trade29.sc.SCBridge.get_tpo_profile#
- SCBridge.get_tpo_profile(key: str, study_id: int, number_historical_profiles: int = -1, include_profile: bool = False, include_peak_valley: bool = False) TPOProfileResponse#
Gets historical TPO profiles from the TPO Profile Chart study.
For this method to work, the Sierra Chart ‘TPO Profile Chart’ study needs to be added to the chart.
- Parameters:
- keystr
A string key that matches the key given to the target Study in SierraChart.
- study_idint
ID of the TPO study.
- number_historical_profilesint, default -1
Number of historical profiles to fetch. If this is -1 then all available profiles will be returned.
- include_profilebool, default False
Whether or not to include detailed profile data (the histogram that is seen in Sierra Chart)
- include_peak_valleybool, default False
Whether or not to include peak valley data. ‘Draw Peaks & Valleys needs to be enabled in study settings. Peaks are 1 and valleys are 2.
- Returns:
Examples
>>> from trade29.sc import SCBridge >>> bridge = SCBridge() running bridge v0.17.0--------2024/08/06 19:45:48 connecting to sc starting receiver starting sender sc version: 2666, scdx version 208, connection: t29scdx2, keys: xx,signal >>> ret = bridge.get_tpo_profile(key="xx", study_id=2, number_historical_profiles=-1) >>> df = ret.as_df() >>> # by default the request does not include detailed TPO profile data. >>> df StartDateTime NumberOfTrades Volume ... VolumeAboveTPOPOC VolumeBelowTPOPOC EndDateTime BeginIndex EndIndex 0 2024-08-06 664365 664365 ... 165638 498727 2024-08-06 20:00:00 0 8 1 2024-08-05 1000208 1000208 ... 527539 472669 2024-08-06 00:00:00 0 6 2 2024-08-04 29501 29501 ... 13003 16498 2024-08-05 00:00:00 0 2 3 2024-08-02 778346 778346 ... 221357 556989 2024-08-02 21:00:00 0 8 4 2024-08-01 646856 646856 ... 307707 339149 2024-08-02 00:00:00 0 10 5 2024-07-31 555678 555678 ... 283767 271911 2024-08-01 00:00:00 0 9 6 2024-07-30 423686 423686 ... 238377 185309 2024-07-31 00:00:00 0 8 7 2024-07-29 419518 419518 ... 159118 260400 2024-07-30 00:00:00 0 9 8 2024-07-28 16866 16866 ... 12276 4590 2024-07-29 00:00:00 0 2 9 2024-07-26 403039 403039 ... 272167 130872 2024-07-26 21:00:00 0 8 10 2024-07-25 623056 623056 ... 191987 431069 2024-07-26 00:00:00 0 10 11 2024-07-24 414961 414961 ... 231451 183510 2024-07-25 00:00:00 0 8 12 2024-07-23 340772 340772 ... 80212 260560 2024-07-24 00:00:00 0 9 13 2024-07-22 370142 370142 ... 120236 249906 2024-07-23 00:00:00 0 6 14 2024-07-21 12146 12146 ... 8181 3965 2024-07-22 00:00:00 0 2 15 2024-07-19 483542 483542 ... 406690 76852 2024-07-19 21:00:00 0 6 16 2024-07-18 432474 432474 ... 156922 275552 2024-07-19 00:00:00 0 9 >>> # we can request detailed data + peak/valley data (make sure 'Draw Peaks & Valleys' is checked in the TPO study settings) >>> ret = bridge.get_tpo_profile(key="xx", study_id=2, number_historical_profiles=-1, include_peak_valley=True, include_profile=True) >>> df = ret.as_df() >>> df StartDateTime ... PeakValleys TPO/VbP Profile 0 2024-08-06 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 1 2024-08-05 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 2 2024-08-04 ... PeakValleyLinePrice PeakValleyType StartI... Price AskVol BidVol TotalVolume ... 3 2024-08-02 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 4 2024-08-01 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 5 2024-07-31 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 6 2024-07-30 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 7 2024-07-29 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 8 2024-07-28 ... PeakValleyLinePrice PeakValleyType StartI... Price AskVol BidVol TotalVolume ... 9 2024-07-26 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 10 2024-07-25 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 11 2024-07-24 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 12 2024-07-23 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 13 2024-07-22 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 14 2024-07-21 ... PeakValleyLinePrice PeakValleyType StartI... Price AskVol BidVol TotalVolume ... 15 2024-07-19 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... 16 2024-07-18 ... PeakValleyLinePrice PeakValleyType Start... Price AskVol BidVol TotalVolume ... >>> # the peak valley/TPO profiles are their own dataframes, lets get the latest ones: >>> df["PeakValleys"][0] PeakValleyLinePrice PeakValleyType StartIndex PeakValleyExtensionChartColumnEndIndex 0 2443.699951 2 0 233 1 2444.400146 1 0 233 2 2450.800049 2 0 233 3 2451.699951 2 0 258 4 2453.000000 1 0 258 5 2455.800049 2 0 258 6 2456.400146 1 0 258 7 2457.400146 2 0 258 8 2459.400146 2 0 258 9 2461.699951 2 0 258 10 2463.400146 2 0 286 11 2464.600098 2 0 286 12 2466.100098 1 0 286 13 2470.800049 2 0 286 14 2472.199951 1 0 286 15 2473.600098 1 0 286 >>> df["TPO/VbP Profile"][0] Price AskVol BidVol TotalVolume TPOCount 368 2418.400146 5 3 8 1 367 2418.300049 23 16 39 1 366 2418.199951 27 33 60 1 365 2418.100098 69 59 128 1 364 2418.000000 76 82 158 1 .. ... ... ... ... ... .. ... ... ... ... ... 4 2382.000000 134 168 302 2 3 2381.900146 63 84 147 2 2 2381.800049 7 12 19 2 1 2381.699951 4 3 7 1 0 2381.600098 0 1 1 1
[369 rows x 5 columns]