Subtitle:

Comprehensive Bitcoin Hourly Trading Dataset: 2017–2024

Description:

This dataset offers detailed hourly trading data for Bitcoin (BTC-USD), covering the period from August 17, 2017, to December 25, 2024. It is sourced from Binance, one of the most prominent cryptocurrency exchanges, and is designed to provide a granular view of Bitcoin's market activity over several years.

Dataset Highlights:

• Timeframe: Over seven years of hourly data for in-depth analysis of market trends.
• OHLC Data: Includes Open, High, Low, and Close prices, enabling detailed candlestick analysis.
• Trading Volume: Captures the total traded volume for each hour, aiding in liquidity analysis.
• Quote Asset Volume: Represents the traded volume in USD equivalent, facilitating fiat comparisons.
• Number of Trades: The count of trades executed within each hour, offering insights into market activity.
• Taker Buy Base/Quote Volumes: Differentiates between aggressive (taker) buy orders in both Bitcoin and USD equivalents.
• Close Time: Marks the precise end of each hourly interval for temporal accuracy.

Applications:

This dataset is an excellent resource for:
- Time-Series Analysis: Understanding long-term and short-term market trends.
- Machine Learning Models: Training models for price prediction, anomaly detection, or volatility analysis.
- Algorithmic Trading: Building and backtesting trading strategies.
- Market Research: Analyzing Bitcoin's market dynamics, trading behavior, and historical performance.

The dataset is particularly useful for crypto enthusiasts, data scientists, and financial analysts seeking to explore the nuances of Bitcoin's price movements and trading activity over time.

Context

Within this dataset you will find detailed information hour by hour of the price of bitcoin Thanks to nand0san

Bitcoin Historical Dataset 3M records from 2017-2023

Context:

Bitcoin, the pioneering cryptocurrency, has captured the world's attention as a decentralized digital asset with a fluctuating market value. This dataset offers a comprehensive record of Bitcoin's price evolution, spanning from August 2017 to July 2023. The data has been meticulously collected from the Binance API, with price data captured at one-minute intervals. Each record includes essential information such as the open, high, low, and close prices, alongside associated trading volume. This dataset provides an invaluable resource for those interested in studying Bitcoin's price trends and market dynamics.

Dataset Details:

Total Number of Entries: 3.126.000

Attributes: Timestamp, Open Price, High Price, Low Price, Close Price, Volume , Quote asset volume, Number of trades, Taker buy base asset volume, Taker buy quote asset volume.

Data Type: csv

Size: 133 MB

Date ranges: 2023/08/17 till 2023/07/31

Content:

This dataset provides granular insights into the price history of Bitcoin, allowing users to explore minute-by-minute changes in its market value. The dataset includes attributes such as the open price, high price, low price, close price, trading volume, and the timestamp of each recorded interval. The data is presented in CSV format, making it easily accessible for analysis and visualization.

Inspiration:

The Bitcoin Price Dataset opens up numerous avenues for exploration and analysis, driven by the availability of high-frequency data. Potential research directions include:

Intraday Price Patterns: How do Bitcoin prices vary within a single day? Are there recurring patterns or trends during specific hours? Volatility Analysis: What are the periods of heightened volatility in Bitcoin's price history, and how do they correlate with external events or market developments? Correlation with Events: Can you identify instances where significant price movements coincide with notable events in the cryptocurrency space or broader financial markets? Long-Term Trends: How has the average price of Bitcoin evolved over different years? Are there multi-year trends that stand out? Trading Volume Impact: Is there a relationship between trading volume and price movement? How does trading activity affect short-term price fluctuations?

Acknowledgements:

The dataset has been sourced directly from the Binance API, a prominent cryptocurrency exchange platform. The collaboration with Binance ensures the dataset's accuracy and reliability, offering users a trustworthy foundation for conducting analyses and research related to Bitcoin's price movements.

Licensing:

Users are welcome to utilize this dataset for personal, educational, and research purposes, with attribution to the Binance API as the source of the data.

