Litecoin's market cap in early 2020 was the highest ever-recorded, topping over ten billion U.S. dollars and a value that had increased by 100 percent since August 2020. Market capitalization figures are calculated by multiplying the total number of Litecoin in circulation by the Litecoin price. Compared to both the Bitcoin market capitalization as well as the Ethereum market cap, though, Litecoin's figures were significantly smaller.

The Litecoin cryptocurrency peaked in both 2017 and 2020 - reaching prices worth around 250 dollars - but did not reach this by 2022. As of May 4, 2025, one Litecoin token was worth 85.02 U.S. dollars. Litecoin's price was relatively volatile recently, revealing high price swings between months. What is a cryptocurrency? Cryptocurrencies are digital currencies that do not have a centralized regulating authority. The first of these, Bitcoin, introduced a technology called blockchain, in which a distributed ledger records every transaction on every bitcoin in circulation to prevent fraud. Litecoin also uses this technology. To accommodate the demands of constant ledger updates, users sell computational power in exchange for an amount of Litecoin, a process known as mining. More about Litecoin Cryptocurrencies are still an emerging technology, and few are using them for transactions. As such, most users are speculators who look at the value of all coins in circulation as the market capitalization rather than money supply. Still, the average number of Litecoin transactions ranges in the tens of thousands, meaning that the cryptocurrency has a substantial financial footprint.

Context

In the last few days, I have been hearing a lot of buzz around cryptocurrencies. Things like Block chain, Bitcoin, Bitcoin cash, Ethereum, Ripple etc are constantly coming in the news articles I read. So I wanted to understand more about it and this post helped me get started. Once the basics are done, the DS guy sleeping inside me (always lazy.!) woke up and started raising questions like

1. How many such cryptocurrencies are there and what are their prices and valuations?
2. Why is there a sudden surge in the interest in recent days? Is it due to the increase in the price in the last few days? etc.

For getting answers to all these questions (and if possible to predict the future prices ;)), I started getting the data from coinmarketcap about the cryptocurrencies.

Content

This dataset has the historical price information of some of the top cryptocurrencies by market capitalization. The currencies included are

• Bitcoin
• Ethereum
• Ripple
• Bitcoin cash
• Bitconnect
• Dash
• Ethereum Classic
• Iota
• Litecoin
• Monero
• Nem
• Neo
• Numeraire
• Stratis
• Waves

In case if you are interested in the prices of some other currencies, please post in comments section and I will try to add them in the next version. I am planning to revise it once in a week.

Dataset has one csv file for each currency. Price history is available on a daily basis from April 28, 2013 till Aug 07, 2017. The columns in the csv file are

• Date : date of observation
• Open : Opening price on the given day
• High : Highest price on the given day
• Low : Lowest price on the given day
• Close : Closing price on the given day
• Volume : Volume of transactions on the given day
• Market Cap : Market capitalization in USD

Acknowledgements

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

Cover Image : Photo by Thomas Malama on Unsplash

Inspiration

Some of the questions which could be inferred from this dataset are:

1. How did the historical prices / market capitalizations of various currencies change over time?
2. Predicting the future price of the currencies
3. Which currencies are more volatile and which ones are more stable?
4. How does the price fluctuations of currencies correlate with each other?
5. Seasonal trend in the price fluctuations

Context:

This dataset is an extra updating dataset for the G-Research Crypto Forecasting competition.

Introduction

This is a daily updated dataset, automaticlly collecting market data for G-Research crypto forecasting competition. The data is of the 1-minute resolution, collected for all competition assets and both retrieval and uploading are fully automated. see discussion topic.

The Data

For every asset in the competition, the following fields from Binance's official API endpoint for historical candlestick data are collected, saved, and processed.

1. **timestamp** - A timestamp for the minute covered by the row.
2. **Asset_ID** - An ID code for the cryptoasset.
3. **Count** - The number of trades that took place this minute.
4. **Open** - The USD price at the beginning of the minute.
5. **High** - The highest USD price during the minute.
6. **Low** - The lowest USD price during the minute.
7. **Close** - The USD price at the end of the minute.
8. **Volume** - The number of cryptoasset u units traded during the minute.
9. **VWAP** - The volume-weighted average price for the minute.
10. **Target** - 15 minute residualized returns. See the 'Prediction and Evaluation section of this notebook for details of how the target is calculated.
11. **Weight** - Weight, defined by the competition hosts [here](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)
12. **Asset_Name** - Human readable Asset name.

Indexing

The dataframe is indexed by timestamp and sorted from oldest to newest. The first row starts at the first timestamp available on the exchange, which is July 2017 for the longest-running pairs.

