id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
35,802 | import blankly
The provided code snippet includes necessary dependencies for implementing the `price_event` function. Write a Python function `def price_event(price, symbol, state: blankly.StrategyState)` to solve the following problem:
This function will give an updated price every 15 seconds from our definition belo... | This function will give an updated price every 15 seconds from our definition below |
35,803 | import blankly
def init(symbol, state: blankly.StrategyState):
# Download price data to give context to the algo
state.variables['history'] = state.interface.history(symbol, to=150, return_as='deque',
resolution=state.resolution)['close']
state.varia... | null |
35,804 | from blankly import Strategy, StrategyState, Interface
from blankly import Alpaca
from blankly.indicators import sma
def init(symbol, state: StrategyState):
interface: Interface = state.interface
resolution = state.resolution
variables = state.variables
# initialize the historical data
variables['h... | null |
35,805 | from blankly import Strategy, StrategyState, Interface
from blankly import Alpaca
from blankly.indicators import sma
def price_event(price, symbol, state: StrategyState):
interface: Interface = state.interface
variables = state.variables
variables['history'].append(price)
sma200 = sma(variables['hist... | null |
35,806 | import blankly
from blankly import futures, Side
from blankly.futures import FuturesStrategyState
from blankly.futures.utils import close_position
def price_event(price, symbol, state: FuturesStrategyState):
prev_price = state.variables['prev_price']
position = state.interface.get_position(symbol)
# if th... | null |
35,807 | import blankly
from blankly import futures, Side
from blankly.futures import FuturesStrategyState
from blankly.futures.utils import close_position
def close_position(symbol: str, state: FuturesStrategyState):
"""
Exit a position
Args:
state: the StrategyState
symbol: the symbol to sell
... | null |
35,808 | import blankly
from blankly import Side
def price_event(price, symbol, state: blankly.StrategyState):
order = state.variables.get('order', None)
if order is None:
market = state.interface.market_order(symbol, Side.BUY, state.interface.cash / price)
order_size = state.interface.get_account(symb... | null |
35,809 | from blankly import Alpaca, Strategy, StrategyState
from blankly.metrics import cum_returns
from blankly import trunc
The provided code snippet includes necessary dependencies for implementing the `compare_price_event` function. Write a Python function `def compare_price_event(prices, symbols, state: StrategyState)` t... | Strategy: When the market is doing well, the strategy takes on more risk by holding a leveraged S&P 500 ETF. When the market is shaky, it folds into treasury bonds. |
35,810 | from blankly import Alpaca, Strategy, StrategyState
from blankly.metrics import cum_returns
from blankly import trunc
def init(symbols, state: StrategyState):
# Download price data of the four tickers: 'BND', 'BIL', 'UPRO', 'IEF'
for symbol in symbols:
history_name = str(symbol) + '_history'
st... | null |
35,811 | import blankly
from blankly import Side
from blankly.futures import FuturesStrategyState, FuturesStrategy, BinanceFutures
from blankly.futures.utils import close_position
def close_position(symbol: str, state: FuturesStrategyState):
"""
Exit a position
Args:
state: the StrategyState
symbol:... | null |
35,812 | import blankly
from blankly import Side
from blankly.futures import FuturesStrategyState, FuturesStrategy, BinanceFutures
from blankly.futures.utils import close_position
def close_position(symbol: str, state: FuturesStrategyState):
"""
Exit a position
Args:
state: the StrategyState
symbol:... | null |
35,813 | from blankly import Strategy, StrategyState, Interface
from blankly import Alpaca
from models import OrderPricingModel, OrderDecisionModel
def init(symbol, state: StrategyState):
# initialize this once and store it into state
variables = state.variables
variables['decision_model'] = OrderDecisionModel(symb... | null |
35,814 | from blankly import Strategy, StrategyState, Interface
from blankly import Alpaca
from models import OrderPricingModel, OrderDecisionModel
def price_event(price, symbol, state: StrategyState):
interface: Interface = state.interface
variables = state.variables
decision_model = variables['decision_model']
... | null |
35,815 | from blankly import Screener, Alpaca, ScreenerState
from blankly.indicators import rsi
def is_stock_buy(symbol, state: ScreenerState):
# This runs per stock
prices = state.interface.history(symbol, 40, resolution='1d',
return_as='list') # get past 40 data points
price ... | null |
35,816 | from blankly import Screener, Alpaca, ScreenerState
from blankly.indicators import rsi
def formatter(results, state: ScreenerState):
# results is a dictionary on a per-symbol basis
result_string = 'These are all the stocks that are currently oversold: \n'
for symbol in results:
if results[symbol]['... | null |
35,817 | from blankly import Strategy, StrategyState, Interface
from blankly import Alpaca
from blankly.utils import trunc
from blankly.indicators import macd
SHORT_PERIOD = 12
LONG_PERIOD = 26
SIGNAL_PERIOD = 9
def init(symbol, state: StrategyState):
interface = state.interface
resolution = state.resolution
variab... | null |
35,818 | from blankly import Strategy, StrategyState, Interface
from blankly import Alpaca
from blankly.utils import trunc
from blankly.indicators import macd
def price_event(price, symbol, state: StrategyState):
