LinReg Strategy Analysis

Strategy Number: #186 (186th of 465 strategies)
Strategy Type: Trend Following / Linear Regression
Timeframe: 15 Minutes (15m)

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I. Strategy Overview

LinReg (Linear Regression) is a quantitative trading strategy based on linear regression analysis. Linear regression is a statistical method used to find the best-fit line for price movements, thereby judging trend direction and strength.

Core Features

Feature	Description
Entry Condition	Price breaks above linear regression upper band + volume confirmation
Exit Condition	Price falls below linear regression lower band
Protection	Trailing stop
Timeframe	15 Minutes
Dependencies	TA-Lib, numpy

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II. Strategy Configuration Analysis

2.1 Basic Risk Parameters

minimal_roi = {
    "0": 0.05,
    "30": 0.02,
    "60": 0.01
}

stoploss = -0.10  # -10%

2.2 Parameter Settings

timeframe = '15m'
linreg_period = 20  # Linear regression period
std_dev_multiplier = 2  # Standard deviation multiplier

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III. Entry Conditions Details

3.1 Linear Regression Calculation

# Calculate linear regression line
linreg = np.polyfit(range(period), close, 1)
slope = linreg[0]
intercept = linreg[1]

# Calculate upper and lower bands
upper_band = intercept + slope * np.arange(period) + std_dev_multiplier * std
lower_band = intercept + slope * np.arange(period) - std_dev_multiplier * std

3.2 Entry Signal

# Price breaks above upper band and in an uptrend
Conditions:
- slope > 0 (uptrend)
- price > upper_band (upper band breakout)
- volume > volume_ma (volume expansion)

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IV. Exit Conditions Details

4.1 Exit Conditions

# Price falls below lower band
Conditions:
- price < lower_band
- OR slope < 0 (trend reversal)

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V. Technical Indicator System

Indicator	Description
Linear Regression	Linear regression trend line
Standard Deviation	Standard deviation (volatility)
Volume MA	Volume moving average

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VI. Risk Management

Parameter	Value
Hard Stop	-10%
Take-Profit	5%-1% declining

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VII. Strategy Pros & Cons

✅ Pros

1. Clear Trend: Linear regression provides unambiguous trend direction
2. Breakout Signals: Upper band breakout gives clear entry points
3. Simple & Intuitive: Easy to understand and implement

⚠️ Cons

1. Parameter Sensitive: Period and standard deviation multiplier have significant impact
2. Average Ranging Performance
3. Dependent on Historical Data

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VIII. Applicable Scenarios

Market Environment	Performance
Trending Up	⭐⭐⭐⭐
Trending Down	⭐⭐⭐⭐
Ranging	⭐⭐

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IX. Parameter Optimization

Parameter	Default	Description
Regression Period	20	Longer = more stable
Std Dev Multiplier	2	Larger = more lenient

Timeframe

Recommended 15 minutes or 1 hour.

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X. Summary

LinReg is a statistics-based trend strategy that uses linear regression to determine trend direction and price boundaries. Best used in markets with clear trends.

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