Hope you enjoy this dataset as much as I enjoyed putting it together. Can't wait to see what you can come up with :)

Find my notebook : Advanced EDA & Data Wrangling - Crypto Market Data where I cover the full EDA and advanced data wrangling to get beautiful dataset ready for analysis.

Find my Deep Reinforcement Learning v1 notebook: "https://www.kaggle.com/code/franoisgeorgesjulien/deep-reinforcement-learning-for-trading">Deep Reinforcement Learning for Trading

Find my Quant Analysis notebook:"https://www.kaggle.com/code/franoisgeorgesjulien/quant-analysis-visualization-btc-v1">💎 Quant Analysis & Visualization | BTC V1

Dataset Presentation:

This dataset provides a comprehensive collection of hourly price data for 34 major cryptocurrencies, covering a time span from January 2017 to the present day. The dataset includes Open, High, Low, Close, Volume (OHLCV), and the number of trades for each cryptocurrency for each hour (row).

Making it a valuable resource for cryptocurrency market analysis, research, and trading strategies. Whether you are interested in historical trends or real-time market dynamics, this dataset offers insights into the price movements of a diverse range of cryptocurrencies.

This is a pure gold mine, for all kind of analysis and predictive models. The granularity of the dataset offers a wide range of possibilities. Have Fun!

Ready to Use - Cleaned and arranged dataset less than 0.015% of missing data hour: crypto_data.csv

First Draft - Before External Sources Merge (to cover missing data points): crypto_force.csv

Original dataset merged from all individual token datasets: cryptotoken_full.csv

crypto_data.csv & cryptotoken_full.csv highly challenging wrangling situations: - fix 'Date' formats and inconsistencies - find missing hours and isolate them for each token - import external data source containing targeted missing hours and merge dataframes to fill missing rows

see notebook 'Advanced EDA & Data Wrangling - Crypto Market Data' to follow along and have a look at the EDA, wrangling and cleaning process.

Date Range: From 2017-08-17 04:00:00 to 2023-10-19 23:00:00

Date Format: YYYY-MM-DD HH-MM-SS (raw data to be converted to datetime)

Data Source: Binance API (some missing rows filled using Kraken & Poloniex market data)

Crypto Token in the dataset (also available as independent dataset): - 1INCH - AAVE - ADA (Cardano) - ALGO (Algorand) - ATOM (Cosmos) - AVAX (Avalanche) - BAL (Balancer) - BCH (Bitcoin Cash) - BNB (Binance Coin) - BTC (Bitcoin) - COMP (Compound) - CRV (Curve DAO Token) - DENT - DOGE (Dogecoin) - DOT (Polkadot) - DYDX - ETC (Ethereum Classic) - ETH (Ethereum) - FIL (Filecoin) - HBAR (Hedera Hashgraph) - ICP (Internet Computer) - LINK (Chainlink) - LTC (Litecoin) - MATIC (Polygon) - MKR (Maker) - RVN (Ravencoin) - SHIB (Shiba Inu) - SOL (Solana) - SUSHI (SushiSwap) - TRX (Tron) - UNI (Uniswap) - VET (VeChain) - XLM (Stellar) - XMR (Monero)

Date column presents some inconsistencies that need to be cleaned before formatting to datetime: - For column 'Symbol' and 'ETCUSDT' = '23-07-27': it is missing all hours (no data, no hourly rows for this day). I fixed it by using the only one row available for that day and duplicated the values for each hour. Can be fixed using this code:

start_timestamp = pd.Timestamp('2023-07-27 00:00:00')
end_timestamp = pd.Timestamp('2023-07-27 23:00:00')

hourly_timestamps = pd.date_range(start=start_timestamp, end=end_timestamp, freq='H')

hourly_data = {
  'Date': hourly_timestamps,
  'Symbol': 'ETCUSDT',
  'Open': 18.29,
  'High': 18.3,
  'Low': 18.17,
  'Close': 18.22,
  'Volume USDT': 127468,
  'tradecount': 623,
  'Token': 'ETC'
}

hourly_df = pd.DataFrame(hourly_data)
df = pd.concat([df, hourly_df], ignore_index=True)

df = df.drop(550341)