interface: Interface = state.interface
# allow the resolution to be any resolution: 15m, 30m, 1d, etc.
... | null |
35,819 | from typing import Any
from collections import deque
import numpy as np
import pandas as pd
def to_historical_returns(data: Any):
return pd.Series(data).diff().tolist() | null |
35,820 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
def check_series(data: Any):
def ema(data: Any, period: int = 50, use_series=False) -> Any:
if check_series(data):
use_series = True
dat... | null |
35,821 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data... | null |
35,822 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data... | null |
35,823 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
def check_series(data: Any):
def zlema(data: Any, period: int = 50, use_series=False) -> Any:
if check_series(data):
use_series = True
d... | null |
35,824 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data... | Finding the moving average of a dataset Args: data: (list) A list containing the data you want to find the moving average of period: (int) How far each average set should be |
35,825 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
def check_series(data: Any):
def hma(data: Any, period: int = 50, use_series=False) -> Any:
if check_series(data):
use_series = True
dat... | null |
35,826 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data... | null |
35,827 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data... | null |
35,828 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data... | null |
35,829 | from typing import Any
import numpy as np
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
... | null |
35,830 | from typing import Any
import numpy as np
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
... | null |
35,831 | from typing import Any
import numpy as np
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
def check_series(data: Any):
def absolute_price_oscillator(data, short_period=12, long_period=26, use_series=False):
if check_ser... | null |
35,832 | from typing import Any
import numpy as np
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
... | null |
35,833 | from typing import Any
import numpy as np
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
... | null |
35,834 | from typing import Any
import numpy as np
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def rsi(data: Any, period: int = 14, round_rsi: bool = False, use_series=False) -> np.array:
""" Implements RSI Indicator """
if period >= len(data):
retu... | Calculates Stochoastic RSI Courteous of @lukazbinden :param data: :param period: :param smooth_pct_k: :param smooth_pct_d: :return: |
35,835 | import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data.to_numpy()
return ... | null |
35,836 | import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
def check_series(data: Any):
def wad(high_data, low_data, close_data, use_series=False):
if check_series(high_data) or check_series(low_data) or check_series(close_data):
... | null |
35,837 | import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data.to_numpy()
return ... | null |
35,838 | import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data.to_numpy()
return ... | null |
35,839 | import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data.to_numpy()
return ... | null |
35,840 | import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data.to_numpy()
return ... | null |
35,841 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data... | null |
35,842 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data... | null |
35,843 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data... | null |
35,844 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data... | null |
35,845 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data... | null |
35,846 | from typing import Any
import pandas as pd
import tulipy as ti
from blankly.indicators.utils import check_series, convert_to_numpy
def convert_to_numpy(data: Any):
if isinstance(data, list) or isinstance(data, deque):
return np.fromiter(data, float)
elif isinstance(data, pd.Series):
return data... | null |
