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Innovative Trading Techniques: Introducing the Catapult Indicator

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The Catapult Indicator utilizes three key components to navigate market volatility effectively: Volatility acts as the fulcrum, Momentum as the payload propeller, and a Directional Filter as the support system. This innovative tool aims to provide predictive signals for volatility acceleration based on historical patterns and its subsequent direction. Essentially, it seeks to answer the crucial questions: when will the market shift, and in which direction? While no solution is foolproof, the Catapult Indicator has demonstrated superior performance compared to traditional methods.

I have recently released a new book following the success of my earlier work, New Technical Indicators in Python. This latest publication offers an in-depth exploration of trading strategies, complete with a dedicated GitHub page for ongoing code updates. If this piques your interest, you can find the Amazon link below, or reach out to me on LinkedIn for a PDF version.

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<h2>The Book of Trading Strategies</h2>

<div><h3>Amazon.com: The Book of Trading Strategies: 9798532885707: Kaabar, Sofien: Books</h3></div>

<div><p>www.amazon.com</p></div>

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Understanding Volatility

The volatility component of the Catapult is determined using the 21-period Relative Volatility Index (RVI) to forecast significant market movements. Various methods exist for measuring volatility, such as the Average True Range, Mean Absolute Deviation, and Standard Deviation; we will employ Standard Deviation to derive the Relative Volatility Index.

Standard Deviation serves as a fundamental measure of volatility and is central to many technical indicators, including the renowned Bollinger Bands. Before computing the Standard Deviation, we must first understand Variance, defined as the square of deviations from the mean. This adjustment ensures that distances from the mean remain non-negative, and when we take the square root, we align the unit of measure with that of the mean.

Variance can be calculated using the formula:

Consequently, the Standard Deviation can be expressed as:

# Function to add a specified number of columns to an array def adder(Data, times):

for i in range(1, times + 1):

new_col = np.zeros((len(Data), 1), dtype=float)

Data = np.append(Data, new_col, axis=1)

return Data

# Function to remove a specified number of columns starting from an index def deleter(Data, index, times):

for i in range(1, times + 1):

Data = np.delete(Data, index, axis=1)

return Data

# Function to eliminate a certain number of rows from the beginning def jump(Data, jump):

Data = Data[jump:, ]

return Data

... [Code continues as in the original content]

The thrilling task now is to convert historical Standard Deviation into a predictive tool for potential volatility outbreaks. This can be achieved by applying the Relative Strength Index (RSI) formula to the Standard Deviation values.

The RSI is one of the most well-known momentum indicators, particularly effective in fluctuating markets. Its bounded nature (0 to 100) simplifies interpretation, and its popularity can create self-fulfilling prophecies in market behavior.

The RSI calculation begins with determining price differences for each period. We subtract each closing price from the previous one, calculate the smoothed average of positive differences, and divide that by the smoothed average of negative differences to arrive at Relative Strength, which is transformed into an RSI value.

def ma(Data, lookback, close, where):

Data = adder(Data, 1)

for i in range(len(Data)):

try:

Data[i, where] = (Data[i - lookback + 1:i + 1, close].mean())

except IndexError:

pass

# Cleaning

Data = jump(Data, lookback)

return Data

... [Continued code and descriptions follow]

The Momentum Component

For momentum, the Catapult Indicator employs a 14-period RSI to gauge the likelihood of market direction.

The RSI is constrained between 0 and 100, where two critical levels indicate potential market reversals:

  • Oversold (30): Suggests a potential bullish reversal.
  • Overbought (70): Indicates a possible bearish reversal.

Additionally, comparing the current RSI reading to the midpoint (50) can provide insights into potential bullish or bearish momentum.

For further insights into technical indicators and strategies, my best-selling book, New Technical Indicators in Python, may interest you.

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<h2>New Technical Indicators in Python</h2>

<div><h3>Amazon.com: New Technical Indicators in Python: 9798711128861: Kaabar, Mr Sofien: Books</h3></div>

<div><p>www.amazon.com</p></div>

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The Directional Filter Component

To complement the directional aspect, the Catapult utilizes a 200-period simple moving average (SMA) to align with market trends. This serves as a crucial sanity check and enhances our odds of success.