• Some rows for 'Date' have extra digits '.000' '.874' etc.. instead of the right format YYYY-MM-DD HH-MM-SS. To clean it you can use the following code:

# Count the occurrences of the pattern '.xxx' in the 'Date' column
count_occurrences_before = df['Date'].str.count(r'\.\d{3}')
print("Occurrences before cleaning:", count_occurrences_before.sum()) 

# Remove '.xxx' pattern from the 'Date' column
df['Date'] = df['Date'].str.replace(r'\.\d{3}', '', regex=True)

# Count the occurrences of the pattern '.xxx' in the 'Date' column after cleaning
count_occurrences_after = df['Date'].str.count(r'\.\d{3}')
print("Occurrences after cleaning:", count_occurrences_after.sum()) 

**Disclaimer: Any individual or entity choosing to engage in market analysis, develop predictive models, or utilize data for trading purposes must do so at their own discretion and risk. It is important to understand that trading involves potential financial loss, and decisions made in the financial mar...

I am a new developer and I would greatly appreciate your support. If you find this dataset helpful, please consider giving it an upvote!

Key Features:

Complete 1h Data: Raw 1h historical data from multiple exchanges, covering the entire trading history of BTCUSD available through their API endpoints. This dataset is updated daily to ensure up-to-date coverage.

Combined Index Dataset: A unique feature of this dataset is the combined index, which is derived by averaging all other datasets into one, please see attached notebook. This creates the longest continuous, unbroken BTCUSD dataset available on Kaggle, with no gaps and no erroneous values. It gives a much more comprehensive view of the market i.e. total volume across multiple exchanges.

Superior Performance: The combined index dataset has demonstrated superior 'mean average error' (MAE) metric performance when training machine learning models, compared to single-source datasets by a whole order of MAE magnitude.

Unbroken History: The combined dataset's continuous history is a valuable asset for researchers and traders who require accurate and uninterrupted time series data for modeling or back-testing.

https://i.imgur.com/OVOyF5A.png" alt="BTCUSD Dataset Summary">

https://i.imgur.com/6hxG2G3.png" alt="Combined Dataset Close Plot"> This plot illustrates the continuity of the dataset over time, with no gaps in data, making it ideal for time series analysis.

Included Resources:

Two Notebooks:

Dataset Usage and Diagnostics: This notebook demonstrates how to use the dataset and includes a powerful data diagnostics function, which is useful for all time series analyses.

Aggregating Multiple Data Sources: This notebook walks you through the process of combining multiple exchange datasets into a single, clean dataset. (Currently unavailable, will be added shortly)

Content

The dataset has one CSV file. Price history is available on a minute basis from 2017. This dataset has the historical price information of 1 Bitcoin by USDT (equivalent to USD).

• Date : date of observation
• Open : Opening price on the given minute
• High: Highest price on the given minute
• Low : Lowest price on the given minute
• Close : Closing price on the given minute
• Volume : Volume of transactions on the given minute

Acknowledgements

This data is taken from Binance and it is free to use the data.

This is the market data of Bitcoin in terms of price and volume from August 2015 to August 2021. The time interval of sampling is selected as four-hour, that is to say, we choose every kind of price and volume every of four-hour as the original data. The original market data of Bitcoin are obtained from Poloniex, one of the most active crypto-asset exchanges. Download link on XBlock: http://xblock.pro/#/dataset/5

This dataset offers a detailed examination of Bitcoin's price behavior over the last eight years, featuring a range of technical indicators for analyzing its trends. It captures the daily opening, highest, lowest, and closing prices, along with trading volume. The dataset includes momentum indicators such as the 7-day and 14-day Relative Strength Index (RSI) to determine if the asset is overbought or oversold. It also contains the 7-day and 14-day Commodity Channel Index (CCI), which compares the current price to the historical average to spot short- and medium-term trends. Additionally, it encompasses moving averages like the 50-day and 100-day Simple Moving Average (SMA) and Exponential Moving Average (EMA), which shed light on the asset's trend direction. Other essential indicators in the dataset are the Moving Average Convergence Divergence (MACD), Bollinger Bands for assessing price volatility, the True Range, and the 7-day and 14-day Average True Range (ATR) that provide insights into market volatility. This dataset's primary goal is to forecast the closing price for the next day, making it a crucial tool for predicting future market movements.