35,847 | import numpy as np
import pandas as pd
from blankly.utils.utils import info_print
def cagr(start_value, end_value, years):
if years == 0:
return 0.0
return (end_value / start_value) ** (1 / years) - 1 | null |
35,848 | import numpy as np
import pandas as pd
from blankly.utils.utils import info_print
def cum_returns(start_value, end_value):
return (end_value - start_value) / start_value | null |
35,849 | import numpy as np
import pandas as pd
from blankly.utils.utils import info_print
def sortino(returns, n=252, risk_free_rate=None):
returns = pd.Series(returns)
if risk_free_rate:
mean = returns.mean() * n - risk_free_rate
else:
mean = returns.mean() * n
std_neg = returns[returns < 0].s... | null |
35,850 | import numpy as np
import pandas as pd
from blankly.utils.utils import info_print
def sharpe(returns, n=252, risk_free_rate=None):
returns = pd.Series(returns)
if risk_free_rate:
mean = returns.mean() * n - risk_free_rate
else:
mean = returns.mean() * n
std = returns.std() * np.sqrt(n)
... | null |
35,851 | import numpy as np
import pandas as pd
from blankly.utils.utils import info_print
def max_drawdown(returns):
def calmar(returns, n=252):
return_series = pd.Series(returns)
max_draw = max_drawdown(return_series)
if max_draw == 0:
return 0.0
return return_series.mean() * n / abs(max_draw) | null |
35,852 | import numpy as np
import pandas as pd
from blankly.utils.utils import info_print
def volatility(returns, n=None):
return np.std(returns) * np.sqrt(n) if n else np.std(returns) | null |
35,853 | import numpy as np
import pandas as pd
from blankly.utils.utils import info_print
def variance(returns, n=None):
if len(returns) <= 1:
return 0.0
return np.nanvar(returns) * n if n else np.nanvar(returns)
def beta(returns, market_base_returns, n=None):
m = np.matrix([returns, market_base_returns])
... | null |
35,854 | import numpy as np
import pandas as pd
from blankly.utils.utils import info_print
def var(initial_value, returns, alpha: float):
returns_sorted = np.sort(returns)
index = int(alpha * len(returns_sorted))
return initial_value * abs(returns_sorted[index]) | null |
35,855 | import numpy as np
import pandas as pd
from blankly.utils.utils import info_print
def cvar(initial_value, returns, alpha):
returns_sorted = np.sort(returns)
index = int(alpha * len(returns_sorted))
sum_var = returns_sorted[0]
for i in range(1, index):
sum_var += returns_sorted[i]
return ini... | null |
35,856 | import blankly
The provided code snippet includes necessary dependencies for implementing the `price_event` function. Write a Python function `def price_event(price, symbol, state: blankly.StrategyState)` to solve the following problem:
This function will give an updated price every 15 seconds from our definition belo... | This function will give an updated price every 15 seconds from our definition below |
35,857 | import blankly
def init(symbol, state: blankly.StrategyState):
# Download price data to give context to the algo
state.variables.history = state.interface.history(symbol, to=150, return_as='deque',
resolution=state.resolution)['close']
# Get the max pr... | null |
35,858 | import requests
import blankly
from blankly.data import PriceReader
The provided code snippet includes necessary dependencies for implementing the `price_event` function. Write a Python function `def price_event(price, symbol, state: blankly.StrategyState)` to solve the following problem:
This function will give an up... | This function will give an updated price every 15 seconds from our definition below |
35,859 | import requests
import blankly
from blankly.data import PriceReader
def init(symbol, state: blankly.StrategyState):
# Download price data to give context to the algo
state.variables['history'] = state.interface.history(symbol, to=150, return_as='deque',
... | null |
35,860 | from blankly import Screener, EXCHANGE_CLASS, ScreenerState
def evaluator(symbol, state: ScreenerState):
pass | null |
35,861 | from blankly import Screener, EXCHANGE_CLASS, ScreenerState
def formatter(results, state: ScreenerState):
pass | null |
35,862 | import blankly
from blankly import Screener, EXCHANGE_CLASS, ScreenerState
