evanaze

def get_transaction_data(txn_hash):
    "gets the data for the specified transaction hash"
    # the url with the given hash
    url = f"https://web3api.io/api/v2/transactions/{txn_hash}"
    # the headers
    headers = {
        "x-amberdata-blockchain-id": "bitcoin-mainnet",
        "x-api-key": api_key["AMBERDATA_API_KEY"]
    }
    # get the response

evanaze

def parse_addresses(i: int = 0, n: int = 0) -> str:
    """Returns the address from record i in position n"""
    # parse the list of addresses
    address_list = data_sorted.iloc[i, 1].split("['")[-1].split("']")[0]
    return address_list.split("', '")[n]
  
# the top transaction number we consider
i = 0
# the sender's address
sender = parse_addresses(i)

evanaze

i = 0              # initializing i
win_size = 7       # the window size after a large txn
windows = []       # an array for saving our windows

# iterate through the ohlcv data
while i <= len(ohlcv):
    # if the transaction is a large transaction
    if ohlcv.iloc[i, -1] == True:
        # save the txn and the minutes after
        windows.append(ohlcv.iloc[i:i+win_size])

evanaze

async def listen(api_key):
    "Opens the websocket connection and listens for pending transactions"
    # the amberdata websocket uri
    uri = 'wss://ws.web3api.io'
    # our headers for the connection
    headers = {
        "x-api-key": api_key["AMBERDATA_API_KEY"],
        "x-amberdata-blockchain-id": "408fa195a34b533de9ad9889f076045e"
    }
    # outer loop

evanaze

dfs = []
n_days = 5

for i in range(n_days):
    print(f"Iteration: {i}")
    # get the start and end dates in timestamps
    startDate = results.timestamp.min() + timedelta(i-1)
    endDate = startDate + timedelta(1)
    
    # convert to UNIX format

evanaze

# helper functions
def to_df(response):
    "Turns the repsonse into a dataframe"
    # load the data into a DataFrame and rename the columns
    data = pd.DataFrame(response["data"]).rename({0: "date", 1: "volatility", 2: "sharpe"}, axis=1)
    # turn the date column into datetime type
    data["date"] = pd.to_datetime(data["date"])
    # set the date as the index
    data.set_index("date", inplace=True)
    return data

evanaze

# helper functions
def CAGR(DF):
    "Calculates the Compound Annual Growth Rate"
    df = DF.copy()
    df['daily_ret'] = df['Adj Close'].pct_change()
    df['cum_return'] = (1 + df['daily_ret']).cumprod()
    return (df['cum_return'])**(252/len(df)) - 1

def volatility(DF):
    "Calculates the daily Volatility over a year, given we trade 252x a year"

evanaze

# The S&P 500 Index
spy = pdr.get_data_yahoo("SPY", start, end)
# Gold futures
gold = pdr.get_data_yahoo("GC=F", start, end)
# Apple Stock
aapl = pdr.get_data_yahoo("AAPL", start, end)
# Bond index
bond = pdr.get_data_yahoo("BND", start, end)

evanaze

# load the data into a DataFrame and rename the columns
data = pd.DataFrame(response["data"]).rename({0: "date", 1: "volatility", 2: "sharpe"}, axis=1)
# turn the date column into datetime type
data["date"] = pd.to_datetime(data["date"])
# set the date as the index
data.set_index("date", inplace=True)

evanaze

# our first token to try out
token = "btc"

# the url for our token
url = f"https://web3api.io/api/v2/market/metrics/{token}/historical/sharpe"

# our API key
headers = {'x-api-key': api_key["AMBERDATA_API_KEY"]}

# the start and end of the period