BTC DOGE SOL MOON Ang pagsubaybay sa kasaysayan ng presyo ay nagbibigay-daan sa mga crypto investor na madaling masubaybayan ang performance ng kanilang pamumuhunan. Maginhawa mong masusubaybayan ang opening value, high, at close sa BTC DOGE SOL MOON sa paglipas ng panahon, pati na rin ang trade volume. Bukod pa rito, maaari mong agad na tingnan ang pang-araw-araw na pagbabago bilang isang porsyento, na ginagawang effortless na tukuyin ang mga araw na may significant fluctuations. Ayon sa aming data ng history ng presyo ng BTC DOGE SOL MOON, tumaas ang halaga nito sa hindi pa naganap na peak sa 2025-04-13, na lumampas sa $0.0002548 USD. Sa kabilang banda, ang pinakamababang punto sa trajectory ng presyo ni BTC DOGE SOL MOON, na karaniwang tinutukoy bilang "BTC DOGE SOL MOON all-time low", ay naganap noong 2025-05-07. Kung ang isa ay bumili ng BTC DOGE SOL MOON sa panahong iyon, kasalukuyan silang masisiyahan sa isang kahanga-hangang kita na -100%. Sa pamamagitan ng disenyo, ang 1,000,000,000 BTC DOGE SOL MOON ay malilikha. Sa ngayon, ang circulating supply ng BTC DOGE SOL MOON ay tinatayang 0. Ang lahat ng mga presyong nakalista sa pahinang ito ay nakuha mula sa Bitget, galing sa isang reliable source. Napakahalagang umasa sa iisang pinagmulan upang suriin ang iyong mga investment, dahil maaaring mag-iba ang mga halaga sa iba't ibang nagbebenta. Kasama sa aming makasaysayang BTC DOGE SOL MOON dataset ng presyo ang data sa pagitan ng 1 minuto, 1 araw, 1 linggo, at 1 buwan (bukas/mataas/mababa/close/volume). Ang mga dataset na ito ay sumailalim sa mahigpit na pagsubok upang matiyak ang consistency, pagkakumpleto, at accurancy. Ang mga ito ay partikular na idinisenyo para sa trade simulation at mga layunin ng backtesting, madaling magagamit para sa libreng pag-download, at na-update sa real-time.

The dataset includes open, high, low and close prices of BTCUSDT cryptocurrency pair for different frequencies - 1 second (51.9k observations, within 2022-06-29), 1 minute (23.9k observations, 2022-06-13 - 2022-06-29), 1 hour (21.8k observations, 2020-01-01 - 2022-06-29). Market data is provided by Binance.

Please upvote if you find this dataset useful :-)

Based on 24-hour trading volume, Tether outpaced Ethereum and stablecoin Bitcoin on December 2, 2025, accounting for most crypto trades. This is not unusual, as stablecoin Tether often tends to be the most traded due to its use in purchasing other cryptocurrencies. Bitcoin's leading role is underlined in a market cap league table of more than 100 cryptocurrencies — including ones for DeFi, NFT and stablecoins. Bitcoin and Ethereum are typically the only ones to reach over 100 billion U.S. dollars, with Ethereum usually following by around one-half this amount. Does this mean that Bitcoin gets traded more than Ethereum? Not necessarily, as the daily transactions of Ethereum tend to be significantly higher than that of Bitcoin.

Crypto Fear and Greed Index

Each day, the website https://alternative.me/crypto/fear-and-greed-index/ publishes this index based on analysis of emotions and sentiments from different sources crunched into one simple number: The Fear & Greed Index for Bitcoin and other large cryptocurrencies.

Why Measure Fear and Greed?

