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candle_time timestamp[us, tz=UTC]date 2020-01-01 00:00:00 2026-07-31 23:59:00 | open float64 24.3 413 | high float64 24.4 413 | low float64 24 412 | close float64 24.3 413 | volume float64 0 6.73M | trades unknown |
|---|---|---|---|---|---|---|
2020-01-01T00:00:00 | 41.29 | 41.29 | 41.16 | 41.19 | 1,355.10796 | "160" |
2020-01-01T00:15:00 | 41.2 | 41.21 | 41.17 | 41.2 | 649.57758 | "92" |
2020-01-01T00:30:00 | 41.22 | 41.28 | 41.22 | 41.26 | 478.90868 | "68" |
2020-01-01T00:45:00 | 41.26 | 41.29 | 41.22 | 41.27 | 344.46616 | "76" |
2020-01-01T01:00:00 | 41.28 | 41.39 | 41.27 | 41.37 | 613.06474 | "111" |
2020-01-01T01:15:00 | 41.36 | 41.64 | 41.34 | 41.61 | 2,270.2336 | "370" |
2020-01-01T01:30:00 | 41.61 | 41.7 | 41.56 | 41.6 | 2,917.12209 | "360" |
2020-01-01T01:45:00 | 41.61 | 41.63 | 41.54 | 41.62 | 1,014.26565 | "156" |
2020-01-01T02:00:00 | 41.63 | 41.73 | 41.62 | 41.67 | 1,562.00274 | "205" |
2020-01-01T02:15:00 | 41.68 | 41.75 | 41.65 | 41.68 | 1,229.04906 | "170" |
2020-01-01T02:30:00 | 41.7 | 41.81 | 41.7 | 41.78 | 1,540.90958 | "198" |
2020-01-01T02:45:00 | 41.81 | 41.88 | 41.76 | 41.85 | 1,425.23346 | "189" |
2020-01-01T03:00:00 | 41.84 | 41.85 | 41.6 | 41.62 | 2,188.21413 | "166" |
2020-01-01T03:15:00 | 41.64 | 41.67 | 41.59 | 41.64 | 926.1717 | "54" |
2020-01-01T03:30:00 | 41.65 | 41.66 | 41.53 | 41.54 | 208.53499 | "65" |
2020-01-01T03:45:00 | 41.53 | 41.55 | 41.49 | 41.53 | 550.10288 | "115" |
2020-01-01T04:00:00 | 41.53 | 41.54 | 41.47 | 41.49 | 489.32945 | "93" |
2020-01-01T04:15:00 | 41.47 | 41.52 | 41.45 | 41.49 | 567.83339 | "84" |
2020-01-01T04:30:00 | 41.49 | 41.74 | 41.49 | 41.72 | 2,157.324 | "244" |
2020-01-01T04:45:00 | 41.71 | 41.85 | 41.59 | 41.59 | 2,103.3328 | "247" |
2020-01-01T05:00:00 | 41.61 | 41.69 | 41.6 | 41.63 | 400.16869 | "81" |
2020-01-01T05:15:00 | 41.61 | 41.66 | 41.59 | 41.65 | 205.17099 | "54" |
2020-01-01T05:30:00 | 41.65 | 41.78 | 41.6 | 41.77 | 795.30548 | "110" |
2020-01-01T05:45:00 | 41.76 | 41.84 | 41.7 | 41.78 | 1,120.51488 | "149" |
2020-01-01T06:00:00 | 41.79 | 41.82 | 41.73 | 41.75 | 940.04055 | "85" |
2020-01-01T06:15:00 | 41.74 | 41.81 | 41.74 | 41.78 | 965.12809 | "92" |
2020-01-01T06:30:00 | 41.79 | 41.8 | 41.59 | 41.59 | 2,896.57542 | "258" |
2020-01-01T06:45:00 | 41.6 | 41.64 | 41.5 | 41.62 | 2,524.03381 | "297" |
2020-01-01T07:00:00 | 41.61 | 41.61 | 41.54 | 41.57 | 199.69036 | "38" |
2020-01-01T07:15:00 | 41.58 | 41.63 | 41.55 | 41.62 | 346.46783 | "60" |
2020-01-01T07:30:00 | 41.63 | 41.64 | 41.47 | 41.5 | 762.93314 | "112" |
2020-01-01T07:45:00 | 41.5 | 41.59 | 41.48 | 41.58 | 1,070.72014 | "100" |
2020-01-01T08:00:00 | 41.58 | 41.61 | 41.52 | 41.53 | 1,400.16913 | "143" |
2020-01-01T08:15:00 | 41.52 | 41.52 | 41.41 | 41.47 | 1,670.90307 | "200" |
2020-01-01T08:30:00 | 41.48 | 41.52 | 41.42 | 41.44 | 841.51803 | "101" |
2020-01-01T08:45:00 | 41.43 | 41.51 | 41.4 | 41.51 | 710.21634 | "133" |
2020-01-01T09:00:00 | 41.52 | 41.63 | 41.51 | 41.58 | 779.14497 | "149" |