from blankly.indicators import rsi
def is_stock_buy(symbol, state: ScreenerState):
# This runs per stock
prices = state.interface.history(symbol, 40, resolution='1d',
return_as='list') # get past 40... | null |
35,863 | import blankly
from blankly import Screener, EXCHANGE_CLASS, ScreenerState
from blankly.indicators import rsi
def formatter(results, state: ScreenerState):
# results is a dictionary on a per-symbol basis
result_string = 'These are all the stocks that are currently oversold: \n'
for symbol in results:
... | null |
35,864 | import json
import requests
from collections import OrderedDict
from blankly.utils.exceptions import APIException
class APIException(Exception):
pass
def api_error_handler(func):
def wrapper(*args, **kwargs):
resp = func(*args, **kwargs)
if 'errorMessage' in resp:
raise APIExceptio... | null |
35,865 | import secrets
def generate_coinbase_pro_id():
# Create coinbase pro-like id
coinbase_pro_id = secrets.token_hex(nbytes=16)
coinbase_pro_id = coinbase_pro_id[:8] + '-' + coinbase_pro_id[8:]
coinbase_pro_id = coinbase_pro_id[:13] + '-' + coinbase_pro_id[13:]
coinbase_pro_id = coinbase_pro_id[:18] + ... | null |
35,866 | import blankly.metrics as metrics
from blankly.utils.time_builder import build_year
def build_year() -> int:
def cagr(backtest_data):
account_values = backtest_data['resampled_account_value']
years = (account_values['time'].iloc[-1] - account_values['time'].iloc[0]) / build_year()
return round(metrics.cag... | null |
35,867 | import blankly.metrics as metrics
from blankly.utils.time_builder import build_year
def cum_returns(backtest_data):
account_values = backtest_data['resampled_account_value']
return round(metrics.cum_returns(account_values['value'][0], account_values['value'].iloc[-1]), 2) * 100 | null |
35,868 | import blankly.metrics as metrics
from blankly.utils.time_builder import build_year
def periods_per_year(period: int) -> float:
def sortino(backtest_data, trading_period=86400, risk_free_rate=0):
returns = backtest_data['returns']['value']
ppy = periods_per_year(trading_period)
return round(metrics.sortino... | null |
35,869 | import blankly.metrics as metrics
from blankly.utils.time_builder import build_year
def periods_per_year(period: int) -> float:
"""
Find how many trading periods occur within a trading year
Args:
period: the number of seconds in each trading period
Returns:
number of trading periods in e... | null |
35,870 | import blankly.metrics as metrics
from blankly.utils.time_builder import build_year
def periods_per_year(period: int) -> float:
"""
Find how many trading periods occur within a trading year
Args:
period: the number of seconds in each trading period
Returns:
number of trading periods in e... | null |
35,871 | import blankly.metrics as metrics
from blankly.utils.time_builder import build_year
def periods_per_year(period: int) -> float:
"""
Find how many trading periods occur within a trading year
Args:
period: the number of seconds in each trading period
Returns:
number of trading periods in e... | null |
35,872 | import blankly.metrics as metrics
from blankly.utils.time_builder import build_year
def periods_per_year(period: int) -> float:
"""
Find how many trading periods occur within a trading year
Args:
period: the number of seconds in each trading period
Returns:
number of trading periods in e... | null |
35,873 | import blankly.metrics as metrics
from blankly.utils.time_builder import build_year
def periods_per_year(period: int) -> float:
"""
Find how many trading periods occur within a trading year
Args:
period: the number of seconds in each trading period
Returns:
number of trading periods in e... | null |
35,874 | import blankly.metrics as metrics
from blankly.utils.time_builder import build_year
def var(backtest_data):
returns = backtest_data['returns']['value']
account_values = backtest_data['resampled_account_value']
return round(metrics.var(account_values['value'][0], returns, 0.95), 2) | null |
35,875 | import blankly.metrics as metrics
from blankly.utils.time_builder import build_year
def cvar(backtest_data):
returns = backtest_data['returns']['value']
