SuperHV27 Strategy Deep Dive

Strategy Number: #399 (399th of 465 strategies)
Strategy Type: Multi-indicator trend following + Dynamic ROI + Position management
Timeframe: 5 minutes (5m)

1. Strategy Overview

SuperHV27 is a multi-indicator trend following strategy improved from the BinHV27 series, incorporating ADX, RSI, EMA, SMA and other technical indicators, with dynamic ROI mechanisms and position management logic. The core design philosophy is to capture trend reversal points through complex conditional combinations while optimizing exit timing using dynamic ROI and profit protection mechanisms.

Core Features

Feature	Description
Buy Conditions	Multiple complex buy signal combinations, supporting both new position opening and position addition modes
Sell Conditions	Multi-scenario sell signals + profit protection + position coordination logic
Protection Mechanisms	Dynamic ROI (three decay types) + trade timeout checks + entry price protection
Timeframe	5-minute primary timeframe
Dependencies	talib, qtpylib, arrow, technical (RMI), cachetools

2. Strategy Configuration Analysis

2.1 Basic Risk Parameters

# ROI exit table
minimal_roi = {
    "0": 0.10,      # Immediate requirement: 10% profit
    "30": 0.05,     # After 30 minutes: 5%
    "40": 0.025,    # After 40 minutes: 2.5%
    "60": 0.015,    # After 60 minutes: 1.5%
    "720": 0.01,    # After 12 hours: 1%
    "1440": 0       # After 24 hours: allow 0% profit exit
}

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

# Trailing stop (standard trailing stop not enabled)
trailing_stop = False

Design Rationale:

ROI table uses stepped decrease, from 10% gradually down to 0%
Stop loss value -0.40 is relatively loose, working with dynamic ROI for more refined exit control
Long-term positions allow zero-profit exit to avoid being locked in losses

2.2 Dynamic ROI Mechanism

dynamic_roi = {
    'enabled': True,
    'type': 'connect',      # Connect-type decay
    'decay-rate': 0.015,
    'decay-time': 1440,
    'start': 0.10,          # Starting 10%
    'end': 0,               # Ending 0%
}

Three Decay Types:

linear: Linear decay f(t) = start - (rate * t)
exponential: Exponential decay f(t) = start * e^(-rate*t)
connect: Linear interpolation connecting points in the ROI table

2.3 Order Type Configuration

use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = True

Design Logic:

Enable sell signals to increase active exit opportunities
Only sell when profitable to lock in profits
Ignore ROI when buy signal still exists to extend holding period

3. Buy Conditions Detailed Analysis

3.1 Buy Parameter Groups

buy_params = {
    'adx1': 49,     # ADX threshold during large drops
    'adx2': 36,     # ADX threshold during sustained uptrend
    'adx3': 32,     # ADX threshold during large non-sustained rise
    'adx4': 24,     # ADX threshold during sustained large rise
    'emarsi1': 43,  # EMA-RSI threshold during large drops
    'emarsi2': 27,  # EMA-RSI threshold during sustained uptrend
    'emarsi3': 26,  # EMA-RSI threshold during large non-sustained rise
    'emarsi4': 50   # EMA-RSI threshold during sustained large rise
}

3.2 Buy Condition Categories

The strategy supports two buy modes: New Position Opening and Position Addition.

Mode #1: New Position Opening (when no active trades)

Base Conditions:

# Common conditions that must be met
dataframe['slowsma'].gt(0) &                      # SMA valid
dataframe['close'].lt(dataframe['highsma']) &     # Price below high SMA
dataframe['close'].lt(dataframe['lowsma']) &      # Price below low SMA
dataframe['minusdi'].gt(dataframe['minusdiema']) & # Negative DI above its EMA
dataframe['rsi'].ge(dataframe['rsi'].shift())     # RSI rising

Branch Conditions (Four Groups):

Condition Group	Core Logic	ADX Threshold	EMA-RSI Threshold
Large Drop Trend	Not preparing to change trend + Not sustained rise + bigdown	49	≤43
Sustained Rise Drop	Not preparing to change trend + Sustained rise + bigdown	36	≤27
Large Rise Not Sustained	Not preparing to change trend + Not sustained rise + bigup	32	≤26
Sustained Large Rise	Sustained rise + bigup	24	≤50

# Position addition conditions
conditions.append(dataframe['rmi-up-trend'] == 1)                   # RMI uptrend
conditions.append(trade_data['current_profit'] > profit_factor)     # Current profit above threshold
conditions.append(dataframe['rmi-slow'] >= rmi_grow)               # RMI reached growth target

Position Addition Logic:

Use RMI (Relative Momentum Index) to judge momentum
Profit factor dynamically adjusts with RMI value
Linear growth threshold controls addition timing

4. Sell Logic Detailed Analysis

4.1 Multi-Layer Profit Taking System

The strategy uses a multi-scenario combined sell mechanism:

Scenario Type    Core Condition                 Signal Name
─────────────────────────────────────────────────────────
Price Breakout   Price breaks SMA low or high   Price breakout exit
EMA-RSI         EMA-RSI too high or price too high  Momentum overheating exit
Trend Reversal  Trend reversal confirmed + deceleration  Trend reversal exit
DI Reversal     Negative DI below positive DI  Direction reversal exit

4.2 Sell Parameter Groups

sell_params = {
    'adx2': 36,
    'emarsi1': 43,
    'emarsi2': 27,
    'emarsi3': 26
}

4.3 Five Sell Scenarios

Scenario	Trigger Condition	Core Logic
Scenario 1	Price breaks SMA + bigdown	Price rebounds but trend still down
Scenario 2	Price breaks high SMA + EMA-RSI high	Price overheating exit
Scenario 3	Price breakout + strong ADX + high EMA-RSI + bigup	Uptrend momentum overheating
Scenario 4	Trend reversal confirmed + deceleration + high EMA-RSI	Trend about to reverse
Scenario 5	Trend reversal + DI reversal + price breakout	Clear direction reversal

4.4 Position Coordination Sell

# Trade coordination logic
if trade_data['other_trades']:
    if trade_data['free_slots'] > 0:
        # Free slots available, allow holding
        hold_pct = (trade_data['free_slots'] / 100) * -1
        conditions.append(trade_data['avg_other_profit'] >= hold_pct)
    else:
        # No free slots, sell biggest losing position
        conditions.append(trade_data['biggest_loser'] == True)

5. Technical Indicator System

5.1 Core Indicators

Indicator Category	Specific Indicators	Usage
Trend Indicators	EMA(60, 120), SMA(120, 240)	Judge overall trend direction
Momentum Indicators	RSI(5), EMA-RSI(5), RMI(21, 8)	Judge buy/sell momentum
Direction Indicators	ADX, PLUS_DI, MINUS_DI	Judge trend strength and direction
Trend Judgment	bigup/bigdown, continueup	Compound trend states

5.2 Trend State Indicators

# Major trend direction
dataframe['bigup'] = fastsma > slowsma & difference > close/300
dataframe['bigdown'] = ~bigup

# Trend change preparation
dataframe['preparechangetrend'] = trend > trend.shift()
dataframe['continueup'] = slowsma consecutive rise

# Trend deceleration
dataframe['delta'] = fastsma - fastsma.shift()
dataframe['slowingdown'] = delta < delta.shift()

6. Risk Management Features

6.1 Dynamic ROI Decay

The strategy implements three ROI decay modes, allowing refined control over exit timing:

Decay Type	Formula	Characteristics
linear	`start - (rate * t)`	Uniform decrease
exponential	`start * e^(-rate*t)`	Rapid decrease then gradual
connect	Linear interpolation between ROI table points	Flexible stepping

6.2 Trade Timeout Check

def check_buy_timeout(pair, trade, order, **kwargs):
    # Cancel buy when current price is 1% above order price
    if current_price > order['price'] * 1.01:
        return True

def check_sell_timeout(pair, trade, order, **kwargs):
    # Cancel sell when current price is 1% below order price
    if current_price < order['price'] * 0.99:
        return True

6.3 Entry Price Protection

def confirm_trade_entry(pair, order_type, amount, rate, time_in_force, **kwargs):
    # Reject entry when current price is 1% above expected entry price
    if current_price > rate * 1.01:
        return False
    return True

6.4 Position Coordination Mechanism

The strategy checks all active trades and coordinates sell decisions:

Has free slots: Continue holding if average profit is above hold threshold
No free slots: Prioritize selling the biggest losing position

7. Strategy Advantages and Limitations

✅ Advantages

Refined Dynamic ROI: Three decay modes adapt to different market rhythms
Multi-dimensional Trend Judgment: Integrates ADX, RSI, SMA, DI and other indicators
Smart Position Coordination: Cross-position coordinated selling optimizes capital utilization
Position Addition Mechanism: Add positions when profitable to amplify gains
Complete Entry Protection: Price protection and timeout checks prevent slippage losses

⚠️ Limitations

Complex Parameters: 8 buy parameters, 4 sell parameters, difficult to optimize
Loose Stop Loss: 40% stop loss may incur significant single-trade losses
Position Addition Risk: Adding positions amplifies risk exposure
Many Dependencies: Requires technical library and cachetools

8. Applicable Scenario Recommendations

Market Environment	Recommended Configuration	Notes
Oscillating Rebound	Default configuration	Capture rebound opportunities at trend reversal points
Slow Bull Trend	Enable position addition	Use position addition to amplify gains
Sharp Drop	Tighten stop loss	Prevent triggering wide stop loss
Sideways Consolidation	Disable strategy	Indicators may become ineffective

9. Applicable Market Environment Details

SuperHV27 is a trend reversal capture strategy. Based on its code architecture, it is most suitable for oscillating rebound markets, while performance may be limited in one-sided trend markets.

