avax_oi_funding

avax_oi_funding


# avax_oi_funding.py

import requests

import pandas as pd

import time

import argparse

import os

from datetime import datetime, timedelta


BASE = "https://api.bybit.com/v5/market"


def fetch_open_interest(symbol="AVAXUSDT", category="linear", intervalTime="15min",

            startTime=None, endTime=None, limit=200, sleep=0.15):

  """

  Returns DataFrame with columns: timestamp (ms), openInterest (string)

  Uses cursor pagination if API returns nextPageCursor.

  intervalTime: one of ['5min','15min','30min','1h','4h','1d']

  """

  url = f"{BASE}/open-interest"

  params = {

    "category": category,

    "symbol": symbol,

    "intervalTime": intervalTime,

    "limit": limit

  }

  if startTime:

    params["startTime"] = int(startTime)

  if endTime:

    params["endTime"] = int(endTime)


  all_rows = []

  cursor = None

  while True:

    if cursor:

      params["cursor"] = cursor

    r = requests.get(url, params=params, timeout=20)

    r.raise_for_status()

    j = r.json()

    if j.get("retCode") != 0:

      print("OpenInterest API error:", j)

      break

    res = j.get("result", {})

    lst = res.get("list", [])

    if not lst:

      break

    all_rows.extend(lst)

    cursor = res.get("nextPageCursor")

    if not cursor:

      break

    time.sleep(sleep)

  if not all_rows:

    return pd.DataFrame(columns=["timestamp","openInterest"])

  df = pd.DataFrame(all_rows)

  # convert types

  df["timestamp"] = pd.to_datetime(df["timestamp"].astype(int), unit="ms", utc=True)

  # openInterest is string in API -> convert to float

  df["openInterest"] = df["openInterest"].astype(float)

  return df.sort_values("timestamp").reset_index(drop=True)


def fetch_funding_history(symbol="AVAXUSDT", category="linear",

             startTime=None, endTime=None, limit=200, sleep=0.15):

  """

  Returns DataFrame with columns: fundingRateTimestamp (ms), fundingRate (string)

  Note: API returns up to `limit` rows; if you need a long span we iterate by setting startTime to last+1.

  """

  url = f"{BASE}/funding/history"

  params = {

    "category": category,

    "symbol": symbol,

    "limit": limit

  }

  if startTime:

    params["startTime"] = int(startTime)

  if endTime:

    params["endTime"] = int(endTime)


  all_rows = []

  while True:

    r = requests.get(url, params=params, timeout=20)

    r.raise_for_status()

    j = r.json()

    if j.get("retCode") != 0:

      print("Funding API error:", j)

      break

    res = j.get("result", {})

    lst = res.get("list", [])

    if not lst:

      break

    all_rows.extend(lst)

    # If less than limit returned, we're done

    if len(lst) < limit:

      break

    # otherwise move startTime forward to last timestamp + 1 ms to page forward

    last_ts = int(lst[-1]["fundingRateTimestamp"])

    params["startTime"] = last_ts + 1

    time.sleep(sleep)

  if not all_rows:

    return pd.DataFrame(columns=["fundingRateTimestamp","fundingRate","symbol"])

  df = pd.DataFrame(all_rows)

  df["fundingRateTimestamp"] = pd.to_datetime(df["fundingRateTimestamp"].astype(int), unit="ms", utc=True)

  df["fundingRate"] = df["fundingRate"].astype(float)

  return df.sort_values("fundingRateTimestamp").reset_index(drop=True)


def ts_from_days_ago(days):

  end = int(time.time() * 1000)

  start = int((datetime.utcnow() - timedelta(days=days)).timestamp() * 1000)

  return start, end


def main():

  parser = argparse.ArgumentParser(description="Download AVAX OI and Funding from Bybit")

  parser.add_argument("--symbol", default="AVAXUSDT", help="symbol (default AVAXUSDT)")

  parser.add_argument("--days", type=int, default=30, help="how many days back to fetch (default 30)")

  parser.add_argument("--oi_interval", default="15min", choices=["5min","15min","30min","1h","4h","1d"],

            help="interval for open interest (default 15min)")

  parser.add_argument("--outdir", default=".", help="output directory (default current)")

  args = parser.parse_args()


  symbol = args.symbol

  days = args.days

  oi_interval = args.oi_interval

  outdir = args.outdir

  os.makedirs(outdir, exist_ok=True)


  start_ms, end_ms = ts_from_days_ago(days)


