Plotly Interactive Charts - MOEX Financial Data

Distribution of Return Ranges

This table shows the distribution of daily returns in predefined percentage ranges.

Advanced Filtering Options

Enable Advanced Range Filtering

Rolling Window (days):

Filter by Average: |return| > |rolling_avg| × N

Multiplier N:

Filter by Std Dev: |return| > |rolling_std| × K

Multiplier K:

Filter by EWMA Volatility: |return| > EWMA_vol × M

EWMA Multiplier M:

EWMA Lambda:

Note: Standard deviation and EWMA filters are mutually exclusive - only one can be active at a time.

Combination Logic:

OR (either condition)

AND (all conditions)

Rolling statistics calculated for each day using previous N days

Cumulative Distribution Analysis

Density distribution and cumulative distribution functions of cumulative returns and cumulative days for the selected range.

Average Returns for Next Trading Days After Selected Range

This table shows the average returns across all occurrences for the next trading days following the selected range.

Number of Days to Analyze:

Normalize Cumulative Returns by Day Number

Returns for Next Trading Days After Selected Range

This table shows the returns for each occurrence for the next trading days following the selected range.

Return Statistics Summary

Returns Density Plot

Confidence Level:

95% 99%

Ljung-Box Test for Autocorrelation

Augmented Dickey-Fuller Test for Stationarity

Rolling Autocorrelation

Window Size:

Lag:

Ticker Analysis with Significant First-Order Autocorrelation

This table shows all tickers with statistically significant first-order autocorrelation.

The sign indicates whether the autocorrelation is positive (+) or negative (-).

Summary Statistics

Tickers with Significant First-Order Autocorrelation

Multi-Lag Consecutive Autocorrelation Analysis

This table shows tickers with consecutive significant autocorrelations

starting from lag 1 with the same sign across multiple lags (up to 5).

Analysis Options

Maximum Lags to Analyze:

Summary Statistics

Tickers with Consecutive Significant Autocorrelations Starting from Lag 1

Autocorrelation-Based Strategy Backtesting

Backtest the autocorrelation-based trading strategy on historical data for the selected instrument.

Note: The strategy requires at least 610 days of historical data to calculate the first autocorrelation value. The analysis will start after the minimum data requirement is met.

Backtest Parameters

Entry Timing:

Initial Capital ($):

Start Date:

End Date: