Apollo11 Strategy In-Depth Analysis

Strategy ID: #420 (420th of 465 strategies)
Strategy Type: Multi-Signal Trend Following + Custom Stop Loss + Multi-Layer Protection
Timeframe: 15 minutes (15m)
Author: Shane Jones (https://twitter.com/shanejones)
Original Repository: https://github.com/shanejones/goddard

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1. Strategy Overview

Apollo11 is a complex multi-signal trend following strategy developed by Shane Jones with community contributor assistance. The strategy integrates multiple technical analysis methods including EMA trends, Bollinger Band breakouts, and Fibonacci retracements, equipped with a dynamic stop loss system and 5-layer protection mechanism, making it a "defensive offensive strategy" suitable for intermediate quantitative traders.

Core Features

Feature	Description
Entry Conditions	3 independent entry signals, can be independently enabled/disabled
Exit Conditions	No active exit signals, relies on ROI and custom stop loss
Protection Mechanisms	5 protection parameter groups (cooldown, max drawdown, stop loss protection, low profit protection ×2)
Timeframe	15 minutes (15m)
Startup Candles	480 candles (approximately 5 days of data)
Dependencies	talib, qtpylib, freqtrade.persistence.Trade

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2. Strategy Configuration Analysis

2.1 Basic Risk Parameters

# ROI exit table
minimal_roi = {
    "0": 0.10,   # After 0 minutes, exit at 10% profit
    "30": 0.05,  # After 30 minutes, exit at 5% profit
    "60": 0.02,  # After 60 minutes, exit at 2% profit
}

# Stop loss settings
stoploss = -0.16  # 16% fixed stop loss

Design Rationale:

• ROI table uses progressive take-profit, the longer the time, the lower the take-profit threshold
• Initial target of 10% is relatively high, suitable for catching larger movements
• After 60 minutes drops to 2%, ensuring at least partial profit is locked
• 16% fixed stop loss is relatively wide, giving trades enough room for fluctuation

2.2 Trailing Stop Configuration

trailing_stop = False  # Disable standard trailing stop
use_custom_stoploss = True  # Use custom stop loss
use_sell_signal = False  # Disable exit signals

Design Rationale:

• Disable standard trailing stop, use more flexible custom stop loss logic instead
• Custom stop loss can dynamically adjust based on holding time and profit
• Doesn't rely on technical indicator exits, focuses on entry timing

2.3 Custom Stop Loss Logic

def custom_stoploss(self, pair, trade, current_time, current_rate, current_profit, **kwargs):
    # Profit protection tiers
    if current_profit > 0.20:  # Profit > 20%
        return 0.04  # Stop loss moves up to 4% profit
    if current_profit > 0.10:  # Profit > 10%
        return 0.03  # Stop loss moves up to 3% profit
    if current_profit > 0.06:  # Profit > 6%
        return 0.02  # Stop loss moves up to 2% profit
    if current_profit > 0.03:  # Profit > 3%
        return 0.01  # Stop loss moves up to 1% profit
    
    # Loss stop loss relief (based on holding time)
    if current_profit <= -0.10:  # Loss > 10%
        if trade.open_date_utc + timedelta(hours=60) < current_time:
            return current_profit / 1.75  # Tighten stop loss after 60 hours
    
    if current_profit <= -0.08:  # Loss > 8%
        if trade.open_date_utc + timedelta(hours=120) < current_time:
            return current_profit / 1.70  # Further tighten after 120 hours
    
    return -1  # Use default stop loss

Stop Loss Logic Details:

Profit Range	Stop Loss Position	Protection Effect
> 20%	Lock in 4% profit	Strong protection
> 10%	Lock in 3% profit	Medium-strong protection
> 6%	Lock in 2% profit	Medium protection
> 3%	Lock in 1% profit	Light protection
-10% (after 60h)	Dynamic tightening	Time-limited stop loss
-8% (after 120h)	Dynamic tightening	Time-limited stop loss

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3. Entry Conditions Detailed

3.1 Signal Control Switches

buy_signal_1 = True  # EMA trend crossover signal
buy_signal_2 = True  # Bollinger Band + Fibonacci signal
buy_signal_3 = True  # Volume-weighted breakout signal

Each signal can be independently enabled or disabled for strategy optimization and backtesting analysis.

