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Data Storage and Graph Display

$250-750 USD

Cerrado
Publicado hace alrededor de 14 años

$250-750 USD

Pagado a la entrega
Job is pretty simple, I just thought it would be better if I wrote detailed requirement. Use Django, Python and PostgreSQL to manage and display these scripts, please. I will give you access to the hosting account. The directory needs to be password protected. All terms and calculations can be explained in more detail if required. Job split into 3 tasks - 1) loading data, from zip files, into tables, regular collection of data to add to tables 2) display particular symbol data and graphs (use google charts api) 3) scans - avgs, highs and lows from the data generated in part 2 Terms: Least valuable strike (LVS) - strike price which has the lowest value for all stock options sold - simple calculation explain here: [login to view URL] - I can provide more information/clarification too Least valuable pice (LVP) - actual stock price which has the lowest value for all stock options sold - very similar to above but uses prices not specific strike values. Again, I will explain in more detail, if necessary. Expiry - 3rd saturday of each month, expiry of each options symbol is listed in options_date files 1) Database Tables: Create 3 tables, option_stats, options_data, stock_data. Using the columns in the example files (attached). All dates should be changed to timestamps. Past data for these tables will be on the server, subsequent data will need to be collected, unzipped and loaded into the database. Url, username and password for the collection will be provided. There is data from 2009 onwards for options, from 5 years for stocks. Using info in options_data a script should store all symbols which trade above X thousand contracts for each of the last X months (in a separate table). This list only increases in size, so no symbol can be removed on the basis of losing volume but a symbol can be manually removed or added (separate lists for manually removed/added). This list is referred to as: heavily traded options (HTO). Needs to be a page displaying these lists, the symbols displayed will link to the particular symbol page. 2) Symbol page Data for each symbol should be calculated each day and stored in a database table Least valuable strike/price: Graph & table For each month and time period: 4 time periods: all time, since last expiry, last 10 days, last 20 days Bar chart 1: 6 strikes either side of lowest value - each bar is options value at expiry, y axis data for each strike: %diff from lowest value and strike value Bar chart 2: as Bar Chart 1 but with each bar split into calls and puts. Calls under puts. Line graphs: i) y axis: days we have data for option to present - 30,60,120,365 days x axis: strike prices lines: price, 5/10/20 day avg price, LVS, LVP ii) Time period line graph % difference of price from LVS/LVP - 30,60,120,365 days, 60 default iii) as 2 but % difference above and below from LVS/LVP iv) stock's historical volatility, options implied volatility. implied volatility is in the options_date files for each option, historical volatility is calculated as thus: [login to view URL] more clarification available if needed. historical volatility will be calculated across 10/20/60/120/180/360 daily time periods Implied volatility will be 30/60/120/180/360 Table 1: columns: value of strikes, %diff from lowest value strike & price (today, yesterday, 3 days ago, 5, 10, 20), %change of diff (same time periods) rows: all strike prices Table 2: columns: % proximity to LVP at expiry, %proximity to LVS at expiry, Number of strikes from LVS at expiry, Avg for %proximity for last 3/6/9/12 months rows: last 24 months Table 3: columns: %proximity to any strike at expiry, Avg for %proximity to any strike for last 3/6/9/12 months rows: last 24 months Table 4: Rank vertical short payments % : info on vertical spreads: [login to view URL] Table 5: Rank butterfly return % : info [login to view URL](options) 3) Scans Below tables and graphs for each month and time period: 4 time periods: all time, since last expiry, last 10 days, last 20 days Table 1: Top 20 HTO %diff from LVS, ordered high to low rows: symbols columns: % diff from from LVS Table 2: as Table one but for LVP Table 3: Top 20 HTO - vertical short payment % (formula provided) x %distance from LVS & LVP If current price is > LVS, check calls, if current price < LVS check puts - ranked high to low. column: score row: symbol & vertical short Table 4: Top 20 HTO - butterfly return (formula provided), LVS/LVP column: score row: symbol and butterfly Table 5: Avg % distance from LVS/LVP Top 20 best and worst - last month, last 3/9/12 months Table 6: Top 50 HTO closest to LVS/LVP at expiry for last 3/9/12 months Table 7: stocks reaching implied volatility lows, implied volatility highs - 4 week, 12week, 52 week Bar chart: Avg % value diff for HTO strike either side of LVS: X axis: strike -6 from LVS to +6 Y axis: %diff from strike Line Chart: Avg % diff of all HTO from LVS/LVP last 12 months X axis - days, with marker at opex Y axis - %diff
ID del proyecto: 642632

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4 propuestas
Proyecto remoto
Activo hace 14 años

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4 freelancers están ofertando un promedio de $620 USD por este trabajo
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we need 7 days to complete the project.
$550 USD en 7 días
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If this project is to be built from scratch then I would recommend running Google App Engine which includes Django 0.96.1, and also using the GAE Datastore API and the WSGI Handler. My father is a stock analyst so he can probably help me with the calculation details.
$750 USD en 14 días
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Nothing to declare.
$700 USD en 30 días
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Bandera de UNITED STATES
Palo Alto, United States
5,0
12
Miembro desde mar 24, 2010

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