Dark Pools and Short Data
The DPS sub-module contains the commands from the Dark Pools and Short Data menu within the OpenBB Terminal. These functions are meant to supplement other research on technical trading factors, settlement schedules, market flow, and volume dynamics. Commands are specifically for US-listed equities, and the data is reported to FINRA on a lagging schedule. Some will also have a _chart companion.
How to Use
Each function contained within the openbb.stocks.dps module is listed below with a short description.
| Path | Description |
|---|---|
| openbb.stocks.dps.ctb | Current Borrow Rates from Interactive Brokers |
| openbb.stocks.dps.dpotc | Weekly Volume Totals for a Ticker on ATS and OTC Venues |
| openbb.stocks.dps.ftd | Historical Fails-to-Deliver Numbers |
| openbb.stocks.dps.hsi | Stocks With High Reported Short Interest |
| openbb.stocks.dps.pos | Top Short Volumes From Last Trading Day |
| openbb.stocks.dps.prom | Tickers With Growing Trade Activity on ATS Tapes |
| openbb.stocks.dps.psi | Price vs. Short Volume |
| openbb.stocks.dps.shorted | Most Shorted Stocks According to Yahoo Finance |
| openbb.stocks.dps.sidtc | Short Interest and Days-to-Cover |
| openbb.stocks.dps.spos | Rolling 20-Day Net Short Volume of a Single Stock |
Examples
Import Statements
Start out the Python script or Notebook file with the familiar statements:
from openbb_terminal.sdk import openbb
import pandas as pd
CTB
openbb.stocks.dps.ctb gets the current borrow rate and availability, on Interactive Brokers, for all tickers.
ctb_df = openbb.stocks.dps.ctb()
ctb_df
| Symbol | Fees | Available | |
|---|---|---|---|
| 0 | PCF | 864.8795% | 100000 |
| 1 | EPR PRE | 625.0102% | 80000 |
| 2 | KITT | 574.4353% | 100 |
| 3 | EFSH | 439.7636% | 2000 |
| 4 | EVTL | 434.336% | 9000 |
| 17655 | DSAIF | 0.0% | 1000 |
| 17656 | DRREF | 0.0% | 25000 |
| 17657 | DOSEF | 0.0% | 100 |
| 17658 | DMYY | 0.0% | 100000 |
| 17659 | DLICY | 0.0% | 100 |
POS
openbb.stocks.dps.pos represents the cumulative net short dollar volume (Dark Pool Position $) by security. Is it calculated by taking the net short volume (short volume minus buy volume - a positive number means that the short volume was higher than the buy volume) times the closing price and taking the 20-day cumulative sum of this series. Source: Stockgrid.io
- Net Short Volume: Short volume minus buy volume.
- Net Short Volume $: Net short volume multiplied by the last close.
- Position: The sum of the net short volume over the last 20 trading days.
- Position $: Position multiplied by the last close.
Data is updated at 6pm EST.
The function returns up to 400 results, applying ascend = True unlocks the most negative values.
pos_high = openbb.stocks.dps.pos()
pos_high = pos_high.convert_dtypes()
pos_high.set_index('Date', inplace = True)
pos_low = openbb.stocks.dps.pos(ascend = True)
pos_low = pos_low.convert_dtypes()
pos_low.set_index('Date', inplace = True)
pos_df = pd.concat([pos_low,pos_high])
pos_df.sort_values(by=['Dark Pools Position $'], ascending = False, inplace = True)
pos_df
| Date | Ticker | Short Volume | Short Volume % | Net Short Volume | Net Short Volume $ | Dark Pools Position | Dark Pools Position $ |
|---|---|---|---|---|---|---|---|
| 2022-11-25 | SPY | 10511774 | 0.638082 | 4549536 | 1.83041e+09 | 109490269 | 4.27107e+10 |
| 2022-11-25 | QQQ | 2952642 | 0.490496 | -114422 | -3.283e+07 | 130622909 | 3.6231e+10 |
| 2022-11-25 | NVDA | 6264211 | 0.62147 | 2448753 | 3.98412e+08 | 61850799 | 1.00604e+10 |
| 2022-11-25 | IWM | 3932703 | 0.541717 | 605706 | 1.12407e+08 | 34431260 | 6.31318e+09 |
| 2022-11-25 | HYG | 1170511 | 0.523497 | 105076 | 7.88175e+06 | 75184813 | 5.53232e+09 |
| 2022-11-25 | META | 1652587 | 0.359614 | -1290276 | -1.4375e+08 | -88892626 | -8.17035e+09 |
| 2022-11-25 | SQQQ | 6198104 | 0.358094 | -4912392 | -2.20223e+08 | -160324410 | -8.81487e+09 |
| 2022-11-25 | MSFT | 1246644 | 0.356497 | -1003640 | -2.48391e+08 | -60211625 | -1.41156e+10 |
| 2022-11-25 | TSLA | 14145558 | 0.480631 | -1140119 | -2.08482e+08 | -106217407 | -2.06849e+10 |
| 2022-11-25 | AMZN | 9907246 | 0.467855 | -1361394 | -1.27168e+08 | -238062247 | -2.26635e+10 |
SPOS
openbb.stocks.dps.spos is a section of the same data above, but as a time-series for individual tickers. There is one-year of daily historical data available.