3.2 Entry Signal #1: EMA Trend Crossover + VWMACD

Indicator Configuration:

s1_ema_xs = 3    # Ultra-short period EMA
s1_ema_sm = 5    # Short period EMA
s1_ema_md = 10   # Medium period EMA
s1_ema_xl = 50   # Long period EMA
s1_ema_xxl = 240 # Ultra-long period EMA (trend baseline)

Trigger Conditions:

conditions = [
    dataframe["vwmacd"] < dataframe["signal"],           # VWMACD below signal line
    dataframe["low"] < dataframe["s1_ema_xxl"],           # Low below 240 EMA
    dataframe["close"] > dataframe["s1_ema_xxl"],         # Close above 240 EMA
    qtpylib.crossed_above(dataframe["s1_ema_sm"], dataframe["s1_ema_md"]),  # 5 EMA crosses above 10 EMA
    dataframe["s1_ema_xs"] < dataframe["s1_ema_xl"],      # 3 EMA below 50 EMA
    dataframe["volume"] > 0,                             # Volume exists
]

Logic Interpretation:

1. VWMACD Condition: Volume-weighted MACD below signal line, indicating momentum not yet overheated
2. Price Position: Low touches 240 EMA but close is above it, typical "false breakout then return"
3. EMA Crossover: 5 EMA crosses above 10 EMA, short-term trend strengthening signal
4. Relative Position: 3 EMA below 50 EMA, ensuring not chasing highs
5. Volume Confirmation: Must have volume as prerequisite

Signal Tag: buy_signal_1

3.3 Entry Signal #2: Bollinger Band Lower + Fibonacci Support

Indicator Configuration:

s2_ema_input = 50          # EMA period
s2_ema_offset_input = -1   # EMA offset
s2_bb_sma_length = 49      # Bollinger Band SMA period
s2_bb_std_dev_length = 64  # Standard deviation period
s2_bb_lower_offset = 3     # Lower band offset (3 standard deviations)
s2_fib_sma_len = 50        # Fibonacci SMA period
s2_fib_atr_len = 14        # ATR period
s2_fib_lower_value = 4.236 # Fibonacci extension coefficient

Trigger Conditions:

conditions = [
    qtpylib.crossed_above(dataframe["s2_fib_lower_band"], dataframe["s2_bb_lower_band"]),  # Fibonacci lower band crosses above Bollinger lower band
    dataframe["close"] < dataframe["s2_ema"],  # Close below offset EMA
    dataframe["volume"] > 0,                     # Volume exists
]

Logic Interpretation:

1. Extreme Oversold Detection: When Fibonacci extension lower band (4.236 × ATR) crosses above Bollinger lower band (3 standard deviations), indicates price is at extreme oversold
2. Price Position Confirmation: Close below offset EMA, ensuring buy timing is at low position
3. Volume Confirmation: Must have volume

Signal Tag: buy_signal_2

3.4 Entry Signal #3: Volume-Weighted Breakout

Indicator Configuration:

s3_ema_long = 50   # Long period EMA
s3_ema_short = 20  # Short period EMA
s3_ma_fast = 10    # Fast volume-weighted MA
s3_ma_slow = 20    # Slow volume-weighted MA

Trigger Conditions:

conditions = [
    dataframe["low"] < dataframe["s3_bb_lowerband"],   # Low below Bollinger lower band
    dataframe["high"] > dataframe["s3_slow_ma"],       # High above slow volume-price MA
    dataframe["high"] < dataframe["s3_ema_long"],      # High below long period EMA
    dataframe["volume"] > 0,                           # Volume exists
]

Logic Interpretation:

1. Bollinger Band Penetration: Price touches Bollinger lower band, indicating oversold
2. Volume-Price Breakout: High breaks above slow volume-weighted MA, indicating capital involvement
3. Trend Confirmation: High still below 50 EMA, avoiding high-position chasing
4. Volume Confirmation: Must have volume

Signal Tag: buy_signal_3

3.5 Entry Condition Classification Summary

Condition Group	Condition ID	Core Logic	Signal Tag
Trend Following	#1	EMA crossover + VWMACD + 240 EMA support	buy_signal_1
Extreme Oversold	#2	Bollinger Band + Fibonacci extension double confirmation	buy_signal_2
Volume-Price Breakout	#3	Bollinger Band + Volume-weighted MA combination	buy_signal_3

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4. Exit Logic Detailed

4.1 No Active Exit Signals

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[(), "sell"] = 0
    return dataframe

The strategy doesn't use technical indicator-driven exit signals, relying entirely on ROI table and custom stop loss mechanisms to exit.