The crypto market behaviour is very emotional. People tend to get greedy when the market is rising which results in FOMO (Fear of missing out). Also, people often sell their coins in irrational reaction of seeing red numbers. With our Fear and Greed Index, we try to save you from your own emotional overreactions. There are two simple assumptions:

• Extreme fear can be a sign that investors are too worried. That could be a buying opportunity.
• When Investors are getting too greedy, that means the market is due for a correction.

Therefore, we analyze the current sentiment of the Bitcoin market and crunch the numbers into a simple meter from 0 to 100. Zero means "Extreme Fear", while 100 means "Extreme Greed". See below for further information on our data sources.

Data Sources

We are gathering data from the five following sources. Each data point is valued the same as the day before in order to visualize a meaningful progress in sentiment change of the crypto market.

First of all, the current index is for bitcoin only (we offer separate indices for large alt coins soon), because a big part of it is the volatility of the coin price.

But let’s list all the different factors we’re including in the current index:

Volatility (25 %)

We’re measuring the current volatility and max. drawdowns of bitcoin and compare it with the corresponding average values of the last 30 days and 90 days. We argue that an unusual rise in volatility is a sign of a fearful market.

Market Momentum/Volume (25%)

Also, we’re measuring the current volume and market momentum (again in comparison with the last 30/90 day average values) and put those two values together. Generally, when we see high buying volumes in a positive market on a daily basis, we conclude that the market acts overly greedy / too bullish.

Social Media (15%)

While our reddit sentiment analysis is still not in the live index (we’re still experimenting some market-related key words in the text processing algorithm), our twitter analysis is running. There, we gather and count posts on various hashtags for each coin (publicly, we show only those for Bitcoin) and check how fast and how many interactions they receive in certain time frames). A unusual high interaction rate results in a grown public interest in the coin and in our eyes, corresponds to a greedy market behaviour.

Surveys (15%) currently paused

Together with strawpoll.com (disclaimer: we own this site, too), quite a large public polling platform, we’re conducting weekly crypto polls and ask people how they see the market. Usually, we’re seeing 2,000 - 3,000 votes on each poll, so we do get a picture of the sentiment of a group of crypto investors. We don’t give those results too much attention, but it was quite useful in the beginning of our studies. You can see some recent results here.

Dominance (10%)

The dominance of a coin resembles the market cap share of the whole crypto market. Especially for Bitcoin, we think that a rise in Bitcoin dominance is caused by a fear of (and thus a reduction of) too speculative alt-coin investments, since Bitcoin is becoming more and more the safe haven of crypto. On the other side, when Bitcoin dominance shrinks, people are getting more greedy by investing in more risky alt-coins, dreaming of their chance in next big bull run. Anyhow, analyzing the dominance for a coin other than Bitcoin, you could argue the other way round, since more interest in an alt-coin may conclude a bullish/greedy behaviour for that specific coin.

Trends (10%)

We pull Google Trends data for various Bitcoin related search queries and crunch those numbers, especially the change of search volumes as well as recommended other currently popular searches. For example, if you check Google Trends for "Bitcoin", you can’t get much information from the search volume. But currently, you can see that there is currently a +1,550% rise of the query „bitcoin price manipulation“ in the box of related search queries (as of 05/29/2018). This is clearly a sign of fear in the market, and we use that for our index.

There's a story behind every dataset and here's your opportunity to share yours.

Copyright disclaimer

This dataset is produced and maintained by the administrators of https://alternative.me/crypto/fear-and-greed-index/.

This published version is an unofficial copy of their data, which can be also collected using their API (e.g., GET https://api.alternative.me/fng/?limit=10&format=csv&date_format=us).

This Dataset contains 234 Crypto Coins/Altcoins with historical Open, High, Low, Close, and Volume (OHLCV) prices traded in the Binance Exchange. You can use price movements and trading volumes for coins price predictions.

If you want to download actual data - on today for example, then you can use python code from my github.

