Full Time & Sales Data Analyzer

This project is large in scope and will be separated into sections. This project has already been awarded to a freelancer who has demonstrated he can complete the project in its entirety. Part 1: Create the base script that will properly identify the correct raw data master file, filter through trade data, remove duplicates and delete previous files, for usage in the main macro. Part 2: Once all up-to-date trade data has been imported, a Net House Summary module will need to be created. This Summary must be fully flexible and fast. The user should be able to change date ranges and quickly receive all correctly calculated information. All trades must be calculated in order of date, from oldest to newest. In addition to the standard Net House Summary that is to be calculated, I would like to add these functions immediately: 1) Line graph of cumulative net house positions during selected time frame 2) A column in net house summary that calculates the percentage of trades going through the parent exchange vs the alternate exchanges (dark pools). I need to detect when the dark pools become active and receive a spike in activity. Therefore, all dark pools should be aggregated and measured against the volume being transacted through the parent exchanges that are listed as CNX, TSX, TSX-V. The relative % should be graphically displayed alongside the share price for a visual representation. 3) Each house will have a running cumulative net position from the net house summary. These will have been graphically displayed. This next step is to track major changes for each house. We need to have "alerts" for 1) New House Buyer (When a house makes its first buys) 2) New House Seller (When a house makes its first sales) 3) Accumulation Buyer (Logic is TBD ) 4) Distribution Seller (Logic is TBD) Logic will be something like tracking during what 2 week period (this should be adjustable, 1 day, 4 day, 10 day, etc) they had the highest frequency of buying and selling. 4) Separate all trades where price increases, then Count the House Buyers and determine who is pushing price up. Separate all trades where price decreases, then Count the House Seller and determine who is pushing price down. Make sure these are adjustable based on custom Begin Date and End Date. 5) Based on the cumulative house positions, calculate a matrix for correlations. Create a list for the top 3 highest positive correlations and top 3 highest negative correlations. This helps us determine if there is any potential algo trading/MMing or potential manipulation occurring. 6) Determine if MMs are active. Logic: If Delta neutral and Frequency of trades on the bid and ask are large and similar. Part 3: Include importation of shortdata file. Complete combination analysis to flag the trades that must equal the short data number. Example: during recent 2 week period, short volume activity was 550,000. Therefore, Create all possible combinations of trades that equal exactly 550,000. Record which combination required the least amount of trades to equal 550,000. Then, separate these trades and COUNT and SUM the house positions to determine who is shorting, when and why. More scope can/will be added in time and milestone achievements are available.