Example 2: Trend Following Strategy Using Moving Averages

Example 1: Using a synthetic dataset with moving averages

Example 3: Trend Reversal Strategy Using Bollinger Bands

One of the most often used and straightforward techniques for trend analysis is the moving average. By displaying the average price over a given duration, they mitigate the impact of price changes. A moving average can serve as a dynamic level of support or resistance that shows the trend’s strength and direction. Using two moving averages of differing lengths and trading on their crossings is a popular trend-following method. When a shorter-term moving average crosses above a longer-term moving average, signifying an uptrend, for instance, a positive signal is produced. A shorter-term moving average crossing below a longer-term moving average, signifying a decline, generates a negative signal.

I’ll use the Apple stock (AAPL) daily closing prices from January 1, 2020, to December 31, 2020, for this example. As trend indicators, I’ll employ a 50-day and a 200-day simple moving average (SMA). Additionally, to prevent overbought and oversold situations, I’ll employ a 14-day relative strength index (RSI) as a filter. On a scale ranging from 0 to 100, the relative strength index, or RSI, gauges the velocity and deviation of price fluctuations. In general, overbought situations are indicated by an RSI above 70, while oversold conditions are indicated by an RSI below 30. The following is the strategy:

Purchase when the RSI is below 70 and the 50-day SMA crosses over the 200-day SMA.
Sell when the RSI is over 70 or the 50-day SMA crosses below the 200-day SMA.
Otherwise, have cash on hand.

The code for this approach is as follows:

Python3

# Import libraries
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
 
# Download data
data = yf.download("AAPL", start="2020-01-01", end="2020-12-31")
 
# Calculate moving averages
data["SMA_50"] = data["Close"].rolling(50).mean()
data["SMA_200"] = data["Close"].rolling(200).mean()
 
# Calculate RSI
delta = data["Close"].diff()
gain = delta.clip(lower=0)
loss = delta.clip(upper=0).abs()
avg_gain = gain.ewm(com=13, adjust=False).mean()
avg_loss = loss.ewm(com=13, adjust=False).mean()
rs = avg_gain / avg_loss
data["RSI"] = 100 - (100 / (1 + rs))
 
# Define signals
data["signal"] = 0
data.loc[(data["SMA_50"] > data["SMA_200"]) & (data["RSI"] < 70), "signal"] = 1
data.loc[(data["SMA_50"] < data["SMA_200"]) | (data["RSI"] > 70), "signal"] = -1
 
# Calculate returns
data["return"] = data["Close"].pct_change()
data["strategy_return"] = data["return"] * data["signal"].shift(1)
 
# Plot results
plt.figure(figsize=(12,8))
plt.subplot(211)
plt.plot(data["Close"], label="Price")
plt.plot(data["SMA_50"], label="SMA_50")
plt.plot(data["SMA_200"], label="SMA_200")
plt.scatter(data.index, data["Close"], c=data["signal"], cmap="coolwarm", marker="o", alpha=0.5, label="Signal")
plt.title("AAPL Trend Following Strategy Using Moving Averages")
plt.xlabel("Date")
plt.ylabel("Price")
plt.legend()
 
plt.subplot(212)
plt.plot((1 + data["strategy_return"]).cumprod(), label="Strategy")
plt.plot((1 + data["return"]).cumprod(), label="Buy and Hold")
plt.title("Cumulative Returns")
plt.xlabel("Date")
plt.ylabel("Return")
plt.legend()
plt.tight_layout()
plt.show()

Output:

As you can see, the strategy generates buy and sell signals based on the moving average crossovers and the RSI filter. The strategy outperforms the buy-and-hold strategy in terms of cumulative returns, especially during the downtrend in March and April 2020. However, the strategy also suffers from some whipsaws and false signals, such as in June and September 2020, when the price oscillates around the moving averages. This is a common drawback of trend-following strategies, as they tend to lag behind the price movements and are prone to noise and volatility.

Understanding Trend Analysis and Trend Trading Strategies

Consider being able to forecast future changes in the financial markets, such as the stock market. Here’s where trend trading tactics and trend analysis are useful. We will explain trend analysis fundamentals in this post and provide newbies with a thorough overview of comprehending and using trend trading techniques. Trend analysis and trend trading are two popular techniques that traders use to identify and profit from the market’s direction.

In this article, we will explain these techniques, how they work, and how you can apply them to your trading.

Table of Content

What is Trend Analysis?
Steps in Trend Analysis
What is Trend Trading?
Trend Trading Strategies
How to Trade the Trend – Trend Trading Strategies
Example 1: Using a synthetic dataset
Example 2: Trend Following Strategy Using Moving Averages
Example 3: Trend Reversal Strategy Using Bollinger Bands
Trend Trading Strategy – Pros and Cons
Final Word – Why Trend Trading is a Highly Effective Technique to Trade Financial Markets?

Example 2: Trend Following Strategy Using Moving Averages

Python3

Understanding Trend Analysis and Trend Trading Strategies

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