The Discovery Module contains lists of companies, cryptocurrencies, forex, commodities, etfs and indices including screeners, quotes, performance metrics and more to find and select tickers to use in the Finance Toolkit.

To install the FinanceToolkit it simply requires the following:

pip install financetoolkit -U

If you are looking for documentation regarding the toolkit, ratios, models, technicals, fixed income, risk, performance and economics, please have a look below:

Toolkit Discovery Ratios Models Options Technicals Fixed Income Risk Performance Economics

init

Initializes the Discovery Controller Class.

Args:

As an example: 

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

stock_list = discovery.get_stock_list()

# The total list equals over 60.000 rows
stock_list.iloc[48000:48010]

Which returns:

Symbol Name Price Exchange Exchange Code
RBL.AX Redbubble Limited 0.54 Australian Securities Exchange ASX
RBL.BO Rane Brake Lining Limited 870.05 Bombay Stock Exchange BSE
RBL.NS Rane Brake Lining Limited 870.05 National Stock Exchange of India NSE
RBLAY Robinsons Land Corporation 4.61 Other OTC PNK
RBLBANK.BO RBL Bank Limited 280.9 Bombay Stock Exchange BSE
RBLBANK.NS RBL Bank Limited 280.9 National Stock Exchange of India NSE
RBLN-B.CO Roblon A/S 91.8 Copenhagen CPH
RBLX Roblox Corporation 45.72 New York Stock Exchange NYSE
RBMNF Rugby Resources Ltd. 0.065 Other OTC PNK
RBMS.JK PT Ristia Bintang Mahkotasejati Tbk 50 Jakarta Stock Exchange JKT

search_instruments

The search instruments function allows you to search for a company or financial instrument by name. It returns a dataframe with all the symbols that match the query.

Args:

  • query (str): A query to search for, e.g. ‘META’.

Returns: pd.DataFrame: A dataframe with all the symbols that match the query.

As an example: 

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

discovery.search_instruments(query='META')

Which returns:

Symbol Name Currency Exchange Exchange Code
META Meta Platforms, Inc. USD NASDAQ Global Select NASDAQ
META.L WisdomTree Industrial Metals Enhanced USD London Stock Exchange LSE
METAUSD Metadium USD USD CCC CRYPTO
META.MI WisdomTree Industrial Metals Enhanced EUR Milan MIL
META.JK PT Nusantara Infrastructure Tbk IDR Jakarta Stock Exchange JKT

get_stock_screener

Screen stocks based on a set of criteria. This can be useful to find companies that match a specific criteria or your analysis. Further filtering can be done by utilising the Finance Toolkit and calculating the relevant ratios to filter by. This can be:

  • Market capitalization (market_cap_higher, market_cap_lower)
  • Price (price_higher, price_lower)
  • Beta (beta_higher, beta_lower)
  • Volume (volume_higher, volume_lower)
  • Dividend (dividend_higher, dividend_lower)

Note that the limit is 1000 companies. Thus if you hit the 1000, it is recommended to narrow down your search to prevent companies from being excluded simply because of this limit.

Args:

  • market_cap_higher (int): The minimum market capitalization of the stock.
  • market_cap_lower (int): The maximum market capitalization of the stock.
  • price_higher (int): The minimum price of the stock.
  • price_lower (int): The maximum price of the stock.
  • beta_higher (int): The minimum beta of the stock.
  • beta_lower (int): The maximum beta of the stock.
  • volume_higher (int): The minimum volume of the stock.
  • volume_lower (int): The maximum volume of the stock.
  • dividend_higher (int): The minimum dividend of the stock.
  • dividend_lower (int): The maximum dividend of the stock.

Returns: pd.DataFrame: A dataframe with all the symbols that match the query.

As an example: 

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

discovery.get_stock_screener(
market_cap_higher=1000000,
market_cap_lower=200000000000,
price_higher=100,
price_lower=200,
beta_higher=1,
beta_lower=1.5,
volume_higher=100000,
volume_lower=2000000,
dividend_higher=1,
dividend_lower=2,
is_etf=False
)

Which returns:

Symbol Name Market Cap Sector Industry Beta Price Dividend Volume Exchange Exchange Code Country
NKE NIKE, Inc. 163403295604 Consumer Cyclical Footwear & Accessories 1.079 107.36 1.48 1045865 New York Stock Exchange NYSE US
SAF.PA Safran SA 66234006559 Industrials Aerospace & Defense 1.339 160.16 1.35 119394 Paris EURONEXT FR
ROST Ross Stores, Inc. 46724188589 Consumer Cyclical Apparel Retail 1.026 138.785 1.34 169879 NASDAQ Global Select NASDAQ US
HES Hess Corporation 44694706090 Energy Oil & Gas E&P 1.464 145.51 1.75 123147 New York Stock Exchange NYSE US

get_stock_list

The stock list function returns a complete list of all the symbols that can be used in the FinanceToolkit. These are over 60.000 symbols.

