kline-timestamp 0.3.0

KlineTimestamp is a Python library designed to efficiently handle timestamps within discrete time intervals (klines/candlesticks), commonly used in financial data analysis.

KlineTimestamp

KlineTimestamp is a Python library designed to accurately and efficiently handle timestamps within discrete time intervals (klines or candlesticks), commonly used in financial time series and market analysis.

It provides a clean, immutable, timezone-aware API that makes reasoning about candle boundaries extremely intuitive.

Repository: https://github.com/nand0san/KlineTimestamp

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Features

• Canonical open/close computation Automatically snap any timestamp (ms) to the correct candle open/close.

• Timezone-aware conversions Built-in support for pytz with proper DST handling.

• Clean datetime integration Convert directly to datetime.datetime or pandas.Timestamp.

• Immutable API Every transformation returns a new instance (with_timezone, with_interval, addition/subtraction with timedelta).

• Navigation between candles Use next() and prev() to move through consecutive klines.

• Strict comparison semantics Candle identity depends only on (open, tick_ms); timezone affects display only.

• Guaranteed total ordering Timestamps are comparable via lexicographic (open, tick_ms) ordering.

• All Binance intervals All fixed-duration Binance intervals (1m to 1w) plus the variable-duration monthly interval (1M).

• Monthly candles (1M) Calendar-aware: handles 28/29/30/31-day months correctly, including leap years.

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Installation

pip install kline_timestamp

Requires Python 3.9+.

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Dependencies

• pandas>=2.3.0,<3.0
• pytz>=2024.1

These are declared automatically in the installed package.

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Quick start

from kline_timestamp import KlineTimestamp

Constructor

KlineTimestamp(timestamp_ms: int, interval: str, tzinfo: str | pytz.BaseTzInfo = "UTC")

Parameter	Type	Description
timestamp_ms	int	Epoch milliseconds (UTC). Must be int.
interval	str	Candle interval (see table below). Case-insensitive except 1M.
tzinfo	str or pytz.BaseTzInfo	Timezone for display. Default "UTC".

Instance attributes

Attribute	Type	Description
open	int	Candle open timestamp (epoch ms, UTC).
close	int	Candle close timestamp (epoch ms, UTC).
tick_ms	int	Duration of the candle in milliseconds. For 1M, varies per month.
interval	str	Normalized interval string.
tzinfo	pytz.BaseTzInfo	Timezone instance.

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Supported intervals

Fixed-duration intervals (constant tick_ms):

Interval	Duration
1m	1 minute
3m	3 minutes
5m	5 minutes
15m	15 minutes
30m	30 minutes
1h	1 hour
2h	2 hours
4h	4 hours
6h	6 hours
8h	8 hours
12h	12 hours
1d	1 day
3d	3 days
1w	1 week

Variable-duration interval:

Interval	Duration
1M	1 calendar month (28-31 days)

Important: 1M (uppercase) is monthly. 1m (lowercase) is 1 minute. The interval is case-sensitive for 1M; all others are case-insensitive.

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Methods

Conversions

kt.to_datetime()           # -> datetime.datetime (tz-aware)
kt.to_pandas_timestamp()   # -> pd.Timestamp (tz-aware)

Immutable transformations

kt.with_timezone("UTC")    # -> new KlineTimestamp, same candle, different tz
kt.with_interval("15m")    # -> new KlineTimestamp, re-snapped to new interval

Navigation

kt.next()                  # -> next candle (same interval)
kt.prev()                  # -> previous candle (same interval)

For 1M, next()/prev() correctly navigate calendar months (handles Dec->Jan, Jan->Dec, variable month lengths).

Arithmetic

kt + timedelta(hours=1)    # -> new KlineTimestamp shifted forward
kt - timedelta(hours=1)    # -> new KlineTimestamp shifted backward
kt - kt_other              # -> timedelta (difference between opens)

Comparisons

kt == kt_other             # equality by (open, tick_ms)
kt < kt_other              # ordering by (open, tick_ms)

Timezone is ignored for equality, hashing, and ordering.

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Examples

Fixed interval

from kline_timestamp import KlineTimestamp
from datetime import timedelta

kt = KlineTimestamp(1633036800000, "1h", "Europe/Madrid")

print("Open:", kt.open)
print("Close:", kt.close)
print("Datetime:", kt.to_datetime())
print("Pandas:", kt.to_pandas_timestamp())

kt_utc = kt.with_timezone("UTC")
print("UTC:", kt_utc.to_datetime())

kt_plus = kt + timedelta(hours=1)
print("After +1h:", kt_plus.to_datetime())

print("Next:", kt.next().to_datetime())
print("Prev:", kt.prev().to_datetime())

kt_other = KlineTimestamp(1633033200000, "1h", "UTC")
print("Comparison:", kt > kt_other)
print("Time delta:", kt - kt_other)

Monthly interval (1M)

from kline_timestamp import KlineTimestamp

# March 2024
kt = KlineTimestamp(1709251200000, "1M", "UTC")
print("Open:", kt.to_datetime())    # 2024-03-01 00:00:00+00:00
print("Close ms:", kt.close)        # last ms of March 31

# Navigate months
kt_apr = kt.next()                  # April 2024
kt_feb = kt.prev()                  # February 2024 (leap year, 29 days)
print("Feb days:", kt_feb.tick_ms // 86400000)  # 29

# Any timestamp within a month snaps to that month’s open
kt_mid = KlineTimestamp(1710000000000, "1M", "UTC")  # ~Mar 9
print(kt_mid.open == kt.open)       # True

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License

MIT License. See the LICENSE file for details.

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Contributing

Pull requests are welcome. Open an issue or send a PR at:

https://github.com/nand0san/KlineTimestamp

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Acknowledgments

• Inspired by practical needs in quantitative financial analysis.
• Uses the excellent pytz and pandas libraries for timezone and datetime handling.