This article provides demonstrate how to analyze an investment portfolio beyond the trading universe of ALGOGENE.

• import custom data files
• specify user-defined instrument and file formats
• add new trading instruments for portfolio analysis/optimization

Data Preparation

Suppose we want to build a portfolio for several stocks that are currently not available on ALGOGENE. We downloaded the market data from Yahoo Finance.

For example, we got the daily prices in 2020 for

• 0005.HK, the stock price of HSBC listed on HKEx (https://finance.yahoo.com/quote/0005.HK/history?p=0005.HK)
• 0939.HK, the stock price of China Construction Bank listed on HKEx (https://finance.yahoo.com/quote/0939.HK/history?p=0939.HK)

The downloaded files from Yahoo Finance are in csv format. When we open the files using Notepad or other plain text editors, we can see the data structure as follows:

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Date,Open,High,Low,Close,Adj Close,Volume
2020-01-02,60.849998,60.950001,60.599998,60.900002,60.479397,14629077
2020-01-03,60.900002,61.200001,60.250000,60.400002,59.982849,14419537
2020-01-06,60.099998,60.400002,59.799999,60.000000,59.585613,13809308
2020-01-07,60.200001,60.299999,59.799999,59.900002,59.486305,8818594
2020-01-08,59.299999,59.400002,58.849998,59.299999,58.890450,16826669
2020-01-09,59.549999,59.900002,59.549999,59.849998,59.436649,17802374
2020-01-10,59.950001,60.049999,59.750000,59.849998,59.436649,19011475
2020-01-13,59.500000,60.299999,59.500000,59.950001,59.535961,40492594
...

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Date,Open,High,Low,Close,Adj Close,Volume
2020-01-02,6.730000,6.830000,6.730000,6.800000,6.420695,239292513
2020-01-03,6.830000,6.850000,6.720000,6.720000,6.345157,277420033
2020-01-06,6.700000,6.710000,6.580000,6.650000,6.279061,260518248
2020-01-07,6.640000,6.700000,6.600000,6.610000,6.241293,204806676
2020-01-08,6.540000,6.590000,6.480000,6.560000,6.194082,267421237
2020-01-09,6.650000,6.710000,6.600000,6.670000,6.297946,215859440
2020-01-10,6.730000,6.750000,6.680000,6.720000,6.345157,223748680
2020-01-13,6.740000,6.830000,6.740000,6.800000,6.420695,481124394
...

As we can see, the files contains 7 columns in total and structured below.

Data Import

Now, let's import our data files as follows:

• After login the portal, go to [My History] > [Custom File Viewer]
• Select '/data' directory, then upload data files


• We can then 'Edit' to view the uploaded content


• Now, we need to create a meta file '_meta_.json' to instruct the system how to process the data files.
• '_meta_.json' is in JSON format where we can specify the instrument name in the first key
• in this example, we label them as 'HSBC' and 'CCB' respectively
• it should be noticed that our specified name has to be distinct from ALGOGENE's existing instruments. Otherwise, the system will skip processing our data files.
• the second key of '_meta_.json' should contain the following
• 'file': the file name on the cloud directory
• 'file_delimiter': the file delimiter used in a data file
• 'period_start': the starting date of a data file, in the format of YYYY-MM-DD
• 'period_end': the end date of a data file, in the format of YYYY-MM-DD
• 'settleCurrency': the settlement currency of the instrument (it is HKD in our example)
• 'contractSize': the number of share per each lot of the instrument
• 'fmt_time': specified the date format in a data file, in Python date encoding
• %Y: the year in four-digit format, eg. "2018"
• %y: the year in two-digit format, that is, without the century. For example, "18" instead of "2018"
• %m: the month in 2-digit format, from 01 to 12
• %b: the first three characters of the month name. eg. "Sep"
• %d: day of the month, from 1 to 31
• %H: the hour, from 0 to 23
• %M: the minute, from 00 to 59
• %S: the second, from 00 to 59
• %f: the microsecond from 000000 to 999999
• %Z: the timezone
• %z: UTC offset
• %j: the number of the day in the year, from 001 to 366
• %W: the week number of the year, from 00 to 53, with Monday being counted as the first day of the week
• %U: the week number of the year, from 00 to 53, with Sunday counted as the first day of each week
• %a: the first three characters of the weekday, e.g. Wed
• %A: the full name of the weekday, e.g. Wednesday
• %B: the full name of the month, e.g. September
• %w: the weekday as a number, from 0 to 6, with Sunday being 0
• %p: AM/PM for time
• 'col_time': the column position of data date (first column index = 0)
• 'col_open': the column position of open price (first column index = 0)
• 'col_high': the column position of high price (first column index = 0)
• 'col_low': the column position of low price (first column index = 0)
• 'col_close': the column position of closing price (first column index = 0)
• 'col_volume': the column position of volume (first column index = 0)

The sample meta file used in the example can be copied here:

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{
   "HSBC": {
       "file": "0005.HK.csv",
       "file_delimiter": ",",
       "period_start": "2020-01-01",
       "period_end": "2020-12-31",
       "settleCurrency": "HKD",
       "contractSize": 400,
       "fmt_time": "%Y-%m-%d",
       "col_time": 0,
       "col_open": 1,
       "col_high": 2,
       "col_low": 3,
       "col_close": 5,
       "col_volume": 6
   },
   "CCB": {
       "file": "0939.HK.csv",
       "file_delimiter": ",",
       "period_start": "2020-01-01",
       "period_end": "2020-12-31",
       "settleCurrency": "HKD",
       "contractSize": 1000,
       "fmt_time": "%Y-%m-%d",
       "col_time": 0,
       "col_open": 1,
       "col_high": 2,
       "col_low": 3,
       "col_close": 5,
       "col_volume": 6
   }
}

Portfolio Optimization

After we properly setup the '_meta_.json', we can then include our custom instruments for portfolio analysis.

• Go to [Market Analysis] > [Portfolio Optimizer]


• Choose *My Instrument* under asset class
• Then you will see our defined symbol "HSBC" and "CCB"
• Now you can conduct portfolio analysis as in previous article