Returns: pd.DataFrame: A dataframe with all the symbols in the toolkit.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

stock_list = discovery.get_stock_list()

# The total list equals over 60.000 rows
stock_list.iloc[38000:38010]

Which returns:

Symbol Name Price Exchange Exchange Code
LEO.V Lion Copper and Gold Corp. 0.09 Toronto Stock Exchange Ventures TSX
LEOF.TA Lewinsky-Ofer Ltd. 263.1 Tel Aviv TLV
LEON Leone Asset Management, Inc. 0.066 Other OTC OTC
LEON.SW Leonteq AG 34.35 Swiss Exchange SIX
LER.AX Leaf Resources Limited 0.014 Australian Securities Exchange ASX
LERTHAI.BO LERTHAI FINANCE LIMITED 265 Bombay Stock Exchange BSE
LES.WA Less S.A. 0.22 Warsaw Stock Exchange WSE
LESAF Le Saunda Holdings Limited 0.071 Other OTC PNK
LESHAIND.BO Lesha Industries Limited 4.68 Bombay Stock Exchange BSE
LESL Leslie’s, Inc. 6.91 NASDAQ Global Select NASDAQ

get_stock_quotes

Returns the real time stock prices for each company. This includes the bid and ask size, the volume, the bid and ask price, the last sales price and the last sales size.

Returns: pd.DataFrame: A dataframe with quotes for each company.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

stock_quotes = discovery.get_stock_quotes()

stock_quotes.iloc[3000:3010]

Which returns:

Symbol Bid Size Ask Price Volume Ask Size Bid Price Last Sale Price Last Sale Size Last Sale Time
EIPX 0 0 59676 0 0 21.28 0 1.7039e+12
EIRL 2 64.67 5455 2 57.7 61.1316 0 1.7039e+12
EIS 10 61.71 15886 2 56.2 58.1909 0 1.7039e+12
EIX 1 75.7 1.41398e+06 1 50.1 71.49 0 1.70389e+12
EJAN 1 31.42 252595 1 28.1 28.67 0 1.7039e+12
EJH 6 3.83 0 8 3.82 3.82 100 1.7042e+12
EJUL 2 27.97 10226 2 23.16 23.63 0 1.7039e+12
EKG 4 20 1197 1 6.38 15.9357 0 1.70388e+12
EKSO 3 2.54 0 5 2.31 2.31 100 1.7042e+12
EL 1 143.9 0 1 142.5 143 100 1.7042e+12

get_stock_shares_float

Returns the shares float for each company. The shares float is the number of shares available for trading for each company. It also includes the number of shares outstanding and the date.

Returns: pd.DataFrame: A dataframe with the shares float for each company.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

shares_float = discovery.get_stock_shares_float()

shares_float.iloc[50000:50010]

Which returns:

Symbol Date Free Float Float Shares Outstanding Shares
OPY.AX NaT 51.4746 119853548 2.3284e+08
OPYGY NaT 4.49504 60892047 1.35465e+09
OQAL 2024-01-01 13:12:23 0 0 226543
OQLGF 2023-12-31 21:48:07 0.6765 1150607 1.70082e+08
OR 2024-01-02 05:18:03 99.3281 183921869 1.85166e+08
OR-R.BK 2024-01-01 05:29:30 23.153 2778360000 1.2e+10
OR.BK 2024-01-02 03:52:39 22.7847 2734164000 1.2e+10
OR.PA 2024-01-02 07:57:35 45.2727 242084445 5.34725e+08
OR.SW 2023-12-31 13:38:10 45.2727 355743960 7.8578e+08
OR.TO 2023-12-31 17:56:33 99.3317 183928535 1.85166e+08

get_sectors_performance

Returns the sectors performance for each sector. This features the sector performance over the last months.