4.2 Tiered ROI Take-Profit

Time (minutes)    Minimum Profit Threshold    Signal Name
───────────────────────────────────────────────────────────
0                 10%                         ROI_0
30                5%                          ROI_30
60                2%                          ROI_60

Take-Profit Logic:

• Immediately after opening requires 10% profit
• After 30 minutes lowers requirement to 5%
• After 60 minutes further lowers to 2%
• After 60 minutes, completely relies on stop loss

4.3 Custom Stop Loss Dynamic Adjustment

Stage	Profit Range	Stop Loss Position	Description
Profit Protection 1	> 20%	Lock in 4% profit	High return protection
Profit Protection 2	> 10%	Lock in 3% profit	Medium-high return protection
Profit Protection 3	> 6%	Lock in 2% profit	Medium return protection
Profit Protection 4	> 3%	Lock in 1% profit	Light protection
Time Stop Loss 1	< -10%, holding > 60h	Dynamic tightening	Long-term loss handling
Time Stop Loss 2	< -8%, holding > 120h	Dynamic tightening	Very long-term loss handling

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5. Technical Indicator System

5.1 Core Indicators

Indicator Category	Specific Indicators	Usage
EMA Series	3, 5, 10, 20, 50, 200, 240 EMA	Trend judgment, support/resistance
Bollinger Bands	20 period, 3 standard deviations	Oversold detection, volatility range
ATR	14 period	Volatility measurement
Volume-Weighted MA	10, 20 period VWMA	Volume-price relationship analysis
VWMACD	12, 26, 9 parameters	Volume-weighted MACD
Fibonacci Extension	4.236 × ATR	Extreme support level

5.2 Indicator Calculation Details

EMA Calculation:

dataframe["s1_ema_xs"] = ta.EMA(dataframe, timeperiod=3)   # Ultra-short
dataframe["s1_ema_sm"] = ta.EMA(dataframe, timeperiod=5)   # Short
dataframe["s1_ema_md"] = ta.EMA(dataframe, timeperiod=10)  # Medium
dataframe["s1_ema_xl"] = ta.EMA(dataframe, timeperiod=50)  # Long
dataframe["s1_ema_xxl"] = ta.EMA(dataframe, timeperiod=240) # Ultra-long

Bollinger Band Calculation:

s3_bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
dataframe["s3_bb_lowerband"] = s3_bollinger["lower"]

Volume-Weighted MACD Calculation:

dataframe["fastMA"] = ta.EMA(dataframe["volume"] * dataframe["close"], 12) / ta.EMA(dataframe["volume"], 12)
dataframe["slowMA"] = ta.EMA(dataframe["volume"] * dataframe["close"], 26) / ta.EMA(dataframe["volume"], 26)
dataframe["vwmacd"] = dataframe["fastMA"] - dataframe["slowMA"]
dataframe["signal"] = ta.EMA(dataframe["vwmacd"], 9)

Fibonacci Extension Lower Band:

s2_fib_atr_value = ta.ATR(dataframe, timeframe=14)
s2_fib_sma_value = ta.SMA(dataframe, timeperiod=50)
dataframe["s2_fib_lower_band"] = s2_fib_sma_value - s2_fib_atr_value * 4.236

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6. Risk Management Features

6.1 Multi-Layer Protection Mechanism

@property
def protections(self):
    return [
        # Protection 1: Cooldown Period
        {
            "method": "CooldownPeriod",
            "stop_duration": to_minutes(minutes=0),  # 0 minute cooldown
        },
        # Protection 2: Maximum Drawdown Protection
        {
            "method": "MaxDrawdown",
            "lookback_period": to_minutes(hours=12),  # 12 hour lookback
            "trade_limit": 20,                        # Minimum 20 trades
            "stop_duration": to_minutes(hours=1),     # Stop for 1 hour
            "max_allowed_drawdown": 0.2,              # Maximum 20% drawdown
        },
        # Protection 3: Consecutive Stop Loss Protection
        {
            "method": "StoplossGuard",
            "lookback_period": to_minutes(hours=6),   # 6 hour lookback
            "trade_limit": 4,                         # Minimum 4 trades
            "stop_duration": to_minutes(minutes=30),  # Stop for 30 minutes
            "only_per_pair": False,                   # All pairs
        },
        # Protection 4: Low Profit Protection (Short Period)
        {
            "method": "LowProfitPairs",
            "lookback_period": to_minutes(hours=1, minutes=30),  # 1.5 hour lookback
            "trade_limit": 2,                         # Minimum 2 trades
            "stop_duration": to_minutes(hours=15),    # Stop for 15 minutes
            "required_profit": 0.02,                 # 2% minimum profit
        },
        # Protection 5: Low Profit Protection (Long Period)
        {
            "method": "LowProfitPairs",
            "lookback_period": to_minutes(hours=6),   # 6 hour lookback
            "trade_limit": 4,                         # Minimum 4 trades
            "stop_duration": to_minutes(minutes=30),  # Stop for 30 minutes
            "required_profit": 0.01,                  # 1% minimum profit
        },
    ]

