Kamaflage Strategy Analysis

Strategy Number: #12 (12th of 465 strategies)
Strategy Type: Multi-Indicator Momentum Strategy (KAMA + RMI + MACD)
Timeframe: 5 minutes (5m)

I. Strategy Overview

Kamaflage is a complex multi-indicator momentum strategy that combines multiple technical indicators including KAMA (Kaufman Adaptive Moving Average), RMI (Relative Momentum Index), and MACD, and introduces an order book check mechanism to optimize order execution. The strategy name "Kamaflage" likely comes from the combination of "KAMA" and "Camouflage".

Core Features

Feature	Description
Entry Conditions	Multi-condition combination (KAMA + MACD + RMI + Volume)
Exit Conditions	RMI oversold + profit check + order book price
Protection	Trailing stop + Order timeout check
Timeframe	5 minutes
Dependencies	TA-Lib, technical
Special Features	Order book check, order timeout cancellation

II. Strategy Configuration Analysis

2.1 Base Risk Parameters

# ROI exit table (hyperopt results)
minimal_roi = {
    "0": 0.15,      # Immediate exit: 15% profit
    "10": 0.10,     # After 10 minutes: 10% profit
    "20": 0.05,     # After 20 minutes: 5% profit
    "30": 0.025,    # After 30 minutes: 2.5% profit
    "60": 0.01,     # After 60 minutes: 1% profit
}

# Stoploss setting
stoploss = -0.10  # -10% hard stoploss

# Trailing stop
trailing_stop = True
trailing_stop_positive = 0.01125       # 1.125% trailing activation
trailing_stop_positive_offset = 0.04673  # 4.673% offset trigger
trailing_only_offset_is_reached = True

Design Logic:

Multi-level ROI: 5-level decreasing ROI, longer holding time means lower exit threshold
Trailing stop: Activates 1.125% trailing after 4.67% profit, suitable for trending markets
Hyperparameter optimization: ROI and trailing parameters from hyperopt results

2.2 Order Type Configuration

Uses Freqtrade default configuration, but implements order timeout check functions.

III. Entry Conditions Details

3.1 Entry Logic (No Position)

# Entry conditions when no position
conditions = [
    dataframe["kama-3"] > dataframe["kama-21"],           # KAMA fast line > slow line
    dataframe["macd"] > dataframe["macdsignal"],          # MACD > signal line
    dataframe["macd"] > params["macd"],                   # MACD > threshold (0)
    dataframe["macdhist"] > params["macdhist"],           # MACD hist > threshold (0)
    dataframe["rmi"] > dataframe["rmi"].shift(),          # RMI rising
    dataframe["rmi"] > params["rmi"],                     # RMI > threshold (50)
    dataframe["volume"] < (dataframe["volume_ma"] * 20),  # Volume < avg volume × 20
]

Logic Analysis:

KAMA trend: 3-period KAMA > 21-period KAMA, confirms short-term trend upward
MACD golden cross: MACD line above signal line and greater than 0, momentum upward
MACD hist confirmation: MACD histogram greater than 0, momentum strengthening
RMI momentum: RMI indicator rising and above 50
Volume filter: Excludes abnormally high volume (possibly manipulation)

3.2 Entry Conditions (With Position)

# Add position conditions when already holding
conditions = [
    dataframe["close"] > dataframe["sar"],    # Price > SAR
    dataframe["rmi"] >= 75,                   # RMI >= 75
]

Note: When already holding a position, consider adding only when trend is strong.

