Agent skill · data analytics · aradotso
btc-trading-since-2020
btc-trading-since-2020 — an agent skill for coding agents.
Why this skill is useful
Provides domain-specific knowledge and Python scripts for loading and analyzing a unique Bitcoin trading dataset that the AI wouldn't reliably generate on its own.
What it needs
Requires git, numpy, pandas, python installed locally. About 6k tokens when loaded. Last updated 2026-07-09. 70 stars on the source repository.
What this skill does
bash git clone https://github.com/bwjoke/BTC-Trading-Since-2020.git cd BTC-Trading-Since-2020 bash Latest tagged build (replace date as needed) gh release download data-2026-04-17 --repo bwjoke/BTC-Trading-Since-2020 api-v1-execution-tradeHistory.csv # primary execution ledger (balance-affecting) api-v1-order.csv # order intent + lifecycle api-v1-user-walletHistory.csv # deposits, withdrawals, funding, realised PnL api-v1-position.snapshot.csv # terminal position anchor api-v1-user-wallet.snapshot-all.csv # terminal wallet anchor api-v1-user-margin.snapshot-all.csv # terminal margin/equity anchor api-v1-user-walletSummary.all.csv # BitMEX summary cross-check api-v1-instrument.all.csv # instrument dictionary + contract specs api-v1-wallet-assets.csv # asset scale + wallet metadata derived-equity-curve.csv # XBT-equivalent wealth curve manifest.json # checksums, row counts, time ranges python import pandas as pd import numpy as np DATADIR = "./BTC-Trading-Since-2020" # adjust to your clone path def loadexecutions(): df = pd.readcsv(f"{DATADIR}/api-v1-execution-tradeHistory.csv", lowmemory=False) df["timestamp"] = pd.todatetime(df["timestamp"], utc=True) return df def loadorders(): df = pd.readcsv(f"{DATADIR}/api-v1-order.csv", lowmemory=False) df["timestamp"] = pd.todatetime(df["timestamp"], utc=True) df["transactTime"] = pd.todatetime(df["transactTime"], utc=True) return df def loadwallethistory(): df = pd.readcsv(f"{DATADIR}/api-v1-user-walletHistory.csv", lowmemory=False) df["timestamp"] = pd.todatetime(df["timestamp"], utc=True) df["transactTime"] = pd.todatetime(df["transactTime"], utc=True) return df def loadequitycurve(): df = pd.readcsv(f"{DATADIR}/derived-equity-curve.csv", lowmemory=False) df["timestamp"] = pd.todatetime(df["timestamp"], utc=True) return df def loadinstruments(): return pd.readcsv(f"{DATADIR}/api-v1-instrument.all.csv", lowmemory=False) python SATOSHI = 1e8 # BitMEX stores XBT amounts as integer satoshis def toxbt(satoshiseries): """Convert …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/btc-trading-since-2020