spy_spos = openbb.stocks.dps.spos('SPY')
spy_spos.tail(5)
| dates | Net Short Vol. (1k $) | Position (1M $) | |
|---|---|---|---|
| 4 | 2021-12-03 00:00:00 | 1218.48 | 10786 |
| 3 | 2021-12-02 00:00:00 | 3224.17 | 9567.52 |
| 2 | 2021-12-01 00:00:00 | 2558.79 | 6343.35 |
| 1 | 2021-11-30 00:00:00 | 2737.27 | 3784.56 |
| 0 | 2021-11-29 00:00:00 | 1047.29 | 1047.29 |
PSI
Price vs.Short Interest. This function has two data sources, with their own syntax. openbb.stocks.dps.psi_sg will return the other columns openbb.stocks.dps.spos is missing. The example below puts them all together.
spy_spos = openbb.stocks.dps.spos('SPY')
spy_spos.rename(columns = {'dates':'date'}, inplace= True)
spy_spos.set_index(keys = 'date', inplace =True)
spy_psi,spy_price = openbb.stocks.dps.psi_sg('SPY')
spy_psi.set_index('date', inplace = True)
spy_pos = spy_psi.join(spy_spos)
spy_pos
| date | Short Vol. [1M] | Short Vol. % | Short Exempt Vol. [1k] | Total Vol. [1M] | Net Short Vol. (1k $) | Position (1M $) |
|---|---|---|---|---|---|---|
| 2022-11-25 00:00:00 | 10.5118 | 63.81 | 37.129 | 16.474 | 1830.41 | 42710.7 |
| 2022-11-23 00:00:00 | 13.6484 | 60.55 | 91.552 | 22.5399 | 1914.28 | 42099.3 |
| 2022-11-22 00:00:00 | 10.0279 | 53.85 | 52.335 | 18.6233 | 572.874 | 42444.8 |
| 2022-11-21 00:00:00 | 9.42557 | 57.6 | 61.242 | 16.3632 | 981.734 | 42187.3 |
| 2022-11-18 00:00:00 | 18.445 | 60.68 | 112.603 | 30.3973 | 2571.31 | 42352.3 |
| 2022-02-16 00:00:00 | 11.0333 | 52.77 | 7.958 | 20.909 | 516.966 | 19836.2 |
| 2022-02-15 00:00:00 | 11.8138 | 57.01 | 36.259 | 20.7236 | 1295.52 | 21808.3 |
| 2022-02-14 00:00:00 | 12.744 | 53.08 | 50.473 | 24.0105 | 648.705 | 19105.9 |
| 2022-02-11 00:00:00 | 14.1183 | 46.63 | 55.511 | 30.2776 | -898.962 | 17553.7 |
| 2022-02-10 00:00:00 | 22.5696 | 62.98 | 46.454 | 35.834 | 4181.07 | 21316.8 |
The other version of this command is, openbb.stocks.dps.psi_q. It returns daily short volume from Nasdaq and NYSE separately, with daily historic data going back to 2013, and is sourced by Quandl. The first table below is the short volume and from Nasdaq.
