[vc_row el_id=”Intro”][vc_column offset=”vc_col-lg-2 vc_col-md-1″][/vc_column][vc_column offset=”vc_col-lg-8 vc_col-md-10″ css=”.vc_custom_1532967194912{padding-right: 10px !important;padding-left: 10px !important;}”][ultimate_spacer height=”60″ height_on_tabs=”10″ height_on_tabs_portrait=”10″ height_on_mob_landscape=”10″ height_on_mob=”10″][ult_animation_block animation=”zoomIn” animation_duration=”0.6″ animation_delay=”0″ animation_iteration_count=”1″][arrowpress_heading small_heading_title=”Algorithms” big_heading_title=”Lysander” tag_heading=”tag-h1″ animation_type=”zoomIn” animation_delay=”500″][/ult_animation_block][ultimate_spacer height=”19″ height_on_tabs_portrait=”0″ height_on_mob_landscape=”40″ height_on_mob=”40″][ult_animation_block animation=”fadeInDown” animation_duration=”0.6″ animation_delay=”0″ animation_iteration_count=”1″][vc_column_text css=”.vc_custom_1549319691848{margin-top: 0px !important;margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

The Lysander algorithm focuses on automatically investing in stocks based mainly on fundamental metrics and a secondary technical-based parameter. Lysander scans the entire stock universe and narrows down a list of the top 50 stocks based on favorable ratios calculated on metrics such as price, earnings, EBITDA, debt, equity, and enterprise value. In addition, the algorithm further narrows out small-cap stocks to help reduce volatility and to ensure availability of accurate fundamental data. As fundamental data is only updated quarterly, a secondary technical indicator based on past price action and volume are used to re-balance the portfolio in the interim.

Lysander is re-balanced on a weekly basis based on releases of new fundamental data and the secondary technical indicator. A risk management strategy is implemented to allocate more capital to a low volatility bond fund automatically when market volatility increases.

[/vc_column_text][/ult_animation_block][ultimate_spacer height=”30″ height_on_tabs=”10″ height_on_tabs_portrait=”10″ height_on_mob_landscape=”10″ height_on_mob=”10″][ult_animation_block animation=”fadeInDown” animation_duration=”0.6″ animation_delay=”0″ animation_iteration_count=”1″][vc_column_text css=”.vc_custom_1548997024300{margin-top: 0px !important;margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Current Status

[/vc_column_text][/ult_animation_block][ultimate_spacer height=”10″ height_on_tabs=”5″ height_on_tabs_portrait=”5″ height_on_mob_landscape=”5″ height_on_mob=”5″][vc_single_image image=”1929″ img_size=”full” alignment=”center”][/vc_column][vc_column offset=”vc_col-lg-2 vc_col-md-1″][/vc_column][/vc_row][vc_row el_id=”Algo-Performance”][vc_column][ultimate_spacer height=”35″ height_on_tabs=”15″ height_on_tabs_portrait=”15″ height_on_mob_landscape=”15″ height_on_mob=”15″][ult_animation_block animation=”zoomIn” animation_duration=”0.6″ animation_delay=”0″ animation_iteration_count=”1″][arrowpress_heading big_heading_title=”Algorithm Performance” tag_heading=”tag-h1″ animation_delay=”500″ color_desc=”#ffffff”][/ult_animation_block][ultimate_spacer height=”15″ height_on_tabs=”5″ height_on_tabs_portrait=”5″ height_on_mob_landscape=”5″ height_on_mob=”5″][vc_row_inner][vc_column_inner width=”1/12″][/vc_column_inner][vc_column_inner width=”5/6″][vc_column_text css=”.vc_custom_1548996350080{margin-top: 0px !important;margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Backtest Parameters

Timeframe: January 1st, 2007 – January 15, 2019
Initial Capital: $100,000

[/vc_column_text][ultimate_spacer height=”19″ height_on_tabs_portrait=”0″ height_on_mob_landscape=”40″ height_on_mob=”40″][vc_column_text]

[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/12″][/vc_column_inner][/vc_row_inner][vc_row_inner][vc_column_inner width=”1/6″][/vc_column_inner][vc_column_inner width=”2/3″][ultimate_spacer height=”30″ height_on_tabs_portrait=”0″ height_on_mob_landscape=”15″ height_on_mob=”10″][arrowpress_heading big_heading_title=”Preliminary Results” tag_heading=”tag-h1″ animation_delay=”500″ color_desc=”#ffffff”][vc_column_text][ultimatetables 2 /][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/6″][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row el_id=”Next-Steps”][vc_column width=”1/6″][/vc_column][vc_column width=”2/3″][ultimate_spacer height=”40″ height_on_tabs=”15″ height_on_tabs_portrait=”15″ height_on_mob_landscape=”15″ height_on_mob=”15″][arrowpress_heading big_heading_title=”Next Steps” tag_heading=”tag-h1″ animation_delay=”500″ color_desc=”#ffffff”][ultimate_spacer height=”15″ height_on_tabs=”5″ height_on_tabs_portrait=”5″ height_on_mob_landscape=”5″ height_on_mob=”5″][vc_column_text css=”.vc_custom_1549321008782{margin-top: 0px !important;margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Lysander incorporates a few aspects of the Actium algorithm and preliminary results show a strong win rate with high returns and strong profit-loss ratio. More research needs to be completed into which fundamental factors indicate low volatility and growth in near-term timeframes. Additional research also needs to be completed in optimizing which low market correlated ETFs perform best as a hedge for the overall portfolio. These efforts look to increase the overall risk-adjusted return metrics (Sharpe, Information, and Treynor ratios), and lower the overall portfolio drawdown.

Backtesting will continue as algorithm factor research continues and will be moved into out-of-sample testing once preliminary results become satisfactory. This step is especially important to avoid curve-fitting and to ensure the algorithm performs in all market environments.

[/vc_column_text][ultimate_spacer height=”20″ height_on_tabs=”5″ height_on_tabs_portrait=”5″ height_on_mob_landscape=”5″ height_on_mob=”5″][vc_btn title=”Return to Home” shape=”square” color=”blue” align=”center” i_icon_fontawesome=”fa fa-arrow-left” add_icon=”true” link=”url:https%3A%2F%2Fquantumdensity.com|||”][ultimate_spacer height=”60″ height_on_tabs_portrait=”10″ height_on_mob_landscape=”10″ height_on_mob=”10″][/vc_column][vc_column width=”1/6″][/vc_column][/vc_row]