3.3 Hyperparameters

# Buy hyperparameters (optimized results)
buy_params = {
    "macd": 0,
    "macdhist": 0,
    "rmi": 50,
}

IV. Exit Logic Explained

4.1 Exit Conditions

# Exit conditions when holding position
active_trade = Trade.get_trades([...]).all()
if active_trade:
    ob = self.dp.orderbook(metadata["pair"], 1)
    current_price = ob["asks"][0][0]
    current_profit = active_trade[0].calc_profit_ratio(rate=current_price)
    
    conditions = [
        (dataframe["buy"] == 0) &              # No buy signal
        (dataframe["rmi"] < 30),               # RMI < 30 (oversold)
        (current_profit > -0.03),              # Profit > -3%
        (dataframe["volume"] > 0),             # Volume > 0
    ]

Logic Analysis:

RMI oversold: RMI < 30 confirms short-term oversold
Profit check: Only sell if loss not exceeding 3% (avoid cutting at big losses)
Order book price: Uses real-time order book price to calculate profit
Buy signal disappeared: Consider exit when buy signal disappears

4.2 Order Timeout Check

# Buy order timeout check
def check_entry_timeout(self, pair, trade, order, **kwargs) -> bool:
    ob = self.dp.orderbook(pair, 1)
    current_price = ob["bids"][0][0]
    if current_price > order["price"] * 1.01:  # Price increased over 1%
        return True  # Cancel order
    return False

# Sell order timeout check
def check_exit_timeout(self, pair, trade, order, **kwargs) -> bool:
    ob = self.dp.orderbook(pair, 1)
    current_price = ob["asks"][0][0]
    if current_price < order["price"] * 0.99:  # Price decreased over 1%
        return True  # Cancel order
    return False

Purpose:

Prevent orders from not filling due to price movement
Cancel orders when price deviation exceeds 1%

V. Technical Indicator System

5.1 Core Indicators

Indicator Category	Specific Indicator	Parameters	Purpose
Trend	KAMA	3, 21 periods	Kaufman Adaptive Moving Average
Momentum	MACD	Default	Momentum strength and direction
Momentum	RMI	Default	Relative Momentum Index
Stoploss	SAR	Default	Trend-following stop
Volume	Volume MA	24 periods	Volume filter

5.2 Indicator Calculation

# KAMA (Kaufman Adaptive Moving Average)
dataframe["kama-3"] = ta.KAMA(dataframe, timeperiod=3)
dataframe["kama-21"] = ta.KAMA(dataframe, timeperiod=21)

# MACD
macd = ta.MACD(dataframe)
dataframe["macd"] = macd["macd"]
dataframe["macdsignal"] = macd["macdsignal"]
dataframe["macdhist"] = macd["macdhist"]

# RMI (Relative Momentum Index)
dataframe["rmi"] = RMI(dataframe)

# SAR
dataframe["sar"] = ta.SAR(dataframe)

# Volume Moving Average
dataframe["volume_ma"] = dataframe["volume"].rolling(window=24).mean()

VI. Risk Management Features

6.1 Trailing Stop

trailing_stop = True
trailing_stop_positive = 0.01125
trailing_stop_positive_offset = 0.04673
trailing_only_offset_is_reached = True

Working mechanism:

Trailing stop activates after profit reaches 4.673%
Triggers exit when pulling back 1.125% from highest point
Needs to reach offset before trailing activates

6.2 Order Timeout Check

Buy orders:

Cancel when current price > order price × 1.01

Sell orders:

Cancel when current price < order price × 0.99

6.3 Sell Profit Protection

current_profit > -0.03  # Loss not exceeding 3%

Purpose: Avoids forced selling at large losses.

VII. Strategy Pros & Cons

✅ Advantages

Multi-indicator combination: KAMA + MACD + RMI + SAR multi-dimensional confirmation
Order book check: Uses real-time order book to optimize order execution
Order timeout: Prevents orders from not filling due to price movement
Trailing stop: Locks profits, protects gains
Hyperparameter optimization: Key parameters from hyperopt

⚠️ Limitations

High complexity: Multiple indicators and conditions, difficult to debug
No trend filter: No EMA/SMA long-term trend judgment
No BTC correlation: Doesn't detect Bitcoin market trend
Order book dependency: Requires exchange to support orderbook API
Parameter sensitivity: Hyperopt results may overfit

VIII. Applicable Scenarios

Market Environment	Recommended Configuration	Note
Ranging market	Default configuration	Multi-indicator combination suitable for ranging
Uptrend	Default configuration	Trailing stop locks profits
Downtrend	Pause or light position	No trend filter, easy to lose
High volatility	Adjust parameters	May need to widen order timeout threshold
Low volatility	Adjust ROI	Lower ROI threshold for small moves

IX. Applicable Market Environments Explained

Kamaflage is a multi-indicator momentum strategy based on the core philosophy of "multi-condition confirmation".

