR1-day high - 3-day lowAverage daily range

3.2 Local Extrema Detection​

The strategy uses scipy.signal.argrelextrema to detect local price troughs:

min_peaks = argrelextrema(dataframe['close'].values, np.less, order=100)
for mp in min_peaks[0]:
    dataframe.at[mp, 'buy_signal'] = True

Explanation:

  • order=100 means finding local minima within 100 candles
  • Detected trough positions are marked as buy_signal = True
  • This is the core entry timing identification mechanism

3.3 Two Buy Logic Sets​

Scenario 1: New Position (No Existing Position)​

# Condition combination
(1h_rsi >= 65) &                           # Information layer trend confirmation
(close <= 3d_low + 0.95*adr) &             # Price in low region
(rmi-dn-trend == 1) &                      # RMI downtrend
(rmi-slow >= 33) &                         # RMI not oversold
(rmi-fast <= 41) &                         # Fast RMI limited
(mp <= 53) &                               # Momentum ping-pong not high
(buy_signal == True) &                     # Local trough detected
(volume > 0)                               # Has volume

Logic Interpretation:

  • Under the premise of large timeframe uptrend (1h RSI >= 65)
  • Waiting for price pullback to low region
  • RMI downtrend confirms pullback
  • Local extrema detection confirms trough position

Scenario 2: Add Position (Existing Position)​

# Dynamic threshold calculation
rmi_grow = linear_growth(30, 70, 180, 720, open_minutes)  # 30-70 linear growth
profit_factor = 1 - (rmi-slow / 300)  # Profit factor

# Condition combination
(rmi-up-trend == 1) &                      # RMI uptrend
(current_profit > peak_profit * factor) &  # Profit protection
(rmi-slow >= rmi_grow) &                   # RMI linear growth threshold
(buy_signal == True) &                     # Local trough
(volume > 0)

Add Position Features:

  • After holding more than 180 minutes, RMI threshold grows linearly from 30 to 70
  • Profit protection: current profit > peak profit × factor
  • Only add position in uptrend, avoid trading against trend

4. Sell Logic Detailed Analysis​

4.1 Dynamic Stop-loss System​

The strategy uses time-weighted dynamic stop-loss:

loss_cutoff = linear_growth(-0.03, 0, 0, 300, open_minutes)
# Holding 0 minutes: allow 3% loss
# Holding 300 minutes: require profit

4.2 Sell Trigger Conditions​

# Basic conditions
(current_profit < loss_cutoff) &           # Profit below dynamic threshold
(current_profit > stoploss) &              # Above fixed stop-loss
(rmi-dn-trend == 1) &                      # RMI downtrend
(volume > 0)

# Exit threshold after profit
if peak_profit > 0:
    crossed_below(rmi-slow, 50)            # Was profitable: RMI falls below 50
else:
    crossed_below(rmi-slow, 10)            # Never profitable: RMI falls below 10

4.3 Portfolio Profit Management​

When other positions exist, the strategy considers overall portfolio:

if free_slots > 0:
    # When having free slots
    hold_pct = (free_slots / 100) * -1
    avg_other_profit >= hold_pct           # Other position profit threshold
else:
    # When no free slots
    biggest_loser == True                   # Only allow biggest loser to sell

5. Technical Indicator System​

5.1 Core Indicators​

Indicator CategorySpecific IndicatorPurpose
MomentumRMI(21,5), RMI(8,4)Trend direction, overbought/oversold
Rate of ChangeROC(6), MPMomentum strength measurement
TrendRMI trend countTrend confirmation
Price1d high, 3d low, ADRPrice position assessment

5.2 Information Timeframe Indicators (1h)​

The strategy uses 1h as information layer:

  • RSI(14): Confirm large timeframe trend
  • 1d_high: 24-hour high
  • 3d_low: 72-hour low
  • ADR: Average daily range

5.3 Local Extrema Detection​

# Using scipy signal processing
from scipy.signal import argrelextrema
min_peaks = argrelextrema(close_values, np.less, order=100)

6. Risk Management Features​

6.1 Multi-layer Stop-loss Protection​

Stop-loss TypeTrigger ConditionDescription
Fixed Stop-lossLoss >= 10%Hard exit
Dynamic Stop-lossProfit < time thresholdSoft exit
RMI Stop-lossRMI falls below thresholdTrend reversal
Portfolio Stop-lossOverall position considerationFund management

