True Strength Index (TSI)

The True Strength Index (TSI) is a momentum oscillator developed by William Blau. It measures the strength of a financial instrument by using double smoothing to minimize long-term trends and provide a clearer indication of price changes and potential shifts in market momentum.

Understanding the True Strength Index

The TSI takes into account the price difference between successive bars, often referred to as the price change or delta, and smooths it using a pair of exponential moving averages (EMAs). This double EMA smoothing approach helps filter out the noise and identify more significant trends or shifts in momentum. The TSI oscillates between positive and negative values and can be interpreted in ways similar to other momentum oscillators like the Relative Strength Index (RSI).

Formula

The TSI is calculated with the following formula:

1. Price Change ([delta](../d/delta.html)): ( [Delta](../d/delta.html) = \text{Close} - \text{Close}_{prev} )

2. Smoothed Price Change ([Delta](../d/delta.html)): ( \text{Smoothed} [Delta](../d/delta.html) = EMA(EMA([Delta](../d/delta.html), r), s) )

3. Absolute Price Change (absDelta): ( abs[Delta](../d/delta.html) = |[Delta](../d/delta.html)| )

4. Smoothed Absolute Price Change (absDelta): ( \text{Smoothed} abs[Delta](../d/delta.html) = EMA(EMA(abs[Delta](../d/delta.html), r), s) )

5. True Strength Index: ( TSI = \frac{Smoothed [Delta](../d/delta.html)}{Smoothed abs[Delta](../d/delta.html)} \times 100 )

Where:

• EMA is the Exponential Moving Average
• r and s are the periods for the first and second EMA smoothings, respectively

Interpretation

Overbought and Oversold Conditions

• Overbought: A TSI value above a certain high threshold (commonly 25) may indicate that the asset is in an overbought condition and could be due for a potential price correction.
• Oversold: Conversely, a TSI value below a low threshold (commonly -25) could suggest that the asset is oversold and might be poised for a rebound.

Crossovers and Signal Lines

• Signal Line: Often, a smoothed version of the TSI (usually with a shorter EMA period) is used as a signal line to generate buy or sell signals. When the TSI crosses above the signal line, it may be a bullish signal, implying it’s a good time to buy. When the TSI crosses below the signal line, it could be a bearish signal, suggesting a potential sell time.
• Zero Line Crosses: A cross above zero might indicate bullish momentum, while a drop below zero could signal bearish momentum.

Divergences

Divergences between the TSI and the price of the underlying asset can also provide valuable trading signals:

• Bullish Divergence: When the price makes a lower low, but the TSI makes a higher low, it can be an indication of weakening downward momentum and potential reversal to the upside.
• Bearish Divergence: When the price sets a higher high, but the TSI forms a lower high, it could signal a weakening uptrend and potential reversal to the downside.

Advantages of TSI

1. Reduces Noise: The double smoothing process helps reduce noise from short-term price fluctuations, making the TSI a clearer indicator of momentum shifts.
2. Identification of Bullish & Bearish Markets: The TSI’s ability to track both price direction and momentum allows traders to identify bullish or bearish markets more effectively.
3. Useful for Various Timeframes: TSI can be applied across multiple timeframes, making it a versatile tool for both short-term and long-term traders.

Implementation and Usage

In Trading Platforms

Many trading platforms like MetaTrader, thinkorswim (by TD Ameritrade), and TradingView support TSI as a built-in indicator. Traders can easily add TSI to their charts and modify parameters (r and s) to suit their specific trading strategies and the particular asset they are analyzing.

Optimization

Parameter optimization is a common practice among traders using TSI. Adjusting the periods for the EMAs (r and s) can help fine-tune the indicator to match the volatility and behavioral characteristics of different financial instruments.

Example Calculation

Let’s assume r = 25 and s = 13. Here is a step-by-step process to calculate the TSI:

1. Price Changes: Compute the daily price changes by subtracting the previous day’s close from today’s close.
2. Smoothing: Apply a 25-period EMA to the series of price changes, followed by a 13-period EMA to the resulting series.
3. Absolute Price Changes: Take the absolute value of the daily price changes.
4. Smoothing Absolute Changes: Apply the same EMA smoothings (25-period followed by 13-period) to the absolute price changes.
5. TSI Calculation: Divide the double-smoothed price change by the double-smoothed absolute price change and multiply by 100 to obtain the TSI value.

Application in Algorithmic Trading

The TSI can play a significant role in algorithmic trading due to its responsiveness and accuracy in detecting momentum shifts. Here’s an example of how TSI can be integrated into an algorithmic trading strategy:

Strategy Outline

1. Data Preprocessing: Collect historical price data for the financial instrument.
2. TSI Calculation: Calculate the TSI values based on chosen r and s periods.
3. Signal Generation:
   • Buy Signal: Trigger a buy trade when the TSI crosses above the signal line or rises above zero.
   • Sell Signal: Trigger a sell trade when the TSI crosses below the signal line or falls below zero.
4. Backtesting: Validate the strategy by running it against historical data to assess performance (profitability, drawdown, win-rate, etc.).
5. Optimization: Adjust parameters (r and s) and thresholds to optimize performance.
6. Implementation: Deploy the strategy in a live trading environment with risk management settings.

Libraries and Frameworks

Various libraries and frameworks in languages such as Python and R can be leveraged to implement TSI-based strategies:

• Python:
  • pandas for data manipulation
  • numpy for numerical calculations
  • ta-lib or pandas_ta for technical indicators, including TSI
  • [backtrader](../b/backtrader.html) for backtesting
• R:
  • quantmod for data retrieval
  • TTR for technical analysis functions, including TSI
  • PerformanceAnalytics for performance metrics

Example in Python

Below is a simple example of computing TSI using Python and the ta-lib library:

[import](../i/import.html) pandas as pd
[import](../i/import.html) talib

# Sample data: replace with actual price data
data = {
    'Close': [10, 11, 12, 11, 13, 14, 13, 14, 15, 14, 16]
}
df = pd.DataFrame(data)

# Compute TSI
df['TSI'] = talib.TSI(df['Close'], timeperiod1=25, timeperiod2=13)

print(df)

Practical Example: Using TSI in TradingView

TradingView is a popular platform that traders use for charting, screening, and analyzing financial markets. Integrating TSI into TradingView charts is straightforward:

1. Adding TSI Indicator:
   • Open a chart in TradingView.
   • Click on “Indicators” in the toolbar.
   • Search for “True Strength Index” and select it.
2. Customizing Parameters:
   • Adjust the periods for the EMAs (r and s) as desired in the settings.
3. Interpreting Signals:
   • Observe TSI crossovers and divergences in relation to price movements.
   • Utilize overbought and oversold conditions to make informed trading decisions.

You can explore TSI further on TradingView’s website: TradingView.

Conclusion

The True Strength Index is a valuable momentum oscillator that provides insights into the strength and direction of market trends. Its double smoothing technique reduces noise and makes it an effective tool for identifying potential entry and exit points in various trading strategies. Whether used for manual market analysis or integrated into more complex algorithmic trading systems, TSI can significantly enhance a trader’s ability to navigate financial markets.