9.1 Strategy Core Logic

Multi-indicator confirmation: KAMA + MACD + RMI confirm simultaneously before buying
Order book optimization: Uses real-time order book for price check and order cancellation
Trailing stop: Locks profits, lets profits run

9.2 Performance in Different Market Environments

Market Type	Performance Rating	Reason Analysis
📈 Slow bull/ranging up	★★★★☆	Multi-indicator confirmation + trailing stop performs well
🔄 Wide ranging	★★★★☆	Multi-indicator combination suitable for ranging
📉 Single-sided crash	★★☆☆☆	No trend filter, may lose consecutively
⚡️ Extreme sideways	★★★☆☆	Too little volatility, signals reduce

9.3 Key Configuration Recommendations

Configuration	Recommended Value	Note
Number of pairs	20-40	Moderate signal frequency
Max positions	3-6	Control risk
Position mode	Fixed position	Recommended fixed position
Timeframe	5m	Mandatory requirement

X. Important Note: Order Book Dependency

10.1 Moderate Learning Curve

Strategy code is about 150 lines, requires understanding multiple indicators and order book mechanism.

10.2 Moderate Hardware Requirements

Multi-indicator computation increases computation:

Number of Pairs	Minimum RAM	Recommended RAM
20-40 pairs	1GB	2GB
40-80 pairs	2GB	4GB

10.3 Order Book Dependency

Strategy uses self.dp.orderbook() to get real-time order book:

Requires exchange to support orderbook API
Order book data may have latency in live trading
Order book data may be inaccurate in backtesting

10.4 Manual Trader Recommendations

Manual traders can reference this strategy's multi-indicator approach:

Observe KAMA, MACD, RMI multiple indicators simultaneously
Use trailing stop to protect profits
Set order timeout cancellation mechanism

XI. Summary

Kamaflage is a well-designed multi-indicator momentum strategy. Its core value lies in:

Multi-indicator combination: KAMA + MACD + RMI + SAR multi-dimensional confirmation
Order book optimization: Uses real-time order book for price check and order cancellation
Trailing stop: Locks profits, protects gains
Hyperparameter optimization: Key parameters from hyperopt results

For quantitative traders, this is an excellent multi-indicator strategy template. Recommendations:

Use as an advanced case for multi-indicator combinations
Learn order book check and order timeout mechanisms
Can add trend filter, BTC correlation and other protection mechanisms on this basis
Note that hyperparameters may overfit, test thoroughly before live trading

I. Strategy Overview​

Core Features​

II. Strategy Configuration Analysis​

2.1 Base Risk Parameters​

2.2 Order Type Configuration​

III. Entry Conditions Details​

3.1 Entry Logic (No Position)​

3.2 Entry Conditions (With Position)​

3.3 Hyperparameters​

IV. Exit Logic Explained​

4.1 Exit Conditions​

4.2 Order Timeout Check​

V. Technical Indicator System​

5.1 Core Indicators​

5.2 Indicator Calculation​

VI. Risk Management Features​

6.1 Trailing Stop​

6.2 Order Timeout Check​

6.3 Sell Profit Protection​

VII. Strategy Pros & Cons​

✅ Advantages​

⚠️ Limitations​

VIII. Applicable Scenarios​

IX. Applicable Market Environments Explained​

9.1 Strategy Core Logic​

9.2 Performance in Different Market Environments​

9.3 Key Configuration Recommendations​

X. Important Note: Order Book Dependency​

10.1 Moderate Learning Curve​

10.2 Moderate Hardware Requirements​

10.3 Order Book Dependency​

10.4 Manual Trader Recommendations​

XI. Summary​