Plot Trading History¶
Analyze trading histories stored in S3 with this Jupyter Notebook
Plot a Trading History
dataset using seaborn and matplotlib
-
analysis_engine.plot_trading_history.
plot_trading_history
(title, df, red=None, red_color=None, red_label=None, blue=None, blue_color=None, blue_label=None, green=None, green_color=None, green_label=None, orange=None, orange_color=None, orange_label=None, date_col='minute', xlabel='Minutes', ylabel='Algo Trading History Values', linestyle='-', width=9.0, height=9.0, date_format='%d\n%b', df_filter=None, start_date=None, footnote_text=None, footnote_xpos=0.7, footnote_ypos=0.01, footnote_color='#888888', footnote_fontsize=8, scale_y=False, show_plot=True, dropna_for_all=False, verbose=False, send_plots_to_slack=False)[source]¶ Plot columns up to 4 lines from the
Trading History
datasetParameters: - title – title of the plot
- df – dataset which is
pandas.DataFrame
- red – string - column name to plot in
red_color
(or defaultae_consts.PLOT_COLORS[red]
) where the column is in thedf
and accessible with:df[red]
(default ishigh
) - red_color – hex color code to plot the data in the
df[red]
(default isae_consts.PLOT_COLORS['red']
) - red_label – optional - string for the label used
to identify the
red
line in the legend - blue – string - column name to plot in
blue_color
(or defaultae_consts.PLOT_COLORS['blue']
) where the column is in thedf
and accessible with:df[blue]
(default isclose
) - blue_color – hex color code to plot the data in the
df[blue]
(default isae_consts.PLOT_COLORS['blue']
) - blue_label – optional - string for the label used
to identify the
blue
line in the legend - green – string - column name to plot in
green_color
(or defaultae_consts.PLOT_COLORS['darkgreen']
) where the column is in thedf
and accessible with:df[green]
- green_color – hex color code to plot the data in the
df[green]
(default isae_consts.PLOT_COLORS['darkgreen']
) - green_label – optional - string for the label used
to identify the
green
line in the legend - orange – string - column name to plot in
orange_color
(or defaultae_consts.PLOT_COLORS['orange']
) where the column is in thedf
and accessible with:df[orange]
- orange_color – hex color code to plot the data in the
df[orange]
(default isae_consts.PLOT_COLORS['orange']
) - orange_label – optional - string for the label used
to identify the
orange
line in the legend - date_col – string - date column name
(default is
minute
) - xlabel – x-axis label
- ylabel – y-axis label
- linestyle – style of the plot line
- width – float - width of the image
- height – float - height of the image
- date_format – string - format for dates
- df_filter – optional - initialized
pandas.DataFrame
query for reducing thedf
records before plotting. As an eaxmpledf_filter=(df['close'] > 0.01)
would find only records in thedf
with aclose
value greater than0.01
- start_date – optional - string
datetime
for plotting only from a date formatted asYYYY-MM-DD HH\:MM\:SS
- footnote_text – optional - string footnote text
(default is
algotraders <DATE>
) - footnote_xpos – optional - float for footnote position
on the x-axies
(default is
0.75
) - footnote_ypos – optional - float for footnote position
on the y-axies
(default is
0.01
) - footnote_color – optional - string hex color code for
the footnote text
(default is
#888888
) - footnote_fontsize – optional - float footnote font size
(default is
8
) - scale_y –
optional - bool to scale the y-axis with .. code-block:: python
- use_ax.set_ylim(
- [0, use_ax.get_ylim()[1] * 3])
- show_plot – bool to show the plot
- dropna_for_all – optional - bool to toggle keep None’s in
the plot
df
(default is drop them for display purposes) - verbose – optional - bool to show logs for debugging a dataset
- send_plots_to_slack – optional - bool to send the dnn plot to slack