Dracula Strategy: In-Depth Analysis

Strategy Number: #151 (151st of 465 strategies)
Strategy Type: Bollinger Band Support/Resistance + Money Flow Multi-Condition Strategy
Timeframe: 1 Minute (1m) + 5 Minute Informational Layer

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I. Strategy Overview

Dracula is a complex multi-condition buy strategy developed by author 6h057. The core logic is based on Bollinger Band support/resistance identification, Chaikin Money Flow (CMF) indicator, and EMA trend confirmation — buying when the price touches the lower Bollinger Band and money is flowing in.

Key Characteristics

Feature	Description
Buy Conditions	2 independent buy signal groups
Sell Conditions	5 custom exit signals (via custom_exit)
Protection	1 set of buy protection parameters (stoploss protection)
Timeframe	1 minute (main) + 5 minutes (informational layer)
Dependencies	TA-Lib, ta, numpy

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II. Strategy Configuration Analysis

2.1 Basic Risk Parameters

# ROI Exit Table (time: minutes)
minimal_roi = {
    "0": 0.10,     # Immediate exit: 10% profit
    "30": 0.05,    # After 30 minutes: 5% profit
    "60": 0.02     # After 60 minutes: 2% profit
}

# Stoploss Settings
stoploss = -0.20   # -20% hard stoploss

# Trailing Stop
trailing_stop = True
trailing_only_offset_is_reached = True
trailing_stop_positive = 0.01        # 1% profit triggers trailing
trailing_stop_positive_offset = 0.03  # 3% offset triggers exit

Design Philosophy:

• High initial ROI: The 10% initial ROI indicates the strategy expects to capture large swings
• Moderate stoploss: -20% hard stoploss allows moderate volatility room
• Aggressive trailing: Trailing activates at 1% profit, but only triggers at 3% offset — suitable for trend continuation

2.2 Order Type Configuration

# Using Freqtrade default configuration
order_types = {
    "entry": "limit",
    "exit": "limit",
    "stoploss": "market",
    "stoploss_on_exchange": False
}

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III. Entry Conditions Details

3.1 Protection Mechanism

The strategy has a built-in "stoploss protection" mechanism (lost_protect):

lost_protect = (dataframe["ema"] > (dataframe["close"] * 1.07)).rolling(10).sum() == 0

Protection Logic: Over the past 10 candles, EMA has never been above 107% of the close price, ensuring no chasing after a significant rally.

3.2 Buy Condition Groups

Condition #1: Bollinger Lower Band Breakout + Money Inflow + Support Confirmation

item_buy_logic = []
item_buy_logic.append(dataframe["volume"] > 0)                     # Has volume
item_buy_logic.append(dataframe["cmf"] > 0)                        # CMF > 0 (money inflow)
item_buy_logic.append(prev["bb_bbl_i"] == 1)                       # Previous candle touched lower band
item_buy_logic.append(prev["close"] >= prev1["support"])           # Price not below support
item_buy_logic.append(prev["ema"] < prev["close"])                 # EMA in downward trend
item_buy_logic.append((dataframe["open"] < dataframe["close"]))    # Current candle bullish
item_buy_logic.append(prev["open"] > prev["close"])                # Previous candle bearish (hammer pattern)
item_buy_logic.append((dataframe["bb_bbt"] > self.buy_bbt.value))  # Bollinger band width condition met
item_buy_logic.append(lost_protect)                                # Stoploss protection

Condition #2: Bollinger Lower Band Breakout + Direct Support

item_buy_logic = []
item_buy_logic.append(dataframe["volume"] > 0)
item_buy_logic.append(dataframe["cmf"] > 0)
item_buy_logic.append(dataframe["bb_bbl_i"] == 1)                  # Current candle touched lower band
item_buy_logic.append(dataframe["open"] >= prev1["support"])      # Open price above support
item_buy_logic.append(prev["ema"] < prev["close"])
item_buy_logic.append((dataframe["open"] < dataframe["close"]))
item_buy_logic.append((dataframe["bb_bbt"] > self.buy_bbt.value))
item_buy_logic.append(lost_protect)

3.3 Core Parameters

Parameter	Default	Description
buy_bbt	0.023	Bollinger band width threshold
ewo_high	5.638	EWO high threshold
ewo_low	-19.993	EWO low threshold
low_offset	0.	Buy price offset
rsi_buy	61	RSI buy threshold

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IV. Exit Conditions Details

4.1 Custom Exit Signals

The strategy implements complex exit logic via the custom_exit function:

Scenario	Trigger Condition	Signal Name
Resistance reversal	Previous candle broke upper band + current candle bearish + near resistance	sell_signal_1
Strong reversal	Current candle broke upper band + current candle bearish + far from resistance	sell_signal_2
Trend reversal	Current candle bearish + EMA above close by 107%	stop_loss
Lower Bollinger Band	Current candle bearish + near lower band + has profit	take_profit
SMA signal	Buy tag contains "sma" + profit >= 1%	sma
SMA stoploss	Buy tag contains "sma" + price above EMA49*high_offset	stop_loss_sma

4.2 ROI Exit Priority

Holding Time	Minimum Profit	Exit Triggered
0 minutes	10%	Exits immediately
30 minutes	5%	Exits if reached after 30 min
60 minutes	2%	Exits if reached after 60 min

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V. Technical Indicator System

5.1 Core Indicators

Category	Indicator	Parameters	Purpose
Trend	EMA	150 period	Long-term trend judgment
Volatility	Bollinger Bands	20 period, 2x std dev	Support/resistance
Money Flow	CMF (Chaikin Money Flow)	20 period	Money inflow/outflow
Momentum	RSI	14 period	Overbought/oversold auxiliary

