In this repo, we aim to process historical data of cryptocurrencies to identify patterns and trends within their behavior, and use this information to make informed trading decisions.
We have chosen to focus on several key indices, including: IRS, Pivot Point, Fibonacci retracement, EMA, and stochastic oscillator, as these indices have proven to be effective in predicting market trends and making informed trading decisions. By calculating these indices for various cryptocurrencies, we can identify potential entry and exit points for trades, and make more profitable trading decisions.
Exploratory Data Analysis (EDA) : We will begin by performing exploratory data analysis of the provided historical data to identify any trends or patterns in the time series data. This will involve visualizing the data, analyzing the distribution of the data, and identifying any outliers or anomalies.
Statistical Techniques: We plan to use a variety of statistical techniques to analyze the time series data, including time series decomposition, autocorrelation analysis, and statistical modeling. These techniques will help us to identify any trends, seasonal patterns, or other time-dependent relationships within the data.
Applying Indices: We will apply various indices such as IRS, Fibonacci, EMA, and stochastic oscillator to the time series data at different time frames (e.g., daily, weekly, monthly, etc.). This will help us to identify potential entry and exit points for trades, as well as to identify patterns of trend, consolidation, resistance breakthroughs, and momentum.
Pattern Identification: Once we have applied the indices, we will use them to identify patterns of trend, consolidation, resistance breakthroughs, and momentum within the data. This will involve identifying support and resistance levels, analyzing trends and patterns over time, and identifying potential breakouts or breakdowns in the data.