6.2 Protection Mechanism Details

Protection Type	Trigger Condition	Stop Duration	Description
Cooldown Period	After each sell	0 minutes	Can trade again immediately
Maximum Drawdown	20 trades within 12h with >20% drawdown	1 hour	Overall risk control
Stop Loss Protection	≥4 stop losses within 6h	30 minutes	Prevent consecutive losses
Low Profit Protection (Short)	≥2 trades with <2% profit within 1.5h	15 minutes	Short-term return filter
Low Profit Protection (Long)	≥4 trades with <1% profit within 6h	30 minutes	Long-term return filter

6.3 Custom Stop Loss Smart Protection

The strategy's custom stop loss implements a dynamic mechanism of "the more profit, the stronger the protection":

• Tiered Protection: For each profit level reached, stop loss moves up one level
• Time Stop Loss: When holding too long with serious loss, gradually tighten stop loss
• Progressive Exit: Avoid "either double or zero" extreme situations

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7. Strategy Advantages and Limitations

✅ Advantages

1. Multi-Signal Fusion: 3 independent entry signals covering trend following, extreme oversold, and volume-price breakout scenarios
2. Dynamic Stop Loss: 5-tier profit protection + time stop loss, smart and flexible
3. Multi-Layer Protection: 5 protection groups, controlling risk from multiple dimensions
4. Volume Weighting: Uses VWMACD and volume-weighted MA, effectively filtering false signals
5. Configurable Parameters: All key parameters are configurable for optimization

⚠️ Limitations

1. No Active Exit: Relies on ROI and stop loss, may miss optimal exit points
2. Large Startup Data Requirement: Needs 480 candles (about 5 days) of warmup data
3. Many Parameters: Difficult to optimize, overfitting risk exists
4. Long Only: No short logic, not suitable for bear markets

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8. Applicable Scenario Recommendations

Market Environment	Recommended Configuration	Description
Moderate Uptrend	Enable all 3 signals	Utilize trend and oversold signals to catch opportunities
Oscillating Market	Only enable signal 2	Focus on extreme oversold rebounds
High Volatility	Signals 1+3	Combine trend and volume-price breakout
Downtrend	Use cautiously	May reduce max_open_trades

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9. Applicable Market Environment Details

Apollo11 is a defensive offensive strategy. Through multi-layer protection mechanisms and dynamic stop loss, it strictly controls risk while pursuing returns, most suitable for moderate trend or oscillating-to-upside market environments.

9.1 Strategy Core Logic

• Trend Following Primary: Signal 1 and Signal 3 both rely on EMA trends
• Oversold Rebound Secondary: Signal 2 catches extreme oversold opportunities
• Defense First: 5-layer protection ensures risk is controllable
• Profit Protection: Dynamic stop loss locks in profits

9.2 Performance in Different Market Environments

Market Type	Performance Rating	Reason Analysis
📈 Moderate Uptrend	⭐⭐⭐⭐⭐	Trend following signals perform best, take-profit mechanism effective
🔄 Oscillating Market	⭐⭐⭐⭐☆	Oversold signal catches rebounds, protection mechanisms reduce losses
📉 Downtrend	⭐⭐☆☆☆	Frequent stop loss triggers, recommend reducing position
⚡️ High Volatility	⭐⭐⭐☆☆	May misjudge trends, need parameter adjustment

9.3 Key Configuration Recommendations

Configuration Item	Recommended Value	Description
max_open_trades	3-5	Moderate position count
stake_amount	2-5%	Single position control
startup_candle_count	480+	Ensure sufficient warmup data

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10. Important Reminder: The Price of Complexity

10.1 Learning Cost

This strategy involves multiple technical indicators and complex condition combinations:

• Need to understand EMA trend systems
• Need to understand Bollinger Bands and Fibonacci extensions
• Need to understand volume-weighted indicators
• Need to understand custom stop loss logic

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

10.3 Backtesting vs Live Trading Differences

Due to the strategy using volume-weighted indicators and custom stop loss:

• Stop loss logic in backtesting may differ from live trading
• Volume data may differ across exchanges
• Recommend testing in simulation environment first

10.4 Advice for Manual Traders

For manual traders, you can learn from the strategy:

• Dynamic Stop Loss: The idea of adjusting stop loss position based on profit and time
• Multi-Signal Confirmation: Don't rely on single indicator, verify from multiple dimensions
• Protection Mechanisms: Build your own risk management framework

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11. Summary

Apollo11 is a carefully designed multi-signal trend following strategy. Its core value lies in:

1. Signal Diversity: 3 independent signals covering different market states
2. Risk Control: 5-layer protection mechanism + dynamic stop loss forms complete risk control system
3. Configurability: All parameters open for optimization adjustment
4. Community Verified: From GitHub open-source project, tested by community

For quantitative traders, this is a strategy framework worth in-depth study and optimization. Recommend understanding each signal logic first, then making parameter adjustments, and finally small-position live verification.