Returns: pd.DataFrame: A dataframe with the sectors performance for each sector.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

sectors_performance = discovery.get_sectors_performance()

sectors_performance.tail()

Which returns:

Date Utilities Basic Materials Communication Services Consumer Cyclical Consumer Defensive Energy Financial Services Healthcare Industrials Real Estate Technology
2023-12-27 0.13511 0.40986 -0.23963 0.10358 0.48048 -0.27499 0.30153 0.75715 0.30234 0.35946 0.02372
2023-12-28 0.80513 -0.45131 -0.15858 -0.45874 0.03828 -0.81641 0.02954 -0.01345 0.22808 0.59612 -0.15283
2023-12-29 -0.01347 -0.14525 -0.15072 -0.58879 0.18141 -0.42463 -0.34718 -0.082 -0.2181 -0.52222 -0.57062
2024-01-01 -0.01347 -0.14536 -0.15074 -0.58877 0.18141 -0.41917 -0.34753 -0.08193 -0.21821 -0.52216 -0.5708
2024-01-02 -0.01347 -0.14536 -0.15074 -0.58877 0.18141 -0.41917 -0.34779 -0.08193 -0.21823 -0.52281 -0.57073

get_biggest_gainers

Returns the biggest gainers for the day. This includes the symbol, the name, the price, the change and the change percentage.

Returns: pd.DataFrame: A dataframe with the biggest gainers for the day.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

biggest_gainers = discovery.get_biggest_gainers()

biggest_gainers.head(10)

Which returns:

Symbol Name Change Price Change %
AAME Atlantic American Corporation 0.3001 2.4501 13.9581
ADAP Adaptimmune Therapeutics plc 0.1029 0.793 14.9109
ADTX Aditxt, Inc. 1.81 6.63 37.5519
AFMD Affimed N.V. 0.0861 0.625 15.977
AIH Aesthetic Medical International Holdings Group Limited 0.1016 0.6896 17.2789
ANTE AirNet Technology Inc. 0.1229 0.8299 17.3833
APRE Aprea Therapeutics, Inc. 1.04 4.7 28.4153
ASTR Astra Space, Inc. 0.55 2.28 31.7919
BHG Bright Health Group, Inc. 2.37 7.63 45.057
BROG Brooge Energy Limited 0.73 3.68 24.7458

get_biggest_losers

Returns the biggest losers for the day. This includes the symbol, the name, the price, the change and the change percentage.

Returns: pd.DataFrame: A dataframe with the biggest losers for the day.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

biggest_losers = discovery.get_biggest_losers()

biggest_losers.head(10)

Which returns:

Symbol Name Change Price Change %
AGAE Allied Gaming & Entertainment Inc. -0.2 1.06 -15.873
AVTX Avalo Therapeutics, Inc. -2.7339 9.1 -23.1023
BAYAR Bayview Acquisition Corp Right -0.03 0.12 -20
BBLG Bone Biologics Corporation -1.48 4.52 -24.6667
BKYI BIO-key International, Inc. -0.6 3 -16.6667
BREA Brera Holdings PLC Class B Ordinary Shares -0.2064 0.6112 -25.2446
BTBT Bit Digital, Inc. -0.86 4.23 -16.8959
BTCS BTCS Inc. -0.69 1.63 -29.7414
BTDR Bitdeer Technologies Group -3.36 9.86 -25.416
BYN Banyan Acquisition Corporation -2.035 10.9 -15.7325

get_most_active_stocks

Returns the most active stocks for the day. This includes the symbol, the name, the price, the change and the change percentage.

Returns: pd.DataFrame: A dataframe with the most active stocks for the day.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

most_active_stocks = discovery.get_most_active_stocks()

most_active_stocks.head(10)

Which returns:

Symbol Name Change Price Change %
AAPL Apple Inc. -1.05 192.53 -0.5424
ADTX Aditxt, Inc. 1.81 6.63 37.5519
AMD Advanced Micro Devices, Inc. -1.35 147.41 -0.9075
AMZN Amazon.com, Inc. -1.44 151.94 -0.9388
BAC Bank of America Corporation -0.21 33.67 -0.6198
BITF Bitfarms Ltd. -0.41 2.91 -12.3494
BITO ProShares Bitcoin Strategy ETF -0.33 20.49 -1.585
CAN Canaan Inc. -0.5 2.31 -17.7936
CLSK CleanSpark, Inc. -2.08 11.03 -15.8657
DISH DISH Network Corporation 0.11 5.77 1.9435

get_delisted_stocks

The delisted stocks function returns a complete list of all delisted stocks including the IPO and delisted date.

