There are several dozens of trading tools and metrics available to retail investors to be utilized in inidividual trading strategies. Most retail investors do not have the sophisticated calculations or computational algorithms readily availble to assist with trade decision making.
With the limitation, and lack of access, to the sophisticated strategic tools, there is an inherent opportunity to capture profits with predictive power of computational modeling and machine learning. As the market continues to shift to a more decentralized and democratic model with lower barrier to entry to cryptocurrency market.
Elliott Wave Theory and Fibonacci Retracement are two interrelated trading strategy tools often utilized by seasoned and sophisticated traders. Most often traders utiliz these tools to validate their intuitions regarding the movement of a currency or stock trend. Our approach is to create an algorithmic computation to assist with the measurements.
Elliott Wave Theory (EWT) was developed by Ralph Nelson Elliott in the 1930s. EWT looks for recurrent long-term price patterns related to persistent changes in investor sesntiment and psychology. It identifies impulse waves that set up a pattern and corrective waves that oppose the larger trend. (Ref: Investopedia) The lengths of the waves and the resistence and support of corrective waves are all identifiable with Fibonacci retracement with Fibonacci sequence and ratio as the basis of calculations.
Fibonacci retracement is a measure of potential market price resistence or support between a high point and a low point in the historical data of a currency or stock. Fibonacci is a natural sequence of numbers that is generated by adding the last number with the previous number. With the exception of the first few values in the sequence, Fibonacci sequence develops a natural relationship between the numbers in the form of ratios, which is the Golden Ratio Φ. (Ref: Investopedia)
To properly measure the waves, we have to find the natural low or the head of the wave and the real resistance high of the wave. These have to be identified with the following approaches:
- Momentum defined by percent change, standard deviation, averages, and Relative Strength Index (RSI)
- Directional Covariance
- Shape of the trend line based on the historical data
There are still natural learning curves present in our project where we have to apply laws of physics, advanced statistical theories and methodologies, and designs of quantitative methods to culminate the varieties of triggers altogether as features of a pattern of the greater market movement.
Another challenge is the limitation to our knowledge of Python technologies and libraries that may serve as key tools to streamline our analysis.
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.684.3568&rep=rep1&type=pdf https://link.springer.com/article/10.1007/s40010-016-0323-8 https://www.sciencedirect.com/science/article/abs/pii/S0957417411000881 https://www.investopedia.com/articles/technical/04/033104.asp https://www.investopedia.com/terms/e/elliottwavetheory.asp https://www.investopedia.com/terms/r/rsi.asp