6.2 Order Timeout Protection​

def check_buy_timeout(self, pair, trade, order, **kwargs):
    # Buy order timeout: cancel when current price > order price * 1.01
    if current_price > order['price'] * 1.01:
        return True
    return False

def check_sell_timeout(self, pair, trade, order, **kwargs):
    # Sell order timeout: cancel when current price < order price * 0.99
    if current_price < order['price'] * 0.99:
        return True
    return False

6.3 Entry Price Protection​

def confirm_trade_entry(self, pair, order_type, amount, rate, **kwargs):
    # Entry confirmation: reject when price deviation > 1%
    if current_price > rate * 1.01:
        return False
    return True

7. Strategy Advantages and Limitations​

✅ Advantages​

  1. Local Extrema Detection: Uses scipy signal processing to precisely identify trough positions
  2. Dynamic Position Addition: Smart add-position logic based on holding time and profit
  3. Linear Growth Threshold: Stop-loss and add-position thresholds adjust linearly over time
  4. Portfolio Profit Management: Sell decisions considering overall positions

⚠️ Limitations​

  1. High Complexity: Involves scipy signal processing, linear growth functions, multi-dimensional conditions
  2. Parameter Sensitivity: order=100 extrema detection is sensitive to market structure
  3. Backtest Limitations: Dynamic position addition and portfolio management may not be fully simulated in backtesting
  4. Live Trading Dependency: Trade object queries only work in actual runtime

8. Suitable Scenario Recommendations​

Market EnvironmentRecommended ConfigurationDescription
Oscillating UptrendDefault parametersStrategy's design target scenario
Strong TrendAdjust inf-rsiRaise trend confirmation threshold
High VolatilityAdjust inf-pct-adrExpand price range
Low VolatilityLower stop-lossstoploss = -0.05

9. Suitable Market Environment Details​

Schism2MM is a rebound trading strategy. Based on its code architecture and community long-term live trading verification, it is most suitable for oscillating uptrend markets and performs poorly in one-sided downtrends.

9.1 Strategy Core Logic​

  • Local Extrema Detection: Find price troughs within 100 candles
  • Trend Filter: Require 1h RSI >= 65, ensuring large timeframe uptrend
  • Pullback Entry: Enter when price pulls back to low region
  • Dynamic Position Addition: Can add position after holding more than 3 hours

9.2 Performance in Different Market Environments​

Market TypePerformance RatingAnalysis
📈 Oscillating Uptrend⭐⭐⭐⭐⭐Perfect match with strategy design, pullback buy + trend hold
🔄 Sideways Oscillation⭐⭐⭐☆☆May trigger frequent stop-losses, local extrema detection may fail
📉 One-sided Downtrend⭐⭐☆☆☆Trend filter may not respond in time, frequent stop-loss triggers
⚡ High Volatility⭐⭐☆☆☆Extrema detection may be unstable, significant slippage impact

9.3 Key Configuration Recommendations​

Configuration ItemRecommended ValueDescription
order (extrema)50-150Adjust detection range based on market volatility
inf-rsi60-70Trend confirmation threshold
stoploss-0.08 ~ -0.12Adjust based on risk tolerance

10. Important Note: The Cost of Complexity​

10.1 Learning Cost​

Schism2MM involves multiple advanced concepts:

  • scipy signal processing (argrelextrema)
  • Linear growth functions
  • Portfolio profit management
  • Dynamic stop-loss system

Recommend fully understanding each module before live deployment.

10.2 Hardware Requirements​

Number of PairsMinimum MemoryRecommended Memory
1-102GB4GB
10-304GB8GB
30+8GB16GB

10.3 Backtest vs Live Trading Differences​

Backtest Limitations:

  • Trade.get_trades() not available in backtest
  • Portfolio profit management fails in backtest
  • Dynamic position addition may not be fully simulated

Recommendation: Use dry-run mode for thorough testing before live trading.

10.4 Manual Trading Suggestions​

For manual trading:

  1. Focus on pairs with 1h RSI >= 65
  2. Use TradingView to mark local extrema points
  3. Confirm entry with RMI indicator
  4. Set 10% stop-loss and dynamic take-profit

11. Summary​

Schism2MM is a technology-intensive rebound trading strategy. Its core value lies in:

  1. Scientific Extrema Detection: Uses scipy signal processing to precisely locate troughs
  2. Smart Position Addition: Dynamic add-position logic based on time and profit
  3. Multi-layer Risk Management: Fixed stop-loss + dynamic stop-loss + portfolio management

For quantitative traders, this is a medium-complexity strategy suitable for oscillating uptrend markets, requiring certain Python and signal processing knowledge to fully understand and optimize.