nasdaq_psi = openbb.stocks.dps.psi_q(nyse=False, symbol = 'SPY')
nasdaq_psi.head(5)
| Date | ShortVolume | ShortExemptVolume | TotalVolume |
|---|---|---|---|
| 2013-04-01 00:00:00 | 8.99908e+06 | 3500 | 1.92365e+07 |
| 2013-04-02 00:00:00 | 1.15943e+07 | 6600 | 2.09414e+07 |
| 2013-04-03 00:00:00 | 1.89638e+07 | 13418 | 2.83739e+07 |
| 2013-04-04 00:00:00 | 1.29551e+07 | 7586 | 1.99661e+07 |
| 2013-04-05 00:00:00 | 1.43612e+07 | 0 | 2.81504e+07 |
The second table is from NYSE
nyse_psi = openbb.stocks.dps.psi_q(nyse=True, symbol = 'SPY')
nyse_psi.head(5)
| Date | ShortVolume | ShortExemptVolume | TotalVolume |
|---|---|---|---|
| 2013-04-01 00:00:00 | 1.04127e+06 | 3800 | 1.61224e+06 |
| 2013-04-02 00:00:00 | 1.47136e+06 | 4500 | 1.9255e+06 |
| 2013-04-03 00:00:00 | 2.43128e+06 | 0 | 3.23342e+06 |
| 2013-04-04 00:00:00 | 1.55106e+06 | 800 | 2.28563e+06 |
| 2013-04-05 00:00:00 | 1.94417e+06 | 300 | 2.93733e+06 |
Let's join them together:
nyse_psi.join(nasdaq_psi, lsuffix = ' (NYSE)', rsuffix = ' (NQ)')
| Date | ShortVolume (NYSE) | ShortExemptVolume (NYSE) | TotalVolume (NYSE) | ShortVolume (NQ) | ShortExemptVolume (NQ) | TotalVolume (NQ) |
|---|---|---|---|---|---|---|
| 2013-04-01 00:00:00 | 1.04127e+06 | 3800 | 1.61224e+06 | 8.99908e+06 | 3500 | 1.92365e+07 |
| 2013-04-02 00:00:00 | 1.47136e+06 | 4500 | 1.9255e+06 | 1.15943e+07 | 6600 | 2.09414e+07 |
| 2013-04-03 00:00:00 | 2.43128e+06 | 0 | 3.23342e+06 | 1.89638e+07 | 13418 | 2.83739e+07 |
| 2013-04-04 00:00:00 | 1.55106e+06 | 800 | 2.28563e+06 | 1.29551e+07 | 7586 | 1.99661e+07 |
| 2013-04-05 00:00:00 | 1.94417e+06 | 300 | 2.93733e+06 | 1.43612e+07 | 0 | 2.81504e+07 |
| 2022-11-21 00:00:00 | 838081 | 171 | 2.05784e+06 | 8.47984e+06 | 61071 | 1.41414e+07 |
| 2022-11-22 00:00:00 | 1.24058e+06 | 273 | 2.45227e+06 | 8.70804e+06 | 52062 | 1.60467e+07 |
| 2022-11-23 00:00:00 | 1.82258e+06 | 524 | 3.35511e+06 | 1.17129e+07 | 91028 | 1.90024e+07 |
| 2022-11-25 00:00:00 | 410318 | 353 | 804984 | 1.00677e+07 | 36776 | 1.56151e+07 |
HSI
openbb.stocks.dps.hsi lists the stocks with the highest reported short interest from NYSE or Nasdaq.
openbb.stocks.dps.hsi()
| Ticker | Company | Exchange | ShortInt | Float | Outstd | Industry | |
|---|---|---|---|---|---|---|---|
| 0 | |||||||
| 1 | BBBY | Bed Bath & Beyond Inc. | Nasdaq | 45.11% | 76.45M | 80.36M | Retail (Specialty Non-Apparel) |
| 2 | CVNA | Carvana Co | NYSE | 44.24% | 93.40M | 105.95M | Retail (Specialty Non-Apparel) |
| 3 | BYND | Beyond Meat Inc | Nasdaq | 42.75% | 57.25M | 63.74M | Food Processing |
| 4 | MSTR | MicroStrategy Inc | Nasdaq | 40.51% | 9.34M | 9.35M | Software & Programming |
| 31 | SPCE | Virgin Galactic Holdings Inc | NYSE | 20.98% | 208.85M | 274.56M | Aerospace & Defense |
| 32 | PETS | Petmed Express Inc | Nasdaq | 20.83% | 19.74M | 21.08M | Retail (Drugs) |
| 33 | RIDE | Lordstown Motors Corp | Nasdaq | 20.69% | 162.40M | 216.98M | Auto & Truck Manufacturers |
| 34 | FFIE | Faraday Future Intelligent Ele | Nasdaq | 20.67% | 242.00M | 386.26M | Auto & Truck Manufacturers |
| 35 | IGMS | IGM Biosciences Inc | Nasdaq | 20.57% | 17.31M | 29.14M | Biotechnology & Medical Research |