Returns: pd.DataFrame: A dataframe with all the delisted stocks.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

delisted_stocks = discovery.get_delisted_stocks()

delisted_stocks.head(10)

Which returns:

Symbol Name Exchange IPO Date Delisted Date
AAIC Arlington Asset Investment Corp. NYSE 1997-12-23 2023-12-14
ABCM Abcam plc NASDAQ 2010-12-03 2023-12-12
ADZ DB Agriculture Short ETN AMEX 2008-04-16 2023-10-27
AENZ Aenza S.A.A. NYSE 2013-07-24 2023-12-08
AKUMQ Akumin Inc NASDAQ 2018-03-08 2023-10-25
ALTMW Kinetik Holdings Inc - Warrants (09/11/2023) NASDAQ 2017-05-01 2023-11-07
ARCE Arco Platform Limited NASDAQ 2018-09-26 2023-12-07
ARTEW Artemis Strategic Investment Corporation NASDAQ 2021-11-22 2023-11-03
ASPAU Abri SPAC I, Inc. NASDAQ 2021-08-10 2023-11-02
AVID Avid Technology, Inc. NASDAQ 1993-03-12 2023-11-07

get_crypto_list

The crypto list function returns a complete list of all crypto symbols that can be used in the FinanceToolkit. These are over 4.000 symbols.

Returns: pd.DataFrame: A dataframe with all the symbols in the toolkit.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

crypto_list = discovery.get_crypto_list()

crypto_list.head(10)

Which returns:

Symbol Name Currency Exchange
.ALPHAUSD .Alpha USD USD CCC
00USD 00 Token USD USD CCC
0NEUSD Stone USD USD CCC
0X0USD 0x0.ai USD USD CCC
0X1USD 0x1.tools: AI Multi-tool Plaform USD USD CCC
0XAUSD 0xApe USD USD CCC
0XBTCUSD 0xBitcoin USD USD CCC
0XENCRYPTUSD Encryption AI USD USD CCC
0XGASUSD 0xGasless USD USD CCC
0XMRUSD 0xMonero USD USD CCC

get_crypto_quotes

Returns the quotes for each crypto. This includes the symbol, the name, the price, the change, the change percentage, day low, day high, year high, year low, market cap, 50 day average, 200 day average, volume, average volume, open, previous close, EPS, PE, earnings announcement, shares outstanding and the timestamp.

Returns: pd.DataFrame: A dataframe with the quotes for each crypto.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

crypto_quotes = discovery.get_crypto_quotes()

crypto_quotes.head(10)

Which returns:

Symbol Name Price Change % Change Day Low Day High Year High Year Low Market Cap 50 Day Avg 200 Day Avg Volume Avg Volume Open Previous Close EPS PE Earnings Announcement Shares Outstanding Timestamp
.ALPHAUSD .Alpha USD 21.4023 0 0 21.3991 21.4023 193.252 21.4023 0 23.7774 51.0497 30 162 21.4023 21.4023 nan nan nan nan 2022-10-10 23:28:00
00USD 00 Token USD 0.082484 0.67363 0.00055192 0.0808863 0.0857288 0.28559 0.062939 0 0.0853295 0.0824169 210396 235403 0.0819321 0.0819321 nan nan nan 0 2024-01-02 14:05:40
0NEUSD Stone USD 7.39e-10 -1.70872 -1.3e-11 7.37e-10 7.79e-10 7.76e-10 7.52e-10 0 0 0 1110.14 nan 7.52e-10 7.52e-10 nan nan nan 0 2024-01-02 14:05:12
0X0USD 0x0.ai USD 0.15383 4.3101 0.00635643 0.14748 0.1551 0.17925 0.000275 1.33615e+08 0.12582 0.0734378 805257 1.17131e+06 0.14748 0.14748 nan nan nan 8.68563e+08 2024-01-02 14:05:13
0X1USD 0x1.tools: AI Multi-tool Plaform USD 0.00596268 2.65558 0.000154248 0.00580843 0.00608836 0.48504 0.005089 0 0.00587516 0.0448096 42.9976 216 0.00580843 0.00580843 nan nan nan 0 2024-01-02 14:06:00
0XAUSD 0xApe USD 9.86177e-06 -99.9921 -0.12519 9.86177e-06 0.12527 0.12527 9.86177e-06 0 1.08846e-05 1.08846e-05 197 nan 0.1252 0.1252 nan nan nan nan 2023-06-24 18:30:00
0XBTCUSD 0xBitcoin USD 0.097478 0.6003 0.00058167 0.0944255 0.10393 4.13419 0.03222 946195 0.17478 0.39561 344.45 97856 0.0968963 0.0968963 nan nan nan 9.70675e+06 2024-01-02 14:05:24
0XENCRYPTUSD Encryption AI USD 0.0213021 0 0 0.0213021 0.0213021 15.4064 0.020326 0 1.55438 3.26515 2 202458 0.0213021 0.0213021 nan nan nan nan 2023-07-26 18:30:00
0XGASUSD 0xGasless USD 0.11228 12.1894 0.0121997 0.10008 0.11228 0.19216 3.7e-05 0 0.038569 0.0143848 8700 9628 0.10008 0.10008 nan nan nan 0 2024-01-02 14:06:00
0XMRUSD 0xMonero USD 0.0497938 -38.9213 -0.0317302 0.0496646 2.79013 0.18734 0.0418889 0 0.13616 0.11633 347.276 11 0.081524 0.081524 nan nan nan nan 2024-01-02 14:05:07

