What is MFI (money flow index)? And how it helps in sudden strike recognition?

The Money Flow Index helps traders spot big market moves before they happen. This guide shows how MFI combines price and volume to signal early warning.

Understanding Money Flow Index Basics

Think of MFI as a tool that monitors price and trading volume. When prices go up with high volume, it shows strong buying pressure. As our RSI analysis shows, technical indicators help spot market moves early.

How MFI Calculates Market Pressure

MFI uses these steps:

1. Find typical price (high + low + close ÷ 3)
2. Calculate money flow (typical price × volume)
3. Compare today’s flow with yesterday’s.
4. Track changes over time

At Meyka, we help you understand these signals easily. Try our AI chatbot to see MFI in action.

Spotting Market Moves with MFI

MFI helps you find:

• Overbought levels (above 80)
• Oversold levels (below 20)
• Trend reversals
• Volume confirmations

Real Examples of MFI Signals

When price and MFI move differently, it often signals a coming change. For example:

• Price goes up but MFI drops = possible reversal
• Price drops but MFI rises = potential bounce
• Both align = strong trend

Using MFI for Better Trading

We recommend watching for:

• Divergences between price and MFI
• Extreme readings (above 80 or below 20)
• Trend line breaks
• Volume spikes

Learn how AI helps spot these patterns more easily.

Python Implementation of MFI

For developers and data analysts, here’s how to calculate MFI using Python:

def calculate_mfi(df, period=20):
    """
    Calculate the Money Flow Index (MFI).
    Parameters:
    df (pd.DataFrame): DataFrame containing 'OHLCV' values.
    period (int): The period for the MFI calculation. Default is 20.
    Returns:
    pd.Series: A series containing the MFI values.
    """
    TP = (df["high"] + df["low"] + df["close"]) / 3
    MoneyFlow = TP * df["volume"]
    positive_flow = []
    negative_flow = []
    
    for i in range(1, len(TP)):
        if TP[i] > TP[i - 1]:
            positive_flow.append(MoneyFlow[i])
            negative_flow.append(0)
        elif TP[i] < TP[i - 1]:
            positive_flow.append(0)
            negative_flow.append(MoneyFlow[i])
        else:
            positive_flow.append(0)
            negative_flow.append(0)
            
    positive_mf = pd.Series(positive_flow).rolling(window=period).sum()
    negative_mf = pd.Series(negative_flow).rolling(window=period).sum()
    MFR = positive_mf / negative_mf
    MFI = 100 - (100 / (1 + MFR))
    return MFI

This code:

1. Takes price and volume data
2. Calculates typical price
3. Determines money flow
4. Compares positive and negative flows
5. Returns MFI values

At Meyka, we use similar algorithms to provide real-time MFI analysis.

Start Using MFI Today

Get started with Meyka:

1. Visit meyka.com
2. Chat with our AI-powered stock research chatbot.
3. Wait for our technical stock screener to launch.
4. Track your stocks and even ai stocks.

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