Skip to content

Latest commit

 

History

History
87 lines (62 loc) · 2.99 KB

get-ethdata-ocean-binance.md

File metadata and controls

87 lines (62 loc) · 2.99 KB
Raw

Get Binance ETH/USDT price feed via Ocean

This is published as a free asset in Ocean. Under the hood, it queries the Binance API.

You can see it on Ocean Market here.

0. Setup

We assume you've already done main3.md "Setup".

If needed, re-setup in Python:

  • Do ocean.py setup-remote.md "Setup in Python"
  • And do: from predict_eth.helpers import *

1. Download data

In the Python console:

ETH_USDT_did = "did:op:0dac5eb4965fb2b485181671adbf3a23b0133abf71d2775eda8043e8efc92d19"
ETH_USDT_ddo = ocean.assets.resolve(ETH_USDT_did)
ETH_USDT_datatoken = ocean.get_datatoken(ETH_USDT_ddo.datatokens[0]["address"])

from ocean_lib.ocean.util import to_wei
ETH_USDT_datatoken.dispense(to_wei(1), {"from":alice_wallet})
order_tx_id = ocean.assets.pay_for_access_service(ETH_USDT_ddo, {"from": alice_wallet})
file_name = ocean.assets.download_asset(ETH_USDT_ddo, alice_wallet,"./", order_tx_id)

The next two steps show two different approaches to open the file: Python native support, and Pandas Dataframes.

2. Approach: Open data via Python native file support

In the same Python console:

with open(file_name, "r") as file:
    data_str = file.read().rstrip().replace('"', '')
    cex_x = eval(data_str) #list of lists

# cex_x is a list of 1000 items, one for every hour, on the hour. 
#
# Each item has a list of 5 entries: 
# (0) timestamp (1) open price (2) high price (3) low price (4) close price
#
# Example item: [1662998400000, 1706.38, 1717.87, 1693, 1713.56]
# Timestamp is unix time, but in ms. To get unix time (in s), divide by 1000

# Example: get unix timestamps
uts = [xi[0]/1000 for xi in cex_x]

# Example: get close prices
close_prices = [xi[4] for xi in cex_x]

3. Approach: Open data via Pandas dataframe

In the same Python console:

import pandas as pd

# cex_x is a list of 1000 items, one for every hour, on the hour.
# Each item has a list of 5 entries:
# (0) timestamp (1) open price (2) high price (3) low price (4) close price

# Create initial DataFrame
df = pd.read_json(file_name)

# Add names to columns
df = df.rename(columns={0: 'TimeStamp', 1: 'Open', 2: 'Highest', 3: 'Lowest', 4: 'Close', 5: 'Volume'})

# Transform Timestamp from miliseconds to DateTime
df['TimeStamp'] = pd.to_datetime(df['TimeStamp'], unit="ms")

# Rename column Timestamp to Datetime
df = df.rename(columns={'TimeStamp': 'Datetime'})

To take this further, let's get the data for the hour of the day that makes sense for your analysis.

In the same Python console:

# If you only want the data at 19h every day
data_at_19h = df[df['Datetime'].dt.hour == 19]

# If you want the data every 12 hours, for example at midnight and at 12h every day
data_every_12h = df[(df['Datetime'].dt.hour == 0) | (df['Datetime'].dt.hour == 12)]