Usage Example

The following is a collection of simple starter notebooks for Kaggle's Crypto Comp showing PurgedTimeSeries in use with the collected dataset. Purged TimesSeries is explained here. There are many configuration variables below to allow you to experiment. Use either GPU or TPU. You can control which years are loaded, which neural networks are used, and whether to use feature engineering. You can experiment with different data preprocessing, model architecture, loss, optimizers, and learning rate schedules. The extra datasets contain the full history of the assets in the same format as the competition, so you can input that into your model too.

Baseline Example Notebooks:

• Neural Network Starter
• LightGBM Starter
• Catboost Starter
• XGBoost Starter
• TabNet Starter
• Reinforcement Learning (PPO) Starter

These notebooks follow the ideas presented in my "Initial Thoughts" here. Some code sections have been reused from Chris' great (great) notebook series on SIIM ISIC melanoma detection competition here

Loose-ends:

This is a work in progress and will be updated constantly throughout the competition. At the moment, there are some known issues that still needed to be addressed:

• VWAP: - At the moment VWAP calculation formula is still unclear. Currently the dataset uses an approximation calculated from the Open, High, Low, Close, Volume candlesticks. [Waiting for competition hosts input]
• Target Labeling: There exist some mismatches to the original target provided by the hosts at some time intervals. On all the others - it is the same. The labeling code can be seen here. [Waiting for competition hosts] input]
• Filtering: No filtration of 0 volume data is taken place.

Example Visualisations

Opening price with an added indicator (MA50): https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fb8664e6f26dc84e9a40d5a3d915c9640%2Fdownload.png?generation=1582053879538546&alt=media" alt="">

Volume and number of trades: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fcd04ed586b08c1576a7b67d163ad9889%2Fdownload-1.png?generation=1582053899082078&alt=media" alt="">

License

This data is being collected automatically from the crypto exchange Binance.

Japan CLT: LTC: Long-Term Credit: Doubtful data was reported at 5.000 JPY bn in Sep 2017. This records a decrease from the previous number of 15.000 JPY bn for Mar 2017. Japan CLT: LTC: Long-Term Credit: Doubtful data is updated semiannually, averaging 153.500 JPY bn from Mar 1999 (Median) to Sep 2017, with 38 observations. The data reached an all-time high of 1,404.000 JPY bn in Mar 2000 and a record low of 5.000 JPY bn in Sep 2017. Japan CLT: LTC: Long-Term Credit: Doubtful data remains active status in CEIC and is reported by Financial Services Agency. The data is categorized under Global Database’s Japan – Table JP.KA019: Non Performing Loans: Classified Assets: Financial Reconstruction Law: Excl Agri Coop & Shoko Chukin Bank.

Japan CLT: LTC: Long-Term Credit: Special Attention data was reported at 5.000 JPY bn in Sep 2017. This stayed constant from the previous number of 5.000 JPY bn for Mar 2017. Japan CLT: LTC: Long-Term Credit: Special Attention data is updated semiannually, averaging 28.000 JPY bn from Mar 1999 (Median) to Sep 2017, with 38 observations. The data reached an all-time high of 1,751.000 JPY bn in Sep 2001 and a record low of 5.000 JPY bn in Sep 2017. Japan CLT: LTC: Long-Term Credit: Special Attention data remains active status in CEIC and is reported by Financial Services Agency. The data is categorized under Global Database’s Japan – Table JP.KA019: Non Performing Loans: Classified Assets: Financial Reconstruction Law: Excl Agri Coop & Shoko Chukin Bank.

Japan CLT: Long-Term Credit (LTC): Total Assets data was reported at 7,385.000 JPY bn in Mar 2018. This records an increase from the previous number of 7,248.000 JPY bn for Sep 2017. Japan CLT: Long-Term Credit (LTC): Total Assets data is updated semiannually, averaging 7,336.000 JPY bn from Mar 1999 (Median) to Mar 2018, with 39 observations. The data reached an all-time high of 39,371.000 JPY bn in Sep 2001 and a record low of 6,244.000 JPY bn in Mar 2005. Japan CLT: Long-Term Credit (LTC): Total Assets data remains active status in CEIC and is reported by Financial Services Agency. The data is categorized under Global Database’s Japan – Table JP.KB018: Non Performing Loans: Excl Agri Coop & Shoko Chukin Bank.

Japan CLT: LTC: NPLs based on FRL data was reported at 15.000 JPY bn in Mar 2018. This records an increase from the previous number of 13.000 JPY bn for Sep 2017. Japan CLT: LTC: NPLs based on FRL data is updated semiannually, averaging 284.000 JPY bn from Mar 1999 (Median) to Mar 2018, with 39 observations. The data reached an all-time high of 4,051.000 JPY bn in Sep 2000 and a record low of 13.000 JPY bn in Sep 2017. Japan CLT: LTC: NPLs based on FRL data remains active status in CEIC and is reported by Financial Services Agency. The data is categorized under Global Database’s Japan – Table JP.KB018: Non Performing Loans: Excl Agri Coop & Shoko Chukin Bank.