Ethereum D1 Daily timeframe

   datetime  open  high   low  close   volume
0 2017-08-17 301.13 312.18 298.00 302.00 7030.71034
1 2017-08-18 302.00 311.79 283.94 293.96 9537.84646
2 2017-08-19 293.31 299.90 278.00 290.91 2146.19773
3 2017-08-20 289.41 300.53 282.85 299.10 2510.13871
4 2017-08-21 299.10 346.52 294.60 323.29 5219.44542


... ... ... ... ... ... ... ...

  datetime   open   high   low  close    volume


2396 2024-03-09 3883.37 3942.00 3870.01 3905.21 254839.1386
2397 2024-03-10 3905.20 3964.67 3791.26 3878.47 356664.6800
2398 2024-03-11 3878.47 4086.23 3722.95 4064.80 721554.5139
2399 2024-03-12 4064.80 4093.92 3828.98 3979.96 632787.3941
2400 2024-03-13 3979.97 4083.00 3932.23 4004.79 482305.7766

Ethereum H1 Hourly timeframe

       datetime  open  high  low  close   volume
0 2017-08-17 04:00:00 301.13 302.57 298.0 301.61 125.66877
1 2017-08-17 05:00:00 301.61 303.28 300.0 303.10 377.67246
2 2017-08-17 06:00:00 302.40 304.44 301.9 302.68 303.86672
3 2017-08-17 07:00:00 302.68 307.96 302.6 307.96 754.74510
4 2017-08-17 08:00:00 307.95 309.97 307.0 308.62 150.75029


... ... ... ... ... ... ... ...

       datetime   open   high   low  close   volume


57506 2024-03-14 14:00:00 3878.68 3930.78 3864.80 3887.20 49884.6418
57507 2024-03-14 15:00:00 3887.21 3904.00 3820.20 3845.81 49956.2484
57508 2024-03-14 16:00:00 3845.81 3896.02 3825.07 3892.10 49424.4509
57509 2024-03-14 17:00:00 3892.11 3895.66 3853.45 3874.81 19929.6661
57510 2024-03-14 18:00:00 3874.80 3874.81 3760.29 3812.31 46685.5074

'