get_forex_list

The forex list function returns a complete list of all forex symbols that can be used in the FinanceToolkit. These are over 1.000 symbols.

Returns: pd.DataFrame: A dataframe with the forex symbols.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

forex_list = discovery.get_forex_list()

forex_list.head(10)

Which returns:

Symbol Name Currency Exchange
AEDAUD AED/AUD AUD CCY
AEDBHD AED/BHD BHD CCY
AEDCAD AED/CAD CAD CCY
AEDCHF AED/CHF CHF CCY
AEDDKK AED/DKK DKK CCY
AEDEUR AED/EUR EUR CCY
AEDGBP AED/GBP GBP CCY
AEDILS AED/ILS ILS CCY
AEDINR AED/INR INR CCY
AEDJOD AED/JOD JOD CCY

get_forex_quotes

Returns the quotes for each forex. This includes the symbol, the name, the price, the change, the change percentage, day low, day high, year high, year low, market cap, 50 day average, 200 day average, volume, average volume, open, previous close, EPS, PE, earnings announcement, shares outstanding and the timestamp.

Returns: pd.DataFrame: A dataframe with quotes for each forex.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

forex_quotes = discovery.get_forex_quotes()

forex_quotes.head(10)

Which returns:

Symbol Name Price Change % Change Day Low Day High Year High Year Low 50 Day Avg 200 Day Avg Volume Avg Volume Open Previous Close Timestamp
AEDAUD AED/AUD 0.40089 0.40826 0.00163 0.39766 0.40118 0.43341 0.38041 0.41514 0.41372 11 nan 0.39921 0.39926 2024-01-02 14:02:15
AEDBHD AED/BHD 0.10262 0.0608637 6.2422e-05 0.10244 0.10266 0.10323 0.0991399 0.10264 0.10241 37 48.006 0.10256 0 2024-01-02 13:46:14
AEDCAD AED/CAD 0.36177 0.43587 0.00157 0.35996 0.36295 0.37817 0.35657 0.3701 0.36716 14 nan 0.36002 0.3602 2024-01-02 14:02:15
AEDCHF AED/CHF 0.23062 0.8704 0.00199 0.22847 0.23099 0.25693 0.2278 0.23976 0.24231 nan nan 0.22847 0.22863 2024-01-02 14:02:15
AEDDKK AED/DKK 1.84023 84.023 0.84023 1.83775 1.84081 1.94068 1.78424 1.86572 1.87037 16 49.5329 1.83874 1 2024-01-02 09:37:59
AEDEUR AED/EUR 0.2486 0.81044 0.00199857 0.24636 0.24871 0.265 0.2417 0.25271 0.25197 38 nan 0.24668 0.2466 2024-01-02 14:02:15
AEDGBP AED/GBP 0.21499 0.75924 0.00162 0.21298 0.2157 0.23039 0.2073 0.21802 0.21732 14 nan 0.2133 0.21337 2024-01-02 14:02:15
AEDILS AED/ILS 0.98746 -100 nan 0.98385 0.99536 1.1108 0.97828 1.01241 1.03478 923 549.264 0.98761 nan 2024-01-02 14:05:06
AEDINR AED/INR 22.7025 0.14076 0.0319101 22.625 22.72 22.72 20.1966 19.8653 20.1966 14 nan 22.7082 22.6706 2024-01-02 14:02:15
AEDJOD AED/JOD 0.19335 -3.32563 -0.00665126 0.19315 0.19364 0.19412 0.19185 0.19314 0.19315 38 18.8451 0.19331 0.2 2024-01-02 13:51:18

