How to optimize billion time series data collection

I have a time series collection that contains nearly one billion data.

I simply running:

db.cllection.find({'metaData.eventId': "FB-2348", 'metaData.marketName': "Match Odds", 'metaData.selectionId': 0})

The query cost more than 2mins to finish

"executionStats":{
               "executionSuccess":true,
               "nReturned":10,
               "executionTimeMillis":207968,
               "totalKeysExamined":0,
               "totalDocsExamined":5032348,
               "executionStages":{
                  "stage":"COLLSCAN",
                  "filter":{
                     "$and":[
                        {
                           "meta.eventId":{
                              "$eq":"FB-2348"
                           }
                        },
                        {
                           "meta.marketName":{
                              "$eq":"Match Odds"
                           }
                        },
                        {
                           "meta.selectionId":{
                              "$eq":0
                           }
                        }
                     ]
                  },
                  "nReturned":10,
                  "executionTimeMillisEstimate":200392,
                  "works":5032349,
                  "advanced":10,
                  "needTime":5032338,
                  "needYield":0,
                  "saveState":13268,
                  "restoreState":13268,
                  "isEOF":1,
                  "direction":"forward",
                  "docsExamined":5032348
               },

I have billion data looks like this:

{
ts: 2024-04-05T07:50:53.111+00:00
metaData: {
    betType: "for,ahover,18",
    bookie: "pin88",
    eventId:"2024-04-07,1514,1154",
    inRunning:false,
    sport:"FB"
},
min:null,
price:5.03,
max:118.6643
}

Is there anything wrong with my query? Or is there anything I can optimize in my collection/schema design/data configuration? Thank you.