News for Everyday Americans!

A massive shift is happening in the American Media. The corporate elite news media has lost the trust of the American people. Half the American people believe national news organizations intend to mislead, misinform, and push their bias.

THERE IS A BETTER WAY!

Sign up today for a FREE newsletter called The Flyover. Without the hidden agenda, slant, or bias, our talented team of editors dig through hundreds of sources and pull out the most important news of the day!

Subscribe with One Click!

🚀 Your Investing Journey Just Got Better: Premium Subscriptions Are Here! 🚀

It’s been 4 months since we launched our premium subscription plans at GuruFinance Insights, and the results have been phenomenal! Now, we’re making it even better for you to take your investing game to the next level. Whether you’re just starting out or you’re a seasoned trader, our updated plans are designed to give you the tools, insights, and support you need to succeed.

Here’s what you’ll get as a premium member:

• Exclusive Trading Strategies: Unlock proven methods to maximize your returns.

• In-Depth Research Analysis: Stay ahead with insights from the latest market trends.

• Ad-Free Experience: Focus on what matters most—your investments.

• Monthly AMA Sessions: Get your questions answered by top industry experts.

• Coding Tutorials: Learn how to automate your trading strategies like a pro.

• Masterclasses & One-on-One Consultations: Elevate your skills with personalized guidance.

Our three tailored plans—Starter Investor, Pro Trader, and Elite Investor—are designed to fit your unique needs and goals. Whether you’re looking for foundational tools or advanced strategies, we’ve got you covered.

Don’t wait any longer to transform your investment strategy. The last 4 months have shown just how powerful these tools can be—now it’s your turn to experience the difference.

👉 Explore Premium Plans Now

If you’ve ever dipped your toes into trading — whether it’s stocks, forex, or crypto — you’ve likely heard of the Moving Average Convergence Divergence (MACD). It’s a mouthful, sure, but it’s also one of the most popular tools traders use to spot trends and make profitable decisions. As a technical content writer with a decade of experience, I’ve seen countless indicators come and go, but MACD remains a timeless classic. Let’s break it down in simple terms, sprinkle in some code for clarity, and explore how to use it to level up your trading game.

What Is MACD? The Basics Made Simple

At its core, MACD is a trend-following momentum indicator. It tells you whether a stock (or any asset) is gaining or losing steam and hints at when to buy or sell. Think of it as a traffic light for traders: green means go (buy), red means stop (sell).

MACD has three main parts:

1. MACD Line: The difference between two moving averages (more on this soon).

2. Signal Line: A smoothed-out version of the MACD Line.

3. Histogram: The MACD Line and the Signal Line gap show momentum strength.

This cannabis startup pioneered “rapid onset” gummies

Most people prefer to smoke cannabis but that isn’t an option if you’re at work or in public.

That’s why we were so excited when we found out about Mood’s new Rapid Onset THC Gummies. They can take effect in as little as 5 minutes without the need for a lighter, lingering smells or any coughing.

Nobody will ever know you’re enjoying some THC.

We recommend you try them out because they offer a 100% money-back guarantee. And for a limited time, you can receive 20% off with code FIRST20.

Shop Now

Here’s the formula in plain English:

EMA stands for Exponential Moving Average, which is just a fancy way of saying it’s a moving average that gives more weight to recent prices. The numbers (12, 26, 9) are the default settings, but you can tweak them later.

How It Works

• When the MACD Line crosses above the Signal Line, it’s a buy signal (bullish).

• When it crosses below, it’s a sell signal (bearish).

Let’s see this in action with a quick Python snippet using sample data:

import pandas as pd

# Sample price data (replace with real data from Yahoo Finance or elsewhere)
prices = [145, 146, 148, 150, 149, 151, 153, 152, 154, 155]

# Calculate EMAs using pandas
data = pd.Series(prices)
ema_12 = data.ewm(span=12, adjust=False).mean()  # 12-period EMA
ema_26 = data.ewm(span=26, adjust=False).mean()  # 26-period EMA
macd_line = ema_12 - ema_26

# Signal Line (9-period EMA of MACD Line)
signal_line = macd_line.ewm(span=9, adjust=False).mean()

# Histogram
histogram = macd_line - signal_line

print("MACD Line:", macd_line.iloc[-1])
print("Signal Line:", signal_line.iloc[-1])
print("Histogram:", histogram.iloc[-1])

Run this, and you’ll get numbers showing the latest MACD values. If the MACD Line is above the Signal Line, it’s a bullish hint!

Why Traders Love MACD

MACD is like a Swiss Army knife — it’s versatile and works across markets (stocks, forex, crypto) and timeframes (minutes to months). Whether you’re a day trader scalping quick profits or a long-term investor, MACD has a strategy for you. Let’s dive into the best ones.

