Stat Analyzer
Background
Through statistical analysis, traders can confirm or reject their hypothesis. For example, most traders view negative funding as a bullish sign, but is it truly bullish? Statistical analysis gives us the power to quantify these questions and trade with confirmed results.
To answer this question quantitatively, we apply the following steps:
Identify every single occurrence where the funding rate is negative
On each of these occurences, find the change in price over the next 24 hours.
Take the average of each of these 24 hour price changes to get an overall average price change when funding rate is negative
Compare it to a benchmark
For this analyzer, the benchmark is the full dataset – that is, finding the percentage change in price over the next 24 hours for every single funding rate occurrence. By comparing the results of negative funding rate vs full dataset (every funding rate occurrence), one can gain insights on how negative funding rate impacts price and if the results are significant.
Applying stat analyzer to different indicator parameters or a combination of indicator parameters allows users to find new edges in the market.
Tool Overview
The first input allows users to select from a range of indicators with the option of selecting multiple indicators as well. All queries are run on the user selected indicators. Every indicator selection creates a slider with the indicator parameters as the slider range. Users can choose their query range on each slider and the stat analyzer will generate the backtest table on these selections over the full dataset (as far back as available). If multiple indicators are chosen, the condition is interpreted as an AND condition. In other words, it finds results when indicator 1 falls under the range selected AND indicator 2 falls under the slider range selected (when all conditions apply).
We also pair the backtest results with a highlighter graph visual. Every single time that the filtered condition occurs, it is highlighted on the graph. The date range of this graph can be selected by the user in the date range selector under the sliders. Note, this range does not affect the backtest tables and is only used for the highlighter.
The results generated display statistical analysis table and a graphical visuals highlighting every single occurrence in the date range selected. The table displays two types: backtest and full sample or the benchmark. The following columns are also generated:
Number of Occurrences: raw total count of the query condition (how often did the indicator(s) fall in their selected slider range). For the full sample, this is the total count of dataset.
Pct of Occurrences: This is the count / total count or percentage of times the query condition occurred relative to full dataset. For full sample, this will always be 100%.
5min avg pctChange: The average of every price change percentage over the next 5 minutes from when the condition occurred. In other words for the backtest row, it finds everytime the condition occured and what the price change (in percentage) was over the next 5 mins. Then it takes the average of all of these price changes. For full sample, it is essentially the price change over the next 5 mins on every single occurrence.
All other columns have the same logic as the 5min avg pctChange except applied over different time periods
How do traders use this?
Coming soon ...
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