9.1 Strategy Core Logic

Reversal Capture: Judge trend changes through preparechangetrend and continueup
Low Price Entry: Require price below highsma and lowsma, capturing low position opportunities
Momentum Rise Confirmation: RSI rising, negative DI above EMA as confirmation conditions
Multi-branch Combination: Four branch conditions cover different trend state combinations

9.2 Performance in Different Market Environments

Market Type	Performance Rating	Reason Analysis
📈 Slow Bull Trend	⭐⭐⭐☆☆	Can capture pullback opportunities in trend, position addition effective
🔄 Oscillating Rebound	⭐⭐⭐⭐⭐	Designed for reversal capture, best performance
📉 Sharp Drop	⭐⭐☆☆☆	40% stop loss may incur large losses
⚡ One-sided Surge	⭐⭐☆☆☆	Price below SMA condition hard to satisfy
⚡ Sideways Consolidation	⭐☆☆☆☆	Trend indicators ineffective, few signals

9.3 Key Configuration Recommendations

Configuration Item	Recommended Value	Notes
stoploss	-0.25~-0.30	Tighten stop loss to prevent large losses
dynamic_roi type	'exponential'	Rapidly lower ROI requirements
max_open_trades	3-5	Coordinate with position addition mechanism
timeframe	5m	Default configuration, not recommended to modify

10. Important Note: The Cost of Complexity

10.1 Learning Cost

SuperHV27 involves multiple compound indicators and complex conditional combination logic:

Meaning and usage of ADX/PLUS_DI/MINUS_DI
Special calculation of RMI (Relative Momentum Index)
Differences between three dynamic ROI decay modes
Working principle of position coordination mechanism

Recommended reading time: 2-3 hours for deep understanding

10.2 Hardware Requirements

Number of Pairs	Minimum Memory	Recommended Memory
1-10 pairs	2GB	4GB
10-30 pairs	4GB	8GB
30+ pairs	8GB	16GB

Computational Burden:

Supertrend indicator loop calculation
RMI indicator calculation
Multi-condition combination judgment

10.3 Differences Between Backtesting and Live Trading

Backtesting Environment: Cannot simulate position coordination (assumes single position)
Live Trading Environment: Position addition and coordination mechanisms take effect
Recommendation: Test with single position first, then enable multi-position

10.4 Suggestions for Manual Traders

Manual traders can learn from:

Dynamic ROI concept: Lower profit targets over time
Position coordination logic: When capital is tight, prioritize exiting worst positions
Entry price protection: Reject unfavorable price entries

11. Summary

SuperHV27 is a complex and refined trend reversal capture strategy. Its core value lies in:

Dynamic ROI Flexibility: Three decay modes adapt to different market rhythms
Multi-dimensional Confirmation: Integrates trend, momentum, and direction indicators
Smart Position Management: Position addition and coordinated selling mechanisms
Complete Entry Protection: Price protection and timeout checks

For quantitative traders, this is an advanced strategy suitable for oscillating rebound markets, requiring careful parameter tuning and understanding of complex conditional combination logic. It is recommended to thoroughly understand the strategy mechanism before enabling the position addition feature.

Final Reminder: No matter how good the strategy is, the market will humble you without warning. Test with small positions, survival comes first! 🙏

1. Strategy Overview​

Core Features​

2. Strategy Configuration Analysis​

2.1 Basic Risk Parameters​

2.2 Dynamic ROI Mechanism​

2.3 Order Type Configuration​

3. Buy Conditions Detailed Analysis​

3.1 Buy Parameter Groups​

3.2 Buy Condition Categories​

Mode #1: New Position Opening (when no active trades)​

Mode #2: Position Addition (when active trades exist)​

4. Sell Logic Detailed Analysis​

4.1 Multi-Layer Profit Taking System​

4.2 Sell Parameter Groups​

4.3 Five Sell Scenarios​

4.4 Position Coordination Sell​

5. Technical Indicator System​

5.1 Core Indicators​

5.2 Trend State Indicators​

6. Risk Management Features​

6.1 Dynamic ROI Decay​

6.2 Trade Timeout Check​

6.3 Entry Price Protection​

6.4 Position Coordination Mechanism​

7. Strategy Advantages and Limitations​

✅ Advantages​

⚠️ Limitations​

8. Applicable Scenario Recommendations​

9. Applicable Market Environment Details​

9.1 Strategy Core Logic​

9.2 Performance in Different Market Environments​

9.3 Key Configuration Recommendations​

10. Important Note: The Cost of Complexity​

10.1 Learning Cost​

10.2 Hardware Requirements​

10.3 Differences Between Backtesting and Live Trading​

10.4 Suggestions for Manual Traders​

11. Summary​