top_coins = ['BTCUSDT', 'ETHUSDT', 'BCHUSDT', 'XRPUSDT', 'EOSUSDT', 'LTCUSDT', 'TRXUSDT', 'ETCUSDT', 'LINKUSDT', 'XLMUSDT', 'ADAUSDT', 'XMRUSDT', 'DASHUSDT', 'ZECUSDT', 'XTZUSDT', 'BNBUSDT', 'ATOMUSDT', 'ONTUSDT', 'IOTAUSDT', 'BATUSDT', 'VETUSDT', 'NEOUSDT', 'QTUMUSDT', 'IOSTUSDT', 'THETAUSDT', 'ALGOUSDT', 'ZILUSDT', 'KNCUSDT', 'ZRXUSDT', 'COMPUSDT', 'OMGUSDT', 'DOGEUSDT', 'SXPUSDT', 'KAVAUSDT', 'BANDUSDT', 'RLCUSDT', 'WAVESUSDT', 'MKRUSDT', 'SNXUSDT', 'DOTUSDT', 'DEFIUSDT', 'YFIUSDT', 'BALUSDT', 'CRVUSDT', 'TRBUSDT', 'RUNEUSDT', 'SUSHIUSDT', 'SRMUSDT', 'EGLDUSDT', 'SOLUSDT', 'ICXUSDT', 'STORJUSDT', 'BLZUSDT', 'UNIUSDT', 'AVAXUSDT', 'FTMUSDT', 'HNTUSDT', 'ENJUSDT', 'FLMUSDT', 'TOMOUSDT', 'RENUSDT', 'KSMUSDT', 'NEARUSDT', 'AAVEUSDT', 'FILUSDT', 'RSRUSDT', 'LRCUSDT', 'MATICUSDT', 'OCEANUSDT', 'CVCUSDT', 'BELUSDT', 'CTKUSDT', 'AXSUSDT', 'ALPHAUSDT', 'ZENUSDT', 'SKLUSDT', 'GRTUSDT', '1INCHUSDT', 'CHZUSDT', 'SANDUSDT', 'ANKRUSDT', 'BTSUSDT', 'LITUSDT', 'UNFIUSDT', 'REEFUSDT', 'RVNUSDT', 'SFPUSDT', 'XEMUSDT', 'BTCSTUSDT', 'COTIUSDT', 'CHRUSDT', 'MANAUSDT', 'ALICEUSDT', 'HBARUSDT', 'ONEUSDT', 'LINAUSDT', 'STMXUSDT', 'DENTUSDT', 'CELRUSDT', 'HOTUSDT', 'MTLUSDT', 'OGNUSDT', 'NKNUSDT', 'SCUSDT', 'DGBUSDT', '1000SHIBUSDT', 'BAKEUSDT', 'GTCUSDT', 'BTCDOMUSDT', 'IOTXUSDT', 'AUDIOUSDT', 'RAYUSDT', 'C98USDT', 'MASKUSDT', 'ATAUSDT', 'DYDXUSDT', '1000XECUSDT', 'GALAUSDT', 'CELOUSDT', 'ARUSDT', 'KLAYUSDT', 'ARPAUSDT', 'CTSIUSDT', 'LPTUSDT', 'ENSUSDT', 'PEOPLEUSDT', 'ANTUSDT', 'ROSEUSDT', 'DUSKUSDT', 'FLOWUSDT', 'IMXUSDT', 'API3USDT', 'GMTUSDT', 'APEUSDT', 'WOOUSDT', 'FTTUSDT', 'JASMYUSDT', 'DARUSDT', 'GALUSDT', 'OPUSDT', 'INJUSDT', 'STGUSDT', 'FOOTBALLUSDT', 'SPELLUSDT', '1000LUNCUSDT', 'LUNA2USDT', 'LDOUSDT', 'CVXUSDT', 'ICPUSDT', 'APTUSDT', 'QNTUSDT', 'BLUEBIRDUSDT', 'FETUSDT', 'FXSUSDT', 'HOOKUSDT', 'MAGICUSDT', 'TUSDT', 'RNDRUSDT', 'HIGHUSDT', 'MINAUSDT', 'ASTRUSDT', 'AGIXUSDT', 'PHBUSDT', 'GMXUSDT', 'CFXUSDT', 'STXUSDT', 'COCOSUSDT', 'BNXUSDT', 'ACHUSDT', 'SSVUSDT', 'CKBUSDT', 'PERPUSDT', 'TRUUSDT', 'LQTYUSDT', 'USDCUSDT', 'IDUSDT', 'ARBUSDT', 'JOEUSDT', 'TLMUSDT', 'AMBUSDT', 'LEVERUSDT', 'RDNTUSDT', 'HFTUSDT', 'XVSUSDT', 'ETHBTC', 'BLURUSDT', 'EDUUSDT', 'IDEXUSDT', 'SUIUSDT', '1000PEPEUSDT', '1000FLOKIUSDT', 'UMAUSDT', 'RADUSDT', 'KEYUSDT', 'COMBOUSDT', 'NMRUSDT', 'MAVUSDT', 'MDTUSDT', 'XVGUSDT', 'WLDUSDT', 'PENDLEUSDT', 'ARKMUSDT', 'AGLDUSDT', 'YGGUSDT', 'DODOXUSDT', 'BNTUSDT', 'OXTUSDT', 'SEIUSDT', 'BTCUSDT_231229', 'ETHUSDT_231229', 'CYBERUSDT', 'HIFIUSDT', 'ARKUSDT', 'FRONTUSDT', 'GLMRUSDT', 'BICOUSDT', 'BTCUSDT_240329', 'ETHUSDT_240329', 'STRAXUSDT', 'LOOMUSDT', 'BIGTIMEUSDT', 'BONDUSDT', 'ORBSUSDT', 'STPTUSDT', 'WAXPUSDT', 'BSVUSDT', 'RIFUSDT', 'POLYXUSDT', 'GASUSDT', 'POWRUSDT', 'SLPUSDT', 'TIAUSDT', 'SNTUSDT', 'CAKEUSDT', 'MEMEUSDT', 'TWTUSDT', 'TOKENUSDT', 'ORDIUS...