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Life is a give and take — trading is no different. Image generated with Leonardo AI
The best trading strategies aren’t always the most complex.
It’s tempting to believe that hedge funds rely on ultra-sophisticated algorithms. It turns out, however, that many of their core strategies are quite simple. What sets them apart is discipline, execution, and risk management.
In trading, like in science or marketing, complexity is not always an advantage. Simple, well-tested strategies can outperform convoluted models, especially when applied with the right timing and market conditions.
The key is understanding when to use each approach and how to fine-tune it for real-world performance.
In this article, I’ll walk you through three simple but powerful trading strategies that you can implement in Python and run on a laptop. We’ll break down their logic, code them step by step, and discuss the market conditions where each one shines.
Let’s get into it!
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Three Simple but Powerful Trading Strategies
We cover three trading strategies: moving average crossover, mean reversion, and momentum trading.
When markets are moving strongly in one direction, a moving average crossover helps capture the trend without getting caught in short-term noise. However, lagging signals mean traders may enter late and exit after trends shift.
In markets without a clear trend, prices often revert to historical averages. Mean reversion strategies work well here, but they require careful risk management — if a stock is dropping due to real fundamental problems, betting on a rebound can be dangerous.
When markets are rising aggressively, winners tend to keep winning. Momentum trading profits from this pattern by buying the best-performing assets. However, momentum strategies can collapse in bear markets, leading to sharp losses.
You’ll find the three strategies summarized below.
If you’re not sure which one to use because the markets are uncertain, it’s best to blend all three. You’ll find a hands-on implementation below.
Hands-On Coding: Implementing These Strategies in Python
Let’s see how each trading strategy fairs with some stock data!
Step 1: Load & Prepare Market Data
We use the Yahoo Finance API to download some stock data; in the code example below we’re using Apple stocks. We then calculate the daily percentage returns. (Our analysis is actually on dummy data instead of Apple. The code snippet below is thus for illustrative purposes.)
import pandas as pd
import numpy as np
import yfinance as yf
import matplotlib.pyplot as plt
# Load stock data
symbol = "AAPL"
df = yf.download(symbol, start="2020-01-01", end="2024-01-01")
# Calculate returns
df["Returns"] = df["Adj Close"].pct_change()
df.dropna(inplace=True)Step 2: Moving Average Crossover Strategy
For this strategy, we compute two moving averages:
A short-term (50-day) moving average.
A long-term (200-day) moving average.
We then created buy/sell signals where the short moving average crosses above or below the long moving average. If the short moving average goes above the long one, we buy; if it goes below, we sell.
The plot below displays the stock price along with the two moving averages. When the blue line (short MA) crosses above the red line (long MA), it signals a buy; when it crosses below, it signals a sell.
# Define moving averages
df["Short_MA"] = df["Adj Close"].rolling(window=50).mean()
df["Long_MA"] = df["Adj Close"].rolling(window=200).mean()
# Buy/Sell signals
df["Signal"] = np.where(df["Short_MA"] > df["Long_MA"], 1, -1)
# Plot the strategy
plt.figure(figsize=(10,5))
plt.plot(df.index, df["Adj Close"], label="Stock Price", color="black")
plt.plot(df.index, df["Short_MA"], label="50-day MA", color="blue", linestyle="dashed")
plt.plot(df.index, df["Long_MA"], label="200-day MA", color="red", linestyle="dashed")
plt.legend()
plt.title("Moving Average Crossover Strategy")
plt.show()
Moving Average Crossover Strategy. Image by author
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Step 3: Mean Reversion Strategy (Z-Score Based)
For this strategy, we need to compute a rolling mean and rolling standard deviation. We choose a 50-day window for this.
We then calculate the Z-score to measure how far the price deviates from its rolling mean.
The plot shows the Z-score with its key thresholds at Z=+2, -2. Extreme negative Z-scores (below -2) indicate potential buy opportunities; values reverting back to 0 suggest a mean-reverting sell.
# Calculate Z-score for mean reversion
df["Rolling_Mean"] = df["Adj Close"].rolling(window=50).mean()
df["Rolling_Std"] = df["Adj Close"].rolling(window=50).std()
df["Z_Score"] = (df["Adj Close"] - df["Rolling_Mean"]) / df["Rolling_Std"]
# Buy when Z-score < -2, sell when it returns
df["Mean_Revert_Signal"] = np.where(df["Z_Score"] < -2,
1,
np.where(df["Z_Score"] > 0, -1, 0)
)
Mean Reversion Strategy. Image by author
Step 4: Momentum Trading Strategy
Finally, we measure the stock’s momentum as its percentage return over the past 120 days. We define a buy signal when the momentum is in the top 10% of its historical values, and a sell signal when it is in the bottom 10%.
The plot displays the stock momentum over time, with red and green lines indicating the thresholds for strong buy or sell signals.
# Calculate momentum over the past 6 months
df["Momentum"] = df["Adj Close"].pct_change(periods=120)
# Buy top 10% of stocks, sell bottom 10%
df["Momentum_Signal"] = np.where(df["Momentum"] > df["Momentum"].quantile(0.9),
1,
np.where(df["Momentum"] < df["Momentum"].quantile(0.1), -1, 0)
)
Momentum Trading Strategy. Image by author
Step 5: Comparing Strategies
How did each strategy fare? Let’s say we executed a buy/sell whenever the respective trading strategy told us so. In this particular case, we would have done quite well already with a buy-and-hold strategy, netting us about 20% gains between 2020 and 2022.
With a moving average crossover strategy, we would have made about 10% more (a total of 30%) because we would have sold at $105 in May 2021 and bought again at $95 in March 2022.
The mean reversion strategy would have been a losing game — to be expected in a bullish market. The momentum strategy would have yielded similar returns to a buy-and-hold strategy.
On the whole, it’s always worth considering the market and sector before picking a strategy. It is clear, however, that such a choice can have a huge impact on the returns you might get!
The Bottom Line: Trade Strategically!
While sophisticated algorithms and high-frequency trading may dominate the financial world, this article shows that simple, well-executed strategies can be powerful too.
The key to success lies in understanding market conditions, choosing the right approach, and managing risk effectively.
Each strategy — moving average crossover, mean reversion, and momentum trading — has its strengths and weaknesses. A trend-following approach like the moving average crossover shines in directional markets, while mean reversion works best in sideways conditions. Momentum strategies thrive in strong bull markets but can struggle in downturns.
No single method guarantees profits in all environments, but by blending strategies, testing variations, and refining execution, traders can improve their odds of long-term success.
Whether you’re just starting out or looking to enhance your approach, the real edge in trading isn’t just in the strategy — it’s in the discipline to follow it.
Now it’s your turn — experiment with these strategies, test them on different stocks, and refine them to suit your trading style. The markets are always evolving, and the best traders evolve with them.