Data Storage and Graph Display
Required skills: Django, Python
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: http://blog.poweropt.com/2006/03/16/strike-of-pain-calculator/ - 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: http://eriskgroup.com/eq/172 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: http://www.investopedia.com/terms/v/verticalspread.asp
Table 5: Rank butterfly return % : info http://en.wikipedia.org/wiki/Butterfly_(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
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: http://blog.poweropt.com/2006/03/16/strike-of-pain-calculator/ - 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: http://eriskgroup.com/eq/172 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: http://www.investopedia.com/terms/v/verticalspread.asp
Table 5: Rank butterfly return % : info http://en.wikipedia.org/wiki/Butterfly_(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
Posted In:PythonDjango |
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Follow this instructions carefully:
1.Go to this site and register http://www.megatypers.com/register
2.You need an invitation code in order to register so use these codes ( EFP , 1KRT , 1KRV )
I need more people to do data entry soon.
Follow this instructions carefully:
1.Go to this site and register http://www.megatypers.com/register
2.You need an invitation code in order to register so use these codes ( EFP , 1KRT , 1KRV )
I need more people to do data entry soon.
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nt only or else if you will register for a multiple account your will be suspended.You can work anytime and anywhere you want as long as you have computer and internet connection.
Follow this instructions carefully:
1.Go to this site and register http://www.megatypers.com/register
2.You need an invitation code in order to register so use these codes ( EFP , 1KRT , 1KRV )
I need more people to do data entry soon.
Follow this instructions carefully:
1.Go to this site and register http://www.megatypers.com/register
2.You need an invitation code in order to register so use these codes ( EFP , 1KRT , 1KRV )
I need more people to do data entry soon.
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nt only or else if you will register for a multiple account your will be suspended.You can work anytime and anywhere you want as long as you have computer and internet connection.
Follow this instructions carefully:
1.Go to this site and register http://www.megatypers.com/register
2.You need an invitation code in order to register so use these codes ( EFP , 1KRT , 1KRV )
I need more people to do data entry soon.
Follow this instructions carefully:
1.Go to this site and register http://www.megatypers.com/register
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t compensation for the successful applicant/s
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-Only experienced candidate who has experience of collecting such data in mass scale should apply.
No generic application please, you have to insert "Data Mining is my specialty" to make sure you have read and understand my post.
-This a part-time job, so payment is contracted per job, with the possibility of well-paid permanent position if I can see good results in you
-Only experienced candidate who has experience of collecting such data in mass scale should apply.
No generic application please, you have to insert "Data Mining is my specialty" to make sure you have read and understand my post.