2020-01-01T09:15:00 | 41.6 | 41.65 | 41.57 | 41.62 | 1,061.27339 | "145" |
2020-01-01T09:30:00 | 41.62 | 41.71 | 41.62 | 41.64 | 564.30383 | "85" |
2020-01-01T09:45:00 | 41.64 | 41.65 | 41.56 | 41.58 | 433.26368 | "53" |
2020-01-01T10:00:00 | 41.58 | 41.68 | 41.56 | 41.67 | 664.78439 | "78" |
2020-01-01T10:15:00 | 41.7 | 41.76 | 41.67 | 41.74 | 829.17677 | "124" |
2020-01-01T10:30:00 | 41.76 | 41.76 | 41.7 | 41.75 | 359.81799 | "62" |
2020-01-01T10:45:00 | 41.73 | 41.76 | 41.71 | 41.71 | 702.94036 | "83" |
2020-01-01T11:00:00 | 41.71 | 41.79 | 41.71 | 41.76 | 727.13954 | "80" |
2020-01-01T11:15:00 | 41.76 | 41.98 | 41.74 | 41.91 | 1,573.43823 | "294" |
2020-01-01T11:30:00 | 41.92 | 42 | 41.85 | 41.94 | 2,414.38227 | "263" |
2020-01-01T11:45:00 | 41.95 | 41.95 | 41.53 | 41.59 | 2,974.32495 | "381" |
2020-01-01T12:00:00 | 41.59 | 41.73 | 41.59 | 41.69 | 717.76085 | "143" |
2020-01-01T12:15:00 | 41.7 | 41.78 | 41.68 | 41.71 | 369.31841 | "71" |
2020-01-01T12:30:00 | 41.7 | 41.8 | 41.68 | 41.77 | 807.71576 | "95" |
2020-01-01T12:45:00 | 41.76 | 41.83 | 41.75 | 41.8 | 374.71754 | "68" |
2020-01-01T13:00:00 | 41.8 | 41.84 | 41.76 | 41.79 | 575.02607 | "117" |
2020-01-01T13:15:00 | 41.8 | 41.83 | 41.72 | 41.81 | 1,473.77929 | "127" |
2020-01-01T13:30:00 | 41.82 | 41.9 | 41.81 | 41.87 | 1,036.65584 | "166" |
2020-01-01T13:45:00 | 41.87 | 41.95 | 41.83 | 41.85 | 1,500.01292 | "143" |
2020-01-01T14:00:00 | 41.85 | 41.87 | 41.76 | 41.8 | 885.03911 | "138" |
2020-01-01T14:15:00 | 41.83 | 41.93 | 41.82 | 41.91 | 871.49921 | "129" |
2020-01-01T14:30:00 | 41.92 | 41.94 | 41.85 | 41.88 | 1,283.45658 | "131" |
2020-01-01T14:45:00 | 41.86 | 41.86 | 41.8 | 41.84 | 1,096.48736 | "133" |
2020-01-01T15:00:00 | 41.85 | 41.9 | 41.83 | 41.86 | 502.64067 | "98" |
2020-01-01T15:15:00 | 41.87 | 41.9 | 41.8 | 41.9 | 730.78973 | "85" |
2020-01-01T15:30:00 | 41.9 | 42 | 41.88 | 41.93 | 1,606.31358 | "205" |
2020-01-01T15:45:00 | 41.95 | 41.99 | 41.85 | 41.95 | 1,149.19857 | "109" |
2020-01-01T16:00:00 | 41.94 | 42.05 | 41.86 | 41.92 | 2,940.68206 | "293" |
2020-01-01T16:15:00 | 41.92 | 41.92 | 41.77 | 41.82 | 961.71887 | "147" |
2020-01-01T16:30:00 | 41.8 | 41.94 | 41.78 | 41.9 | 680.92478 | "97" |
2020-01-01T16:45:00 | 41.91 | 42.08 | 41.91 | 42.04 | 1,609.42688 | "165" |
2020-01-01T17:00:00 | 42.06 | 42.07 | 41.94 | 42 | 1,814.02335 | "228" |
2020-01-01T17:15:00 | 42.01 | 42.05 | 41.96 | 42.01 | 793.8158 | "195" |
2020-01-01T17:30:00 | 42.01 | 42.01 | 41.89 | 41.9 | 1,045.57872 | "116" |
2020-01-01T17:45:00 | 41.9 | 41.95 | 41.86 | 41.88 | 1,044.28453 | "164" |
2020-01-01T18:00:00 | 41.89 | 41.91 | 41.82 | 41.83 | 1,647.03465 | "157" |
2020-01-01T18:15:00 | 41.85 | 41.87 | 41.68 | 41.7 | 2,052.19502 | "207" |
2020-01-01T18:30:00 | 41.7 | 41.71 | 41.57 | 41.7 | 1,161.06241 | "180" |
2020-01-01T18:45:00 | 41.71 | 41.84 | 41.61 | 41.82 | 1,511.21541 | "219" |
2020-01-01T19:00:00 | 41.81 | 41.81 | 41.72 | 41.77 | 993.19739 | "136" |