5.2 Custom Indicators

The strategy contains two custom classes:

1. SupResFinder: Support/Resistance Identifier

   • isSupport(): Identifies support levels
   • isResistance(): Identifies resistance levels
   • getSupport(): Gets support level series
   • getResistance(): Gets resistance level series

2. EWO: Elliot Wave Oscillator (simplified)

   def EWO(dataframe, ema_length=5, ema2_length=35):
       ema1 = ta.EMA(df, timeperiod=ema_length)
       ema2 = ta.EMA(df, timeperiod=ema2_length)
       emadif = (ema1 - ema2) / df["close"] * 100
       return emadif

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VI. Risk Management Features

6.1 Trailing Stop Mechanism

trailing_stop = True
trailing_stop_positive = 0.01      # Activates after 1% profit
trailing_stop_positive_offset = 0.03  # Triggers on 3% pullback from high
trailing_only_offset_is_reached = True  # Only trails after activation point

How It Works:

1. After profit reaches 1%, trailing stop activates
2. Exit triggered when price pulls back 3% from the highest point
3. Suitable for capturing trending continuation moves

6.2 Hard Stoploss Protection

stoploss = -0.20  # -20%

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VII. Strategy Pros & Cons

✅ Pros

1. Multi-condition confirmation: Buy requires multiple conditions (CMF, support, trend, etc.), high signal quality
2. Money flow filtering: CMF > 0 ensures money inflow, reduces false breakouts
3. Support/resistance integration: SupResFinder dynamically identifies support and resistance
4. Complete protection: lost_protect prevents chasing after rallies
5. Custom exits: 5 different exit scenarios covering various situations

⚠️ Cons

1. Low signal frequency: Complex conditions mean fewer trading opportunities
2. Timeframe too short: 1-minute candles have high noise, prone to false signals
3. No trend filtering: No EMA200-style trend judgment, may trade against the trend
4. Many parameters: buy_bbt, ewo_high, ewo_low require careful optimization

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VIII. Applicable Scenarios

Market Environment	Recommended Config	Notes
High volatility coins	Default config	Bollinger band strategies suit high volatility
Trending markets	Enable trailing stop	Capture trend continuation
Sideways oscillation	Reduce trading pairs	Signal quality first
Low volatility markets	Pause usage	Bollinger bands narrow, no trading opportunities

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IX. Applicable Market Environments in Detail

Dracula is a mean reversion + money flow strategy based on support/resistance levels.

9.1 Core Strategy Logic

• Buy at Bollinger lower band: Buy when price touches lower band, extreme oversold
• Money flow confirmation: CMF > 0 confirms money inflow, improves signal quality
• Support level verification: Buy price not below dynamically calculated support
• Multi-scenario exits: Different exit signals triggered based on market conditions

9.2 Performance in Different Market Environments

Market Type	Rating	Analysis
📈 Slow bull / oscillating upward	★★★★☆ (Good)	Money flow confirmation catches upward trends
🔄 Wide-range oscillation	★★★★★ (Best)	Bollinger band support/resistance works best in ranges
📉 One-way crash	★★☆☆☆ (Poor)	Support levels may fail, continuously making new lows
⚡️ Extreme consolidation	★★★☆☆ (Fair)	Bollinger bands narrow, fewer signals

9.3 Key Configuration Recommendations

Config Item	Recommended Value	Notes
Number of trading pairs	20-40 USDT pairs	Few signals, need more pairs
Max open trades	3-5 orders	Strict conditions, don't over-allocate
Timeframe	1m (recommend changing to 5m)	1m noise too high
Stoploss	-20%	Keep default

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X. Important Notes: The Cost of Complexity

10.1 Learning Curve

The strategy contains approximately 300 lines of code with custom classes and methods, a steep learning curve. Recommended approach:

1. First understand the support/resistance calculation logic of SupResFinder
2. Understand the CMF money flow indicator principle
3. Gradually adjust parameters and observe signal changes

10.2 Hardware Requirements

The strategy has moderate computational load, not demanding on VPS:

Number of Pairs	Min RAM	Recommended RAM
20-40 pairs	512MB	1GB
40-80 pairs	1GB	2GB

10.3 Backtesting vs. Live Trading Differences

1-minute candle data may differ between backtesting and live trading. Recommendations:

• Run in dry-run mode for 2-4 weeks first
• Observe whether signal trigger frequency matches expectations
• Small-capital live trading for 1 month before scaling up

10.4 Manual Trading Recommendations

Manual traders can reference this strategy's buy logic:

• Price touches Bollinger lower band + CMF > 0 + near support = buy signal
• Combine with overall market trend judgment (BTC direction)
• Consider changing timeframe to 5m to reduce noise

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XI. Summary

Dracula is a complex multi-condition strategy whose core value lies in:

1. Money flow filtering: CMF ensures money inflow, improving signal quality
2. Dynamic support/resistance: SupResFinder calculates support/resistance in real time
3. Multi-scenario exits: custom_exit covers 5 different exit scenarios
4. Protection mechanism: lost_protect prevents chasing after rallies

For quantitative traders, this is an excellent advanced strategy template. Recommendations:

• First understand the principle of each indicator
• Consider changing timeframe from 1m to 5m to reduce noise
• Adjust buy_bbt parameter for coins with different volatility
• Consider adding trend filtering (e.g., EMA200 trend judgment)

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