Trend Following Strategy 편집하기

'''Trend Following Strategy''' is a trading approach that seeks to capitalize on market trends by buying assets in an uptrend and selling (or shorting) assets in a downtrend. It is widely used in stocks, commodities, forex, and futures markets.
==Concept==
Trend following strategies operate on the principle that markets tend to move in sustained trends rather than random fluctuations. Traders using this approach do not attempt to predict price movements but instead react to existing trends.
==Characteristics==
*'''Price-Based Strategy''' – Relies on market price action rather than fundamental analysis.
*'''Medium to Long-Term Approach''' – Trends can last from weeks to months or even years.
*'''Rules-Based Trading''' – Uses predefined entry and exit criteria.
*'''Risk Management Focus''' – Uses stop-loss orders and position sizing to manage risk.
==Common Trend Following Indicators==
Trend followers use technical indicators to identify and confirm trends:
*'''Moving Averages'''
**Simple Moving Average (SMA) and Exponential Moving Average (EMA) to smooth price data.
*'''Bollinger Bands'''
**Measures volatility to identify potential trend continuations or reversals.
*'''Average Directional Index (ADX)'''
**Quantifies trend strength.
*'''Breakout Trading'''
**Trades based on price breaking above or below key levels.
==Example Strategy: Moving Average Crossover==
One common trend-following method is the '''moving average crossover strategy''', which involves:
#Using two moving averages – A short-term moving average (e.g., 50-day SMA) and a long-term moving average (e.g., 200-day SMA).
#Buy signal – When the short-term moving average crosses above the long-term moving average (Golden Cross).
#Sell signal – When the short-term moving average crosses below the long-term moving average (Death Cross).
===Example Implementation in Python===
<syntaxhighlight lang="python">
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Sample Data: Load historical stock prices
df = pd.read_csv("stock_prices.csv")
df["50_SMA"] = df["Close"].rolling(window=50).mean()
df["200_SMA"] = df["Close"].rolling(window=200).mean()

# Identify buy and sell signals
df["Signal"] = np.where(df["50_SMA"] > df["200_SMA"], 1, 0)

plt.plot(df["Close"], label="Stock Price")
plt.plot(df["50_SMA"], label="50-day SMA")
plt.plot(df["200_SMA"], label="200-day SMA")
plt.legend()
plt.show()
</syntaxhighlight>
==Advantages==
*'''Captures Large Market Moves'''
**Profits from long-term trends.
*'''No Need to Predict Markets'''
**Reacts to price movements rather than forecasts.
*'''Works Across Multiple Asset Classes'''
**Can be applied to stocks, commodities, forex, and cryptocurrencies.
==Limitations==
*'''Whipsaw Risk'''
**False signals in choppy or sideways markets.
*'''Lagging Indicator'''
**Trend following reacts to established trends rather than predicting reversals.
*'''Drawdowns'''
**Trend followers can experience prolonged losses during trendless periods.
==Applications==
*'''Systematic Trading Funds'''
**Used by hedge funds and CTAs (Commodity Trading Advisors).
*'''Algorithmic Trading'''
**Implemented in automated trading systems.
*'''Portfolio Diversification'''
**Reduces risk by capturing trends in multiple asset classes.
==See Also==
*[[Momentum Trading]]
*[[Moving Average]]
*[[Technical Analysis]]
*[[Algorithmic Trading]]
*[[Risk Management]]