ohlcv

Set of functions and classes used to handle data with Open, High, Low, Close and Volume

Plotting OHLC data

Functions to plot times series in OHLC format (Open, High, Low, Close) and OHLCV format (same + volume).

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candlestick_plot

 candlestick_plot (df:pandas.core.frame.DataFrame, width:int=950,
                   height:int=600, chart_title:str='',
                   fig:bokeh.plotting._figure.figure|None=None)

Create a candlestick chart (Bokeh) using a dataframe with ‘Open’, ‘High’, ‘Low’, ‘Close’, ‘Volume’.

Type Default Details
df DataFrame df with datetime index, and at least following columns ‘Open’, ‘High’, ‘Low’, ‘Close’, ‘Volume’
width int 950 height of the plot figure
height int 600 height of the plot figure
chart_title str title of the chart
fig bokeh.plotting._figure.figure | None None figure to allow superposition of other lines on candlestick plot
Returns None

Before using the function in a notebook, you must load BokehJS, with:

output_notebook()
Loading BokehJS ...

Let’s load a test DataFrame and plot it.

df = load_test_df()
display(df.head(10))
candlestick_plot(df.head(10), width=800, height=400, chart_title='Candlestick plot')
Open High Low Close Volume
dt
2018-10-22 2759.02 2779.27 2747.27 2754.48 26562
2018-10-23 2753.11 2755.36 2690.69 2743.45 38777
2018-10-24 2744.83 2748.58 2651.23 2672.80 41777
2018-10-25 2670.80 2722.90 2657.93 2680.71 39034
2018-10-26 2675.59 2692.34 2627.59 2663.57 61436
2018-10-29 2667.70 2707.00 2603.33 2639.17 44960
2018-10-30 2639.55 2689.50 2633.05 2688.50 52786
2018-10-31 2688.88 2736.76 2681.25 2704.75 32374
2018-11-01 2707.13 2741.58 2706.88 2731.90 29565
2018-11-02 2725.28 2766.28 2699.96 2723.76 41892

Note that the plot uses a full DateTimeIndex x axis, and the missing bars will just be empty. This allows to compare plots for time series with different missing bars

Handling OLHCV data

Function performing transformation and analysis on OLHCV data.

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resample_ohlcv

 resample_ohlcv (df:pandas.core.frame.DataFrame, rule_str='W-FRI')

*Resample a DataFrame with OHLCV format according to given rule string.

The re-sampling is applied to each of the OHLC and optional V column. Re-sampling aggregate applies first(), max(), min(), last() and sum() to OHLCV respectively.*

Type Default Details
df DataFrame df with datetime index, and at least following columns ‘Open’, ‘High’, ‘Low’, ‘Close’. Optional ‘Volume’
rule_str str W-FRI DateOffset alias for resampling. Default: ‘W-FRI’. Other commons: ‘D’, ‘B’, ‘W’, ‘M’
Returns DataFrame resampled df with columns ‘Open’, ‘High’, ‘Low’, ‘Close’. Optional ‘Volume’

The list of all DateOffset string reference can be found in Pandas’ documentation here.

The test df has one bar (row) for each day. We can resample to aggregate data per week, where one week ends on Friday.

df_wk = resample_ohlcv(df, rule_str='W-FRI')
df_wk.head(5)
Open High Low Close Volume
W-FRI
2018-10-26 2759.02 2779.27 2627.59 2663.57 207586
2018-11-02 2667.70 2766.28 2603.33 2723.76 201577
2018-11-09 2721.51 2817.01 2713.14 2778.60 118857
2018-11-16 2777.10 2794.23 2669.14 2740.15 170290
2018-11-23 2732.15 2746.53 2625.66 2630.36 132017 
print('Days of the week for initial df:',list(df.index.day_of_week[:5]))
print('Days of the week for sampled df:',list(df_wk.index.day_of_week[:5]))
Days of the week for initial df: [0, 1, 2, 3, 4]
Days of the week for sampled df: [4, 4, 4, 4, 4]

source

autocorrelation_ohlcv

 autocorrelation_ohlcv (df:pandas.core.frame.DataFrame, max_lag:int=10,
                        ohlc_col:str='Close')

Return autocorrelation for a range of lags and for the selected ohlc_col_col defined.

Type Default Details
df DataFrame df with DateTimeIndex, with Open, High, Low, Close
max_lag int 10 Maximum lag to consider for the autocorrelation
ohlc_col str Close Columns to use for the autocorrelation. Default: ‘Close’. Options: ‘Open’, ‘High’, ‘Low’, ‘Close’
Returns Series 
autocorrelation_ohlcv(df, max_lag=5, ohlc_col='Open')
1    0.948029
2    0.884840
3    0.826346
4    0.764883
5    0.713426
Name: Autocorrelation, dtype: float64