account_values = backtest_data['resampled_account_value']
return round(metrics.cvar(account_values['value'][0], returns, 0.95), 2) | null |
35,876 | import blankly.metrics as metrics
from blankly.utils.time_builder import build_year
def max_drawdown(backtest_data):
values = backtest_data['returns']['value']
return abs(round(metrics.max_drawdown(values), 2)) * 100 | null |
35,877 | import base64
import hashlib
import hmac
import json
import time
import requests
from requests.auth import AuthBase
from blankly.utils.utils import info_print
def get_auth_headers(timestamp, message, api_key, secret_key, passphrase):
message = message.encode('ascii')
hmac_key = base64.b64decode(secret_key)
... | null |
35,878 | import json
import time
import traceback
import blankly
import blankly.exchanges.interfaces.coinbase_pro.coinbase_pro_websocket_utils as websocket_utils
from blankly.exchanges.interfaces.websocket import Websocket
from blankly.utils.utils import info_print
def create_ticker_connection(id, url, channel):
ws = None ... | null |
35,879 | import time
import blankly.utils.utils as utils
def no_callback(message):
return message
def trade(received):
return ', '.join([
received['time'],
time.time(),
received['price'],
received['open_24h'],
received['volume_24h'],
received['low_24h'],
received['... | null |
35,880 | import collections
import ssl
import threading
import time
import traceback
import msgpack
from websocket import create_connection
import blankly
from blankly.utils.utils import info_print
from blankly.exchanges.abc_exchange_websocket import ABCExchangeWebsocket
from blankly.exchanges.auth.utils import load_auth
from b... | null |
35,881 | from alpaca_trade_api.entity_v2 import trade_mapping_v2
from msgpack.ext import Timestamp
from blankly.utils.utils import isolate_specific, rename_to
def parse_alpaca_timestamp(value: Timestamp):
return value.seconds + (value.nanoseconds * float(1e-9)) | null |
35,882 | from alpaca_trade_api.entity_v2 import trade_mapping_v2
from msgpack.ext import Timestamp
from blankly.utils.utils import isolate_specific, rename_to
def no_callback(message):
return message
def no_logging_callback(message):
response = ""
for i in list(message.keys()):
response += str(message[i])
... | null |
35,883 | import alpaca_trade_api
from blankly.exchanges.auth.auth_constructor import AuthConstructor
live_url = "https://api.alpaca.markets"
paper_url = "https://paper-api.alpaca.markets"
class AuthConstructor(abc.ABC):
def __init__(self, keys_file: str, portfolio_name: str, exchange: str, needed_keys: list):
"""
... | null |
35,884 | import time
from operator import itemgetter
from typing import Optional
import binance.exceptions
from blankly.exchanges.interfaces.binance.binance_interface import BinanceInterface
from datetime import datetime as dt
import pandas as pd
from binance.client import Client
from binance.exceptions import BinanceAPIExcepti... | null |
35,885 | import time
import blankly.utils.utils as utils
def no_callback(message):
return message
def trade(received):
line = str(received['data']['ts'] / 1000) + "," + str(time.time()) + "," + received['data']['sodUtc0'] + "," + \
received[
'data']['open24h'] + "," + received['data']['volCcy24... | null |
35,886 | import requests
import hmac
import base64
import datetime
import json
def sign(message, secretKey):
mac = hmac.new(bytes(secretKey, encoding='utf8'), bytes(message, encoding='utf-8'), digestmod='sha256')
d = mac.digest()
return base64.b64encode(d) | null |
35,887 | import requests
import hmac
import base64
import datetime
import json
def pre_hash(timestamp, method, request_path, body):
return str(timestamp) + str.upper(method) + request_path + body | null |
35,888 | import requests
import hmac
import base64
import datetime
import json
CONTENT_TYPE = 'Content-Type'
OK_ACCESS_KEY = 'OK-ACCESS-KEY'
OK_ACCESS_SIGN = 'OK-ACCESS-SIGN'
OK_ACCESS_TIMESTAMP = 'OK-ACCESS-TIMESTAMP'
OK_ACCESS_PASSPHRASE = 'OK-ACCESS-PASSPHRASE'
APPLICATION_JSON = 'application/json'
def get_header(api_key, s... | null |
35,889 | import requests
import hmac
import base64
import datetime
import json
def parse_params_to_str(params):
url = '?'