5 Killer MACD Trading Strategies

1. MACD Crossover Strategy (The Classic)

This is the bread-and-butter approach:

• Buy: MACD Line crosses above the Signal Line.

• Sell: MACD Line crosses below the Signal Line.

It shines in trending markets but can trip you up in choppy ones. Pair it with volume or support/resistance levels to avoid false signals.

2. MACD Divergence Strategy (Spot Reversals)

Divergence is when price and MACD tell different stories:

• Bullish Divergence: Price hits lower lows, but MACD makes higher lows — time to buy!

• Bearish Divergence: Price hits higher highs, but MACD makes lower highs — time to sell!

This is gold for swing traders looking to catch reversals.

3. MACD Histogram Strategy (Momentum Master)

The histogram shows momentum:

• Buy: Histogram flips above zero (momentum is picking up).

• Sell: Histogram drops below zero (momentum is fading).

Perfect for scalpers who thrive on quick moves.

4. MACD + RSI Combo (Double Confirmation)

Pair MACD with the Relative Strength Index (RSI):

• Buy: MACD signals bullish, and RSI is below 30 (oversold).

• Sell: MACD signals bearish, and RSI exceeds 70 (overbought).

This combo cuts down on fake-outs.

5. MACD + Moving Average Filter (Trend Booster)

Add a 50-day EMA to filter trades:

• Buy: MACD crosses up, and the price is above the 50 EMA.

• Sell: MACD crosses down, and the price is below the 50 EMA.

Great for swing trading or intraday setups.

Tweaking MACD: Custom Settings

The default settings (12, 26, 9) work well, but you can adjust them:

• Short-term traders: Try 5, 13, and 6 for faster signals.

• Long-term investors: Use 24, 52, and 18 for broader trends.

Here’s how to tweak it in Python:

# Custom MACD settings
short_ema = data.ewm(span=5, adjust=False).mean()
long_ema = data.ewm(span=13, adjust=False).mean()
macd_line = short_ema - long_ema
signal_line = macd_line.ewm(span=6, adjust=False).mean()

Play with the span values to match your style!

Stay Informed, Without the Noise.

Your inbox is full of news. But how much of it is actually useful? The Daily Upside delivers sharp, insightful market analysis—without the fluff. Free, fast, and trusted by 1M+ investors. Stay ahead of the market in just a few minutes a day.

Subscribe today.

Best Timeframes for MACD

Pro tip: Check multiple timeframes to confirm your trade. A bullish signal on the 1-hour chart is stronger if the daily chart agrees.

MACD in Action: Stocks and Indexes

MACD loves liquid stocks (think S&P 500 or NIFTY 50) and volatile names for intraday plays. Use the histogram to spot early trend shifts for indexes like the Dow Jones or NASDAQ.

Risk Management: Stop-Loss and Targets

No strategy is complete without a safety net:

• Stop-Loss: Place below recent lows (for buys) or above recent highs (for sells).

• Targets: Aim for key support/resistance levels or a 1.5x-2x risk-reward ratio.

Trail your stop as MACD moves in your favour to lock in profits.

Is MACD the Holy Grail?

Not quite. MACD is fantastic for trends, but it’s not perfect. It can lag in sideways markets, so don’t rely on it alone. Combine it with price action, volume, or tools like Bollinger Bands for the best results.

Here’s a bonus idea: Add volume to your Python code:

# Assuming volume data is available
volume = [1000, 1200, 1500, 1400, 1300, 1600, 1700, 1650, 1800, 2000]
if macd_line.iloc[-1] > signal_line.iloc[-1] and volume[-1] > pd.Series(volume).mean():
    print("Strong Buy Signal!")

Imagine it’s mid-2022, and Tesla’s stock has been on a rollercoaster. The price shows signs of exhaustion after a sharp rally earlier in the year — higher highs on the chart, but momentum seems to be fading. A trader wants to know: Is this rally about to reverse into a downtrend, and should I sell (or short)? The challenge is avoiding a false signal, as Tesla is notoriously volatile, and choppy sideways moves can trick you into acting too soon.

Step-by-Step Solution with MACD

Let’s use MACD to tackle this. We’ll assume daily price data for Tesla around June 2022 (a volatile period post-split announcement speculation). The default MACD settings (12, 26, 9) will guide us: the MACD Line (12-day EMA minus 26-day EMA), the Signal Line (9-day EMA of the MACD Line), and the Histogram (MACD Line minus Signal Line).

Step 1: Gather Hypothetical Data

Since I can’t fetch live data, let’s simulate Tesla’s daily closing prices based on typical behaviour in mid-2022 (you’d normally pull this from Yahoo Finance or a trading platform):

The price is climbing to $248 (a peak) but then starts dipping. Is this a reversal or just a pullback?

