[et_pb_section fb_built=”1″ _builder_version=”3.22″ global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_row _builder_version=”3.25″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.5.6″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
<\/p>\n
<\/p>\n
<\/p>\n
StrategyLearner.<\/code>StrategyLearner<\/code>(<\/span>verbose=False<\/em>, impact=0.0<\/em>, commission=0.0<\/em>)<\/span><\/dt>\n- \n
A strategy learner that can learn a trading policy using the same indicators used in ManualStrategy.<\/p>\n
\n- Parameters<\/dt>\n
- \n
\n- verbose<\/strong> (bool<\/em>) \u2013 If \u201cverbose\u201d is True, your code can print out information for debugging.
If verbose = False your code should not generate ANY output.<\/li>\n- impact<\/strong> (float<\/em>) \u2013 The market impact of each transaction, defaults to 0.0<\/li>\n
- commission<\/strong> (float<\/em>) \u2013 The commission amount charged, defaults to 0.0<\/li>\n<\/ul>\n<\/dd>\n<\/dl>\n
\nadd_evidence<\/code>(<\/span>symbol=’IBM’<\/em>, sd=datetime.datetime(2008<\/em>, 1<\/em>, 1<\/em>, 0<\/em>, 0)<\/em>, ed=datetime.datetime(2009<\/em>, 1<\/em>, 1<\/em>, 0<\/em>, 0)<\/em>, sv=10000<\/em>)<\/span><\/dt>\n- \n
Trains your strategy learner over a given time frame.<\/p>\n
\n- Parameters<\/dt>\n
- \n
\n- symbol<\/strong> (str<\/em>) \u2013 The stock symbol to train on<\/li>\n
- sd<\/strong> (datetime<\/em>) \u2013 A datetime object that represents the start date, defaults to 1\/1\/2008<\/li>\n
- ed<\/strong> (datetime<\/em>) \u2013 A datetime object that represents the end date, defaults to 1\/1\/2009<\/li>\n
- sv<\/strong> (int<\/em>) \u2013 The starting value of the portfolio<\/li>\n<\/ul>\n<\/dd>\n<\/dl>\n<\/dd>\n<\/dl>\n
\ntestPolicy<\/code>(<\/span>symbol=’IBM’<\/em>, sd=datetime.datetime(2009<\/em>, 1<\/em>, 1<\/em>, 0<\/em>, 0)<\/em>, ed=datetime.datetime(2010<\/em>, 1<\/em>, 1<\/em>, 0<\/em>, 0)<\/em>, sv=10000<\/em>)<\/span><\/dt>\n- \n
Tests your learner using data outside of the training data<\/p>\n
\n- Parameters<\/dt>\n
- \n
\n- symbol<\/strong> (str<\/em>) \u2013 The stock symbol that you trained on on<\/li>\n
- sd<\/strong> (datetime<\/em>) \u2013 A datetime object that represents the start date, defaults to 1\/1\/2008<\/li>\n
- ed<\/strong> (datetime<\/em>) \u2013 A datetime object that represents the end date, defaults to 1\/1\/2009<\/li>\n
- sv<\/strong> (int<\/em>) \u2013 The starting value of the portfolio<\/li>\n<\/ul>\n<\/dd>\n
- Returns<\/dt>\n
- A DataFrame with values representing trades for each day. Legal values are +1000.0 indicating
a BUY of 1000 shares, -1000.0 indicating a SELL of 1000 shares, and 0.0 indicating NOTHING.
Values of +2000 and -2000 for trades are also legal when switching from long to short or short to
long so long as net holdings are constrained to -1000, 0, and 1000.<\/dd>\n - Return type<\/dt>\n
- \n
pandas.DataFrame<\/p>\n<\/dd>\n<\/dl>\n<\/dd>\n<\/dl>\n<\/dd>\n<\/dl>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
<\/p>\n
<\/p>\n
<\/div>\n<\/p>\n
<\/p>\n