for key, value in params.items():
url = url + str(key) + '=' + str(value) + '&'
return url[0:-1] | null |
35,890 | import requests
import hmac
import base64
import datetime
import json
def get_timestamp():
now = datetime.datetime.utcnow()
t = now.isoformat("T", "milliseconds")
return t + "Z" | null |
35,891 | import requests
import hmac
import base64
import datetime
import json
def signature(timestamp, method, request_path, body, secret_key):
if str(body) == '{}' or str(body) == 'None':
body = ''
message = str(timestamp) + str.upper(method) + request_path + str(body)
mac = hmac.new(bytes(secret_key, en... | null |
35,892 | import hashlib
import hmac
import time
from collections import OrderedDict
from urllib.parse import urlencode
import requests
from requests.auth import AuthBase
def hmac_encode(message: str, secret_key: str) -> str:
assert isinstance(message, str)
assert isinstance(secret_key, str)
signature = hmac.new(sec... | null |
35,893 | import time
import blankly.utils.utils as utils
def no_callback(message):
return message
def depth(message):
return message
def depth_interface(message):
return message
def trade(message):
return str(message["E"]) + "," + str(time.time()) + "," + message["e"] + "," + message["s"] + "," + \
st... | null |
35,894 | import time
import blankly.utils.utils as utils
from typing import List, Dict, Union, Any
from blankly.exchanges.interfaces.coinbase_pro import coinbase_pro_websocket_utils
def no_callback(message):
return message
def process_trades(response: dict) -> Dict[str, Union[float, Any]]:
output = {
'trade_id':... | null |
35,895 | import time
from blankly.utils.utils import isolate_specific
def no_callback(message):
return message
def trade(message):
line = str(time.time()) + "," + message["sequence"] + "," + message["price"] + "," + \
message["size"] + "," + message["bestAsk"] + "," + message["bestAskSize"] + "," + \
... | null |
35,896 | import json
from blankly.utils.utils import info_print
auth_cache = {}
The provided code snippet includes necessary dependencies for implementing the `write_auth_cache` function. Write a Python function `def write_auth_cache(exchange, name, auth)` to solve the following problem:
Write an authenticated object into the ... | Write an authenticated object into the global authentication cache. This can be used by other pieces of code in the module to pull from exchanges at points they need the API Args: exchange (str): Exchange name ex: "coinbase_pro" or "binance" name (str): Portfolio name ex: "my cool portfolio" auth (obj): Authenticated o... |
35,897 | import json
from blankly.utils.utils import info_print
auth_cache = {}
The provided code snippet includes necessary dependencies for implementing the `read_auth_cache` function. Write a Python function `def read_auth_cache(exchange, name=None)` to solve the following problem:
Pull an authenticated object on an exchang... | Pull an authenticated object on an exchange. This can be used for API calls anywhere in the code. |
35,898 | import json
from blankly.utils.utils import info_print
def load_json(keys_file):
try:
f = open(keys_file)
contents = json.load(f)
f.close()
return contents
except FileNotFoundError:
raise FileNotFoundError("Make sure a Keys.json file is placed in the same folder as the pr... | null |
35,899 | import traceback
import warnings
import random
from typing import List
import requests
import blankly.exchanges.auth.utils
import blankly.utils.utils
from blankly.exchanges.interfaces.alpaca.alpaca_websocket import Tickers as Alpaca_Websocket
from blankly.exchanges.interfaces.binance.binance_websocket import Tickers as... | null |
35,900 | import traceback
import warnings
import random
from typing import List
import requests
import blankly.exchanges.auth.utils
import blankly.utils.utils
from blankly.exchanges.interfaces.alpaca.alpaca_websocket import Tickers as Alpaca_Websocket
from blankly.exchanges.interfaces.binance.binance_websocket import Tickers as... | null |
35,901 | import argparse
import sys
import traceback
import warnings
import os
import platform
import runpy
import time
import requests
import json
import zipfile
import tempfile
import webbrowser
from blankly.deployment.api import API
from blankly.utils.utils import load_json_file, info_print, load_deployment_settings
deployme... | null |
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