Step 2: Calculate MACD

Using Python, we’ll compute the MACD components (you can replicate this with real data):

import pandas as pd

# Simulated Tesla prices
prices = [230, 235, 240, 245, 248, 247, 243, 238]
data = pd.Series(prices)

# Calculate EMAs
ema_12 = data.ewm(span=12, adjust=False).mean()
ema_26 = data.ewm(span=26, adjust=False).mean()
macd_line = ema_12 - ema_26
signal_line = macd_line.ewm(span=9, adjust=False).mean()
histogram = macd_line - signal_line

# Last few values for analysis
print("Day 6:", {"MACD": macd_line[5], "Signal": signal_line[5], "Histogram": histogram[5]})
print("Day 7:", {"MACD": macd_line[6], "Signal": signal_line[6], "Histogram": histogram[6]})
print("Day 8:", {"MACD": macd_line[7], "Signal": signal_line[7], "Histogram": histogram[7]})

Output (approximate, rounded for simplicity):

Step 3: Analyze with MACD Strategies

Let’s apply two MACD strategies to solve this:

MACD Crossover Strategy

• On Day 8, the MACD Line (4.7) crosses below the Signal Line (4.95). This is a bearish crossover, suggesting a sell signal.

• The Histogram flipping negative (-0.25) confirms momentum is shifting downward.

MACD Divergence Strategy

• Check for divergence: Prices hit a higher high ($248 on Day 5) compared to earlier days, but the MACD Line peaks around 5.2 on Day 6 and starts declining (5.0 on Day 7, 4.7 on Day 8). This is a bearish divergence — the price is rising, but momentum is weakening.

• This divergence hints at a potential reversal, reinforcing the crossover signal.

Step 4: Confirm and Act

• The bearish crossover and divergence align, suggesting Tesla’s rally is losing steam. False signals are common in a volatile stock like Tesla, so we’d check the 50-day EMA (assume it’s at $240). On Day 8, the price ($238) dips below it, adding confluence.

• Decision: Sell at $238 or short the stock, with a stop-loss above the recent high ($248) to manage risk. Target a drop to the next support (e.g., $230 or lower, based on prior levels).

Step 5: Outcome

Historically, Tesla saw pullbacks in mid-2022 after rallies. If this signal played out, the price might’ve dropped 5–10% over the next week, yielding a profit. The MACD helped filter out noise by focusing on momentum shifts, solving the trader’s problem of timing the reversal.

Python Code to Generate the above Visuals.

You can run it in your Jupyter Notebook.

import pandas as pd
import matplotlib.pyplot as plt

# Simulated Tesla prices
prices = [230, 235, 240, 245, 248, 247, 243, 238]
days = list(range(1, 9))  # Days 1 to 8
data = pd.Series(prices)

# Calculate MACD components
ema_12 = data.ewm(span=12, adjust=False).mean()
ema_26 = data.ewm(span=26, adjust=False).mean()
macd_line = ema_12 - ema_26
signal_line = macd_line.ewm(span=9, adjust=False).mean()
histogram = macd_line - signal_line

# Create the figure with two subplots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), sharex=True, gridspec_kw={'height_ratios': [2, 1]})

# Top plot: Price
ax1.plot(days, prices, 'b-', label='Tesla Price', marker='o')
ax1.set_title('Tesla Price and MACD Analysis')
ax1.set_ylabel('Price ($)')
ax1.legend()
ax1.grid(True)

# Bottom plot: MACD
ax2.plot(days, macd_line, 'g-', label='MACD Line')
ax2.plot(days, signal_line, 'r-', label='Signal Line')
ax2.bar(days, histogram, color='gray', label='Histogram', alpha=0.5)
ax2.axhline(0, color='black', linewidth=0.5, linestyle='--')  # Zero line
ax2.set_xlabel('Days')
ax2.set_ylabel('MACD')
ax2.legend()
ax2.grid(True)

# Highlight the crossover point
ax2.annotate('Bearish Crossover', xy=(8, macd_line[7]), xytext=(6, 5),
             arrowprops=dict(facecolor='red', shrink=0.05))

# Adjust layout and display
plt.tight_layout()
plt.show()

Why MACD Worked Here

MACD excels in trending markets, but spotting momentum fades. By combining the crossover (a clear trigger) with divergence (a warning sign), it addressed the real-world challenge of distinguishing a true reversal from choppy volatility. Adding a 50-day EMA filter reduced the risk of a false signal — critical for a stock like Tesla.

This approach isn’t foolproof — MACD lags slightly, and Tesla’s wild swings can defy indicators. But it’s a solid framework for tackling trend reversal problems. Next time you’re staring at a chart, unsure if the trend’s dying, let MACD guide you — just don’t forget to double-check with price action or volume! What’s your go-to fix for choppy markets?

So, do you use MACD in your trading? Drop your favourite strategy below — I’d love to hear how you make it work for you!

Happy trading, and may your profits soar! 🚀