Scanner Queries
Scanner Queries description
A Scanner query is a JSON schema query with the following properties.
Property | Type | Description | Expected values | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
instrumentCategory* | String | The configuration includes one category | instrumentCategory: or instrumentCategory: | |||||||||
datapoints | Array | Requested datapoints. In further query schema parts, a specific index references each datapoint as defined in this property | See ScanQuery.Datapoints for details | |||||||||
selectors | Array | JSON with select and value properties | See ScanQuery.Selectors for details | |||||||||
filters | Array | Filters applied to the query | See ScanQuery.Filters for details | |||||||||
sorters | Array | Sorting criteria applied to the query. It is represented as a set of sorters, where each sorter references a datapoint by its 0-based index as defined by the datapoints' value. The default order is descending or ascending if the reversed option is set to true
| The value format is: "sorters": [
{
"datapoint": 7,
"reversed": false
}
], | |||||||||
outputs | Array | Datapoint indices to include in the output | The value format is: "outputs": [
{
"datapoint": 0
},
{
"datapoint": 1
}
] | |||||||||
options | Array | Additional query options. Currently, Scanner API supports only one option - snapshotSize integer($int32), which specifies the maximum results to be returned | The value format is: "options": {
"snapshotSize": 1000
} |
ScanQuery.Datapoints
Each datapoint consists of two parts, a datapoint name (name) and a datapoint expression (expr). dxFeed Scanner supports 150+ pre-built datapoints; here is the full list. To build custom datapoints using functions and pre-built datapoints, you can use dxScript expressions.
Please routinely check the supported pre-built datapoints list here and the /docs/datapoints/ endpoint since new Scanner releases sometimes result in datapoint updates.
Datapoint parameters
The dxFeed Scanner technical indicators support parametrized calculation. A user can specify candleCount, candlePeriod, timePeriod and session parameters to display its calculation.
candlePeriod parameter defines candle type for calculations using:
1m - for 1-minute candle
2m - for 2-minute candle
3m - for 3-minute candle
4m - for 4-minute candles
5m - for 5-minute candles
10m - for 10-minute candle
15m - for 15-minute candle
30m - for 30-minute candle
1h - for 1-hour candle
2h - for 2-hour candle
4h - for 4-hour candle
1d - for 1-day candle
1w - for 1-week candle
1mo - for 1-month candle
1y - for 1-year candle
candleCount parameter defines the exact candle number used for calculation.
session parameter defines the session types used for calculations:
all (default if the parameter is not specified) - regular, pre-market, and post-market
regular - only regular
timePeriod parameter defines the calendar period for the candles' number restrictions:
1d - all fully finished candles in the defined number of calendar days will be taken, any number of calendar days can be defined
1w - all fully finished candles in the defined number of calendar weeks will be taken, any number of weeks can be defined
1mo - all fully finished candles in the defined number of calendar months will be taken, any number of months can be defined
1y - all fully finished candles in the defined number of calendar years will be taken, any number of years can be defined
ytd - all fully finished candles between the start of the year and the requested date will be taken
Example. Ratio between ATM IV and statistical volatility for a month
To get the ratio between ATM Implied Volatility and statistical volatility for one month, use the following datapoint:
{
"name": "RATIO_IV_TO_SV_1_MONTH",
"expr": "underlying.atmIv30Day / underlying.statVol1Month(session=\"regular\")"
}Example. Total call volume ratio to the average for 10 days
To calculate the options session call volume ratio to the 10 days average, use the following datapoint:
{
"name": "VOLUME_TOTAL_CALL_RATIO",
"expr": "underlying.optionsCallVolume(session=\"regular\") / underlying.optionsCallVolumeAvg10Day(session=\"regular\")"
}ScanQuery.Selectors
Note
This section is obsolete. Please refer to it for backward compatibility only. Please refer to Appendix B, Migrate selectors to filters for details on how to implement functionality using filters.
This function is deprecated, relevant features shall be now implemented with ScanQuery.Filters functionality. See also Appendix A, Special functions.
Scanner API optimized the selectors to narrow down the set of symbols on which the search is performed. You can use type (e.g. OPTION, ETF, STOCK), symbol (e.g. AAPL, FB, .AMZN190628C1300, etc.), and underlying for derivatives (AAPL, FB) to limit the set of instruments you work with.
Property | Type | Description | Expected values |
|---|---|---|---|
select* | string | Selector type |
|
values* | array | Selector values to apply | Depending on selector type and the symbol universe dxFeed Scanner works with |
Example. Selecting options only
"selectors": [
{
"select": "type",
"values": [
"OPTION"
]
}
]ScanQuery.Filters
Filters are search criteria built on datapoints. In each filter, you can define a datapoint and criteria (alternatives property) that the datapoint shall match.
Combine several alternatives using
ORoperation for the selected datapoint to meet any alternativeFilters themselves are combined using
ANDoperation for the selected symbol datapoint to meet all alternatives.
Property | Type | Description | ||||||
|---|---|---|---|---|---|---|---|---|
datapoint* | integer($int32) | Datapoint index in datapoint array (0-based). If this is the only provided filter property, then the specified datapoint must have a boolean type | ||||||
not | Boolean | Whether the filter should be negated. False by default | ||||||
alternatives | Array | Alternatives for the filtered datapoint:
|
Example. Filtering a datapoint to be greater than 1.5
"filters": [
{
"datapoint": 7,
"alternatives": [
{
"predicate": ">=",
"args": [
1.5
]
}
]
}
]