get_commodity_list

The commodity list function returns a complete list of all commodity symbols that can be used in the FinanceToolkit.

Returns: pd.DataFrame: A dataframe with all the commodities available.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

commodity_list = discovery.get_commodity_list()

commodity_list.head(10)

Which returns:

Symbol Name Currency Exchange
ALIUSD Aluminum Futures USD COMEX
BZUSD Brent Crude Oil USD ICE
CCUSD Cocoa USD ICE
CLUSD Crude Oil USD CME
CTUSX Cotton USX ICE
DCUSD Class III Milk Futures USD CME
DXUSD US Dollar USD ICE
ESUSD E-Mini S&P 500 USD CME
GCUSD Gold Futures USD CME
GFUSX Feeder Cattle Futures USX CME

get_commodity_quotes

Returns the quotes for each commodity. This includes the symbol, the name, the price, the change, the change percentage, day low, day high, year high, year low, market cap, 50 day average, 200 day average, volume, average volume, open, previous close, EPS, PE, earnings announcement, shares outstanding and the timestamp.

Returns: pd.DataFrame: A dataframe with the quotes for each commodity.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

commodity_quotes = discovery.get_commodity_quotes()

commodity_quotes.head(10)

Which returns:

Symbol Name Price Change % Change Day Low Day High Year High Year Low 50 Day Avg 200 Day Avg Volume Avg Volume Open Previous Close Timestamp
ALIUSD Aluminum Futures 2347 -1.12691 -26.75 2344 2383.5 2670.75 2073.25 2200.86 2221.04 4321 22 2370.75 2373.75 2024-01-02 13:54:40
BZUSD Brent Crude Oil 78.1 1.37591 1.06 77.21 79.06 97.63 68.2 81.291 81.9377 2285 30060 77.21 77.04 2024-01-02 14:10:12
CCUSD Cocoa 4249.5 1.27502 53.5 101.03 4274.5 4478 2507 4115.52 3483.99 18596 14509 4209 4196 2024-01-02 14:10:12
CLUSD Crude Oil 72.63 1.36776 0.98 71.63 73.65 95.03 63.64 76.3836 77.7364 37720 307715 71.71 71.65 2024-01-02 14:10:12
CTUSX Cotton 80.78 -0.2716 -0.22 3.87 81.75 90.75 74.77 79.8394 82.7224 960 15911 80.87 81 2024-01-02 14:10:00
DCUSD Class III Milk Futures 16.35 1.5528 0.25 15.43 17.16 20.49 13.75 16.6668 16.7265 51 212 16.1 16.1 2024-01-02 13:36:35
DXUSD US Dollar 101.862 0.82452 0.833 101.027 101.88 107.05 99.22 103.915 103.24 2999 14880 101.065 101.029 2024-01-02 14:10:10
ESUSD E-Mini S&P 500 4783 -0.76763 -37 4777.75 4828 4841.5 3808.75 4527.31 4378.91 75910 1.63378e+06 4818 4820 2024-01-02 14:00:13
GCUSD Gold Futures 2075 0.15446 3.2 2071.4 2094.7 2130.2 1808.1 2003.86 1960.64 38456 3511 2072.7 2071.8 2024-01-02 14:00:13
GFUSX Feeder Cattle Futures 223.125 0.0112057 0.025 222.725 224.45 257.5 177.55 226.9 230.114 4395 3915 224.4 223.1 2023-12-29 19:04:57

get_etf_list

The etf list function returns a complete list of all etf symbols that can be used in the FinanceToolkit.

