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.

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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.

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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.

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