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Ever wished for one indicator that could cut through the noise and just tell you what’s up with a stock? Say hello to Mswing — a little gem inspired by Sakata Homma, the Japanese trading legend who probably forgot more about markets than we’ll ever know. They call it the “one indicator to rule them all,” and honestly, it’s not hype. It’s a momentum tracker, a strength gauge, a signal machine, and even a market mood ring. Let’s unpack it step by step.

So, What’s Mswing Anyway?

Mswing is like a speedometer for stock prices. It looks at how fast a stock’s moving over 20 and 50 days — two windows that give you the short and long of it. Simple rule: if it’s above zero, the stock’s got some upward juice. Below zero? It’s slumping.

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But here’s where it gets fun. Slap an EMA — exponential moving average, for the uninitiated — on top of Mswing, and you’ve got a cheat sheet for momentum. I break it down like this:

• Mswing’s above zero and above its EMA: The stock’s not just climbing — it’s picking up speed. Green light, baby.

• Above zero but below EMA: It’s still going up, but the engine’s sputtering. Maybe keep an eye on it.

• Below zero but above EMA: Things are grim, but there’s a flicker of hope — like a stock trying to claw its way back.

• Below zero and below EMA: Downtrend city. Might be time to bail.

Load that up, tweak it if you’re feeling fancy, and watch the lines dance.

# Re-import necessary libraries after execution state reset
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Generate synthetic closing price data
np.random.seed(42)
closing_prices = np.cumsum(np.random.randn(100) * 2 + 100)  # Simulated price data

# Define the Mswing Indicator calculation
length1, length2 = 20, 50

# Ensure enough data points exist before calculating Mswing
mswing = np.zeros(len(closing_prices))
for i in range(len(closing_prices)):
    if i >= length2:
        mswing[i] = ((closing_prices[i] - closing_prices[i - length1]) / closing_prices[i - length1] * 100) + \
                    ((closing_prices[i] - closing_prices[i - length2]) / closing_prices[i - length2] * 100)
    else:
        mswing[i] = np.nan  # Not enough data

# Calculate the EMA of Mswing (similar to Pine Script's ta.ema)
def ema(series, span):
    return pd.Series(series).ewm(span=span, adjust=False).mean().to_numpy()

ema_mswing = ema(mswing, 9)

# Create the plot
plt.figure(figsize=(12, 6))
plt.plot(mswing, label="Mswing", color="blue", linestyle="-", alpha=0.7)
plt.plot(ema_mswing, label="EMA of Mswing", color="orange", linestyle="-", alpha=0.9)
plt.axhline(0, color="gray", linestyle="dashed", label="Zero Line")

# Labels and title
plt.title("Mswing Indicator with EMA", fontsize=14)
plt.xlabel("Index", fontsize=12)
plt.ylabel("Mswing Value", fontsize=12)
plt.legend()
plt.grid(True)

# Show the plot
plt.show()

How Strong Is That Momentum?

Mswing doesn’t just say “up” or “down” — it tells you how much. The bigger the number, the harder the stock’s running. I like to think of it as a leaderboard. Grab a bunch of stocks, sort them by Mswing, and boom — you’ve got your winners and losers. A stock at 3.8 is smoking one at 0.5, no contest.

Relative Strength: Who’s Beating the Pack?

Here’s where Mswing flexes. Compare a stock’s Mswing to, say, the NSE 500 index. If the stock’s score beats the index, it’s got swagger. If not, it’s dragging its feet. They even color-code it for us visual folks:

• Green (🟢): Killing it — absolute beast mode.

• Orange (🟠): Doing okay — better than average, but not a superstar.

• Another Orange (🟠): Slacking — underperforming, but not a total disaster.

• Red (🔴): Oof, absolute dud.

Swap index_close for something like close(“NSEI”). If that purple line’s above zero, your stock’s got bragging rights.

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When to Jump In, When to Bail

Mswing’s not just a pretty chart — it’s a playbook. Here’s how I use it:

Getting In

• Mswing crosses above zero: Especially after a long snooze, it’s like the stock’s finally waking up.

• Breaks out of a tight range: Momentum’s brewing — could be go-time.

• Mswing tops its EMA and price cracks a big moving average: Double whammy. I’d eyeball the 50-day SMA here.

Getting Out

• Sell high: Mswing hits 4 or more? Overbought territory — grab your profits and run.

• Cut losses: Mswing’s below zero and price dips under the 50-day SMA? Don’t stick around for the funeral.

# Generate synthetic stock and index closing prices
np.random.seed(42)
stock_prices = np.cumsum(np.random.randn(100) * 2 + 100)  # Simulated stock price data
index_prices = np.cumsum(np.random.randn(100) * 1.5 + 100)  # Simulated index price data

# Define Mswing calculation function
def calculate_mswing(prices, length1, length2):
    mswing = np.zeros(len(prices))
    for i in range(len(prices)):
        if i >= length2:
            mswing[i] = ((prices[i] - prices[i - length1]) / prices[i - length1] * 100) + \
                        ((prices[i] - prices[i - length2]) / prices[i - length2] * 100)
        else:
            mswing[i] = np.nan  # Not enough data
    return mswing

# Calculate Mswing for stock and index
length1, length2 = 20, 50
mswing_stock = calculate_mswing(stock_prices, length1, length2)
mswing_index = calculate_mswing(index_prices, length1, length2)

# Calculate Relative Strength
relative_strength = mswing_stock - mswing_index

# Create the plot
plt.figure(figsize=(12, 6))
plt.plot(relative_strength, label="Relative Strength", color="purple", linestyle="-", alpha=0.9)
plt.axhline(0, color="gray", linestyle="dashed", label="Zero Line")

# Labels and title
plt.title("Mswing with Relative Strength", fontsize=14)
plt.xlabel("Index", fontsize=12)
plt.ylabel("Relative Strength Value", fontsize=12)
plt.legend()
plt.grid(True)

# Show the plot
plt.show()

Market Breadth: The Big Picture

Here’s a trick I love: use Mswing to check the market’s pulse. Take a chunk of stocks — like the NSE 500 — and see how many have Mswing above zero. That’s your breadth. My rough guide:

• Over 80%: Everyone’s partying — might be time to cash out before the crash.

• Under 20%: Everything’s trashed — could be a bargain-hunting zone.

If it’s sky-high or dirt-low, you’ll know what’s up.

Tools to Mess With

• TradingView: Drop that script in and tinker.

• Chartink: Build a scanner — sort stocks or track breadth.

• Video: There’s a killer explainer out there (imagine I linked it — go hunt one down!).

Why I Dig Mswing

After a decade of staring at charts, I’ve seen indicators come and go. Mswing sticks because it’s like a buddy who’s always got something useful to say — whether it’s momentum, strength, or just “hey, get out now.” Start small, test it on a stock you love, and see where it takes you.