FinanceTools.dollarbarsMethod

Calculates dollar bars with a constant amount of dollars per bar. Input is a dataframe in the following order: Time, Price, Volume

Output is a dataframe in the following order: Time, Open, High, Low, Close, Volume

Keyword arguments

• method : chooses the mode used to generate dollar bars, "constant" is set by default and "SMA", "EMA" will be implemented later on.
• threshold : threshold for the dollar accumulator, a new candle will be produced when the cumulative dollar amount exceeds this number.
• frequency : not used
• windowlen : not used
FinanceTools.fracdiffFunction

Calculates the fractional difference of a vector of numbers.

Arguments

• d::Real : Order of difference, applies the n'th order of fractional difference to the series. Integer orders result in the same as the lag operator.
• cutoff::Real=1e-3 : The minimum value of a term in the binomial weights, lower values will result in a more precise result with a higher computatinal cost.

Example

julia> fracdiff([0.:10.;], 0.5)

# This results in:
11-element Vector{Float64}:
-4.1236855200362954e-16
1.0000000000000002
1.5
1.8750000000000002
2.1874999999999996
2.4609375
2.707031249999998
2.9326171874999987
3.1420898437499987
3.3384704589843737
3.523941040039061
FinanceTools.fracdiff!Function

Inplace version of the fracdiff function.

Calculates the fractional difference of a vector of numbers.

Arguments

• d::Real : Order of difference, applies the n'th order of fractional difference to the series. Integer orders result in the same as the lag operator.
• cutoff::Real=1e-3 : The minimum value of a term in the binomial weights, lower values will result in a more precise result with a higher computatinal cost.
FinanceTools.split_adjustMethod

Function rolls over a dataframe comprised of OHCLV data and finds sudden jumps in the stock price of over 50% and assumes it to be a stock split / merge that has to be corrected.

FinanceTools.trendlabelMethod

Implements continuous trend labeling according to "A Labeling Method for Financial Time Series Prediction Based on Trends" by Dingming Wu, Xiaolong Wang *, Jingyong Su, Buzhou Tang and Shaocong Wu" link : https://pdfs.semanticscholar.org/9ab2/13b22d49099256b1c98e476be2022922e8f6.pdf

Function returns buy(1)/sell(-1) signals from a series of prices.

FinanceTools.volumebarsMethod

Calculates volume bars with a constant volume per bar. Input is a dataframe in the following order: Time, Price, Volume

Output is a dataframe in the following order: Time, Open, High, Low, Close, Volume

Keyword arguments

• method : chooses the mode used to generate volume bars, "constant" is set by default and "SMA", "EMA" will be implemented later on.
• threshold : threshold for the volume accumulator, a new candle will be produced when the cumulative volume exceeds this number.
• frequency : not used
• windowlen : not used