  print(f"Fetching OI {symbol} {oi_interval} from {datetime.utcfromtimestamp(start_ms/1000)} to {datetime.utcfromtimestamp(end_ms/1000)}")

  df_oi = fetch_open_interest(symbol=symbol, intervalTime=oi_interval, startTime=start_ms, endTime=end_ms)

  oi_file = os.path.join(outdir, f"{symbol}_open_interest_{oi_interval}.csv")

  df_oi.to_csv(oi_file, index=False)

  print("Saved OI:", oi_file, "rows:", len(df_oi))


  print(f"Fetching Funding history {symbol} for last {days} days")

  df_f = fetch_funding_history(symbol=symbol, startTime=start_ms, endTime=end_ms)

  f_file = os.path.join(outdir, f"{symbol}_funding_history.csv")

  df_f.to_csv(f_file, index=False)

  print("Saved Funding history:", f_file, "rows:", len(df_f))


if __name__ == "__main__":

  main()  Как работает скрипт

  • Подключается к Bybit API /v5/market/recent-trade (список последних сделок).
  • Скачивает трейды пачками (limit=1000).
  • Сохраняет их в CSV с колонками:
  • timestamp (UTC)
  • side (Buy/Sell)
  • size (объём сделки в контрактах)
  • price
  • Далее агрегирует их по 1 минуте в формат:
  • timestamp, taker_buy_volume, taker_sell_volume
  • Результат: CSV-файл avax_takers_1m.csv

avax_trades_to_cvd.py

import requests

import pandas as pd

import time

import os

from datetime import datetime, timedelta


BASE = "https://api.bybit.com/v5/market"


def fetch_trades(symbol="AVAXUSDT", limit=1000, cursor=None):

  """

  Получает список последних сделок (max 1000 за раз).

  Возвращает JSON.

  """

  url = f"{BASE}/recent-trade"

  params = {

    "category": "linear",  # фьючерсы USDT

    "symbol": symbol,

    "limit": limit

  }

  if cursor:

    params["cursor"] = cursor

  r = requests.get(url, params=params, timeout=15)

  r.raise_for_status()

  return r.json()


def download_trades(symbol="AVAXUSDT", minutes=60):

  """

  Скачиваем трейды за последние N минут.

  ВАЖНО: этот API отдаёт только последние ~5000–10000 сделок,

  поэтому для длинной истории лучше вебсокеты или сторонние датасеты.

  """

  end_time = datetime.utcnow()

  start_time = end_time - timedelta(minutes=minutes)


  all_trades = []

  cursor = None

  while True:

    j = fetch_trades(symbol, cursor=cursor)

    if j.get("retCode") != 0:

      print("Ошибка API:", j)

      break

    data = j["result"]["list"]

    if not data:

      break


    # API возвращает трейды в обратном порядке (свежие первыми)

    for t in data:

      ts = int(t["time"])

      dt = datetime.utcfromtimestamp(ts/1000)

      if dt < start_time:

        return all_trades

      trade = {

        "timestamp": dt,

        "side": t["side"],

        "size": float(t["size"]),

        "price": float(t["price"])

      }

      all_trades.append(trade)


    cursor = j["result"].get("nextPageCursor")

    if not cursor:

      break

    time.sleep(0.2)

  return all_trades


def trades_to_cvd(trades):

  """

  Аггрегируем трейды в 1m интервалы: taker_buy_volume / taker_sell_volume.

  """

  df = pd.DataFrame(trades)

  df = df.set_index("timestamp")


  buys = df[df["side"]=="Buy"].resample("1min").agg({"size":"sum"})

  sells = df[df["side"]=="Sell"].resample("1min").agg({"size":"sum"})


  out = pd.DataFrame(index=pd.date_range(df.index.min(), df.index.max(), freq="1min"))

  out["taker_buy_volume"] = buys["size"]

  out["taker_sell_volume"] = sells["size"]

  out = out.fillna(0).reset_index().rename(columns={"index":"timestamp"})

  return out


if __name__ == "__main__":

  print("Скачиваем последние 120 минут сделок по AVAX...")

  trades = download_trades("AVAXUSDT", minutes=120)

  print("Всего сделок:", len(trades))


  df = trades_to_cvd(trades)

  df.to_csv("avax_takers_1m.csv", index=False)

  print("Сохранено:", len(df), "минутных свечей в avax_takers_1m.csv")




Report content on this page

Report Page

Please submit your DMCA takedown request to dmca@telegram.org