Returns: pd.DataFrame: A dataframe with all the etf symbols.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

etf_list = discovery.get_etf_list()

etf_list.head(10)

Which returns:

Symbol Name Price Exchange Exchange Code
01002T.TW Cathay No.1 REIT 17.29 Taiwan TAI
020Y.L iShares IV Public Limited Company - iShares Euro Government Bond 20yr Target Duration UCITS ETF 3.9522 London Stock Exchange LSE
069500.KS KODEX 200 36390 KSE KSC
069660.KS KOSEF 200 36370 KSE KSC
091160.KS Kodex Semicon 36840 KSE KSC
091170.KS Kodex Banks 6695 KSE KSC
091180.KS Kodex Autos 19450 KSE KSC
091220.KS Mirae Asset TIGER Banks ETF 6845 KSE KSC
091230.KS Mirae Asset TIGER Semicon ETF 38400 KSE KSC
098560.KS Mirae Asset TIGER Media & Telecom ETF 7335 KSE KSC

get_index_list

The index list function returns a complete list of all etf symbols that can be used in the FinanceToolkit.

Returns: pd.DataFrame: A dataframe with all the index symbols.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

index_list = discovery.get_index_list()

index_list.head(10)

Which returns:

Symbol Name Currency Exchange
000001.SS SSE Composite Index CNY Shanghai
399967.SZ CSI NATIONAL DEFENSE CNY Shenzhen
512.HK CES CHINA HK MAINLAND INDEX HKD HKSE
DX-Y.NYB US Dollar/USDX - Index - Cash USD ICE Futures
FTSEMIB.MI FTSE MIB Index EUR Milan
IAR.BA MERVAL ARGENTINA USD Buenos Aires
IDX30.JK IDX30 IDR Jakarta Stock Exchange
IMOEX.ME MOEX Russia Index RUB MCX
ITLMS.MI FTSE Italia All-Share Index EUR Milan
KOSPI200.KS KOSPI 200 Index KRW KSE

get_index_quotes

Returns the quotes for each index. This includes the symbol, the name, the price, the change, the change percentage, day low, day high, year high, year low, market cap, 50 day average, 200 day average, volume, average volume, open, previous close, EPS, PE, earnings announcement, shares outstanding and the timestamp.

Returns: pd.DataFrame: A dataframe with all the symbols in the toolkit.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

index_quotes = discovery.get_index_quotes()

index_quotes.head(10)

Which returns:

Symbol Name Price Change % Change Day Low Day High Year High Year Low 50 Day Avg 200 Day Avg Volume Avg Volume Open Previous Close Timestamp
000001.SS SSE Composite Index 2962.28 -0.4255 -12.6587 2962.28 2976.27 3418.95 2882.02 2999.76 3160.83 349408228 290686 2972.78 2974.93 1704178820
399967.SZ CSI NATIONAL DEFENSE 9891.22 0.4875 47.9902 9834.98 10041.4 10041.4 9834.98 0 0 1115610197 0 9857.19 9843.23 1704184147
512.HK CES CHINA HK MAINLAND INDEX 6901.25 0 0 6786.45 6912.54 6912.54 6786.45 0 0 2785244718 0 6862.61 nan 1434960128
DX-Y.NYB US Dollar/USDX - Index - Cash 102.136 0.7924 0.803 101.34 102.167 107.35 99.58 104.108 103.421 0 0 101.417 101.333 1704204265
FTSEMIB.MI FTSE MIB Index 30396.8 0.1488 45.1699 30326.9 30863.6 30863.6 24111 29233.6 28164 0 473923362 30519.5 30351.6 1704203960
IAR.BA MERVAL ARGENTINA 33784.6 0 33784.6 33227.6 33871.5 33871.5 33227.6 0 0 0 0 33227.6 nan 1576872141
IDX30.JK IDX30 498.424 0.6486 3.212 492.621 498.424 498.424 492.621 0 0 0 0 493.985 495.212 1704186018
IMOEX.ME MOEX Russia Index 2222.51 -0.1859 -4.1399 2202.52 2234.55 4292.68 1681.55 2264.41 3183.63 0 0 2225.02 2226.65 1657295461
ITLMS.MI FTSE Italia All-Share Index 32507 0.0859 27.9004 32434.3 32999.1 32999.1 23017.3 22902.7 23017.3 0 0 32651.2 32479.1 1704203955
KOSPI200.KS KOSPI 200 Index 360.55 0.7151 2.56 355.96 361.53 361.53 355.96 0 0 106709 0 356.43 357.99 1704186335