2020-01-01T19:15:00 | 41.78 | 41.83 | 41.69 | 41.71 | 588.35957 | "112" |
2020-01-01T19:30:00 | 41.74 | 41.76 | 41.66 | 41.67 | 536.89744 | "99" |
2020-01-01T19:45:00 | 41.66 | 41.73 | 41.63 | 41.63 | 1,152.27777 | "107" |
2020-01-01T20:00:00 | 41.62 | 41.64 | 41.57 | 41.61 | 1,691.65429 | "148" |
2020-01-01T20:15:00 | 41.62 | 41.71 | 41.51 | 41.7 | 1,390.67591 | "252" |
2020-01-01T20:30:00 | 41.72 | 41.75 | 41.61 | 41.7 | 1,083.05734 | "157" |
2020-01-01T20:45:00 | 41.67 | 41.73 | 41.61 | 41.73 | 277.60069 | "78" |
2020-01-01T21:00:00 | 41.73 | 41.73 | 41.6 | 41.63 | 409.8369 | "80" |
2020-01-01T21:15:00 | 41.6 | 41.71 | 41.58 | 41.62 | 1,277.09586 | "132" |
2020-01-01T21:30:00 | 41.62 | 41.67 | 41.52 | 41.64 | 823.01024 | "154" |
2020-01-01T21:45:00 | 41.64 | 41.76 | 41.63 | 41.66 | 2,956.76887 | "278" |
2020-01-01T22:00:00 | 41.65 | 41.66 | 41.53 | 41.58 | 2,038.79821 | "302" |
2020-01-01T22:15:00 | 41.58 | 41.61 | 41.54 | 41.55 | 357.95934 | "81" |
2020-01-01T22:30:00 | 41.54 | 41.57 | 41.43 | 41.46 | 2,321.14098 | "177" |
2020-01-01T22:45:00 | 41.47 | 41.58 | 41.35 | 41.51 | 1,778.42769 | "274" |
2020-01-01T23:00:00 | 41.5 | 41.5 | 41.32 | 41.41 | 802.06925 | "156" |
2020-01-01T23:15:00 | 41.42 | 41.57 | 41.36 | 41.53 | 1,366.62212 | "170" |
2020-01-01T23:30:00 | 41.53 | 41.6 | 41.47 | 41.55 | 1,022.25392 | "86" |
2020-01-01T23:45:00 | 41.56 | 41.65 | 41.56 | 41.62 | 410.46574 | "65" |
2020-01-02T00:00:00 | 41.58 | 41.64 | 41.53 | 41.6 | 318.32119 | "76" |
2020-01-02T00:15:00 | 41.61 | 41.66 | 41.6 | 41.64 | 352.98726 | "60" |
2020-01-02T00:30:00 | 41.64 | 41.7 | 41.64 | 41.69 | 565.30794 | "80" |
2020-01-02T00:45:00 | 41.71 | 41.74 | 41.66 | 41.74 | 1,306.10215 | "108" |
End of preview. Expand in Data Studio
Litecoin OHLCV Pack — Full 6.5-Year Dataset (Free)
6 timeframes · 3.4M candles (1m) · 2020–2026 · Free, no email required
Complete Litecoin OHLCV dataset built from audited tick-level trade data by The Glitch List.
What's Included
- 6 Parquet files: 1m, 5m, 15m, 1h, 4h, 1d
- Coverage: 2020-01-01 → 2026-07-04 (6.5 years)
- Zero look-ahead bias — verified
- Format: Apache Parquet (ZSTD compressed)
- Built from audited LTC/USDT tick-level trades
Quick Start
import polars as pl
df = pl.read_parquet("LTC_OHLCV_1m.parquet")
print(df.shape)
print(df.head())
Need Tick-Level Data?
The OHLCV pack is built from raw tick data. For HFT simulation, order flow imbalance, and market microstructure research, you need the tick-level dataset.
LTC/USDT Last Year — Rolling 12-Month Tick Data ($9): https://jalvart.gumroad.com/l/ruyrmg
Includes a free Strategy Starter Kit (Jupyter notebook): VWAP, realized volatility, and Order Flow Imbalance — pre-built analysis ready to run. OFI can only be calculated with tick-level data, not OHLCV.
License
Personal research and trading bot development only. No commercial redistribution, resale, or public API deployment.
Citation
If you use this dataset, please credit The Glitch List and link to theglitchlist.com.
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