Moving averages are essential technical indicators, widely recognized for their simplicity and effectiveness in supporting analysis. They assist in identifying support and resistance levels, determining stop-loss and target points, and interpreting underlying trends.

Mathematically, a moving average is calculated by dividing the total of observed values by the number of observations.

With a firm grasp of moving averages, we can now proceed to develop the Catapult Indicator.

Constructing the Catapult Indicator

This indicator is an effective amalgamation of the three components outlined:

  • The 21-period RVI initiates volatility signals, indicating a potential directional move.
  • The 14-period RSI assesses the probable direction: above 50 indicates bullish potential, while below 50 suggests bearish potential.
  • The 200-period SMA reinforces the direction's credibility: if the market is above this average, bullish momentum is likely; conversely, if below, bearish pressure prevails.

lookback_rvi = 21 lookback_rsi = 14 lookback_ma = 200

my_data = ma(my_data, lookback_ma, 3, 4) my_data = rsi(my_data, lookback_rsi, 3, 5) my_data = relative_volatility_index(my_data, lookback_rvi, 3, 6)

The Catapult operates as an overlay indicator, featuring dual indicators: blue and green arrows signal buy opportunities, while blue and red arrows indicate sell opportunities.

The following chart illustrates a practical example using recent hourly EURUSD data.

def signal(Data, rvi_col, signal):

Data = adder(Data, 10)

for i in range(len(Data)):

if Data[i, rvi_col] < 30 and

Data[i - 1, rvi_col] > 30 and

Data[i - 2, rvi_col] > 30 and

Data[i - 3, rvi_col] > 30 and

Data[i - 4, rvi_col] > 30 and

Data[i - 5, rvi_col] > 30:

Data[i, signal] = 1

return Data

The signals produced are straightforward and comprehensible. The Catapult can be integrated with additional trading strategies for enhanced efficacy.

my_data = signal(my_data, 6, 7)

Conclusion

Always ensure to conduct thorough back-tests. Maintain a healthy skepticism towards others' methods, as what works for me may not suit your trading style.

I advocate for a hands-on learning approach rather than mere imitation. Grasp the underlying principles of the strategy, adapt it, and refine your version through testing before deciding to implement it or discard it. My decision to refrain from providing explicit back-testing results is intended to encourage readers to explore and enhance the strategy independently.

Medium serves as a treasure trove of insightful articles. I immersed myself in numerous reads before embarking on my own writing journey. Consider joining Medium through my referral link!

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<h2>Join Medium with my referral link — Sofien Kaabar</h2>

<div><h3>As a Medium member, a portion of your membership fee goes to writers you read, and you get full access to every story…</h3></div>

<div><p>kaabar-sofien.medium.com</p></div>

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In conclusion, are the strategies I propose realistic? Yes, but only when the trading environment is optimized (robust algorithms, low costs, trustworthy brokers, and sound risk management). Are these strategies exclusively for trading? No, they are designed to inspire new ideas and approaches in trading, as we grow weary of conventional methods like an oversold RSI suggesting short positions or a breakthrough resistance indicating long positions. I aim to introduce a fresh perspective known as Objective Technical Analysis, which relies on concrete data for evaluating techniques rather than outdated traditions.

A Final Note

I have recently launched an NFT collection aimed at supporting various humanitarian and medical initiatives. The Society of Light is a series of limited collectibles designed to contribute positively, with a percentage of each sale going directly to the associated charity. Here are some benefits of purchasing these NFTs:

  • High-potential gain: By focusing remaining sales proceeds on marketing The Society of Light, I aim to maximize their value in the secondary market, ensuring a portion of royalties also benefits the same charity.
  • Art collection and portfolio diversification: Owning a collection of avatars that symbolize altruistic efforts is deeply rewarding. Investing can serve both personal gain and charitable purposes.
  • Flexible donations: This provides a versatile way to allocate funds to your preferred causes.
  • Free book offer: All NFT purchasers will receive a complimentary PDF copy of my latest book, as detailed in this article's link.
Support a Cause Through NFTs

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