File size: 6,060 Bytes
1609dbf
c5f202a
b7f33b4
1609dbf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8dcb148
 
 
 
 
 
 
 
 
 
 
 
 
1609dbf
b7f33b4
 
 
 
1609dbf
cf27d1b
b7f33b4
005b424
 
 
 
 
 
 
 
 
 
 
1609dbf
 
8dcb148
4b6a96c
 
1609dbf
 
 
 
 
 
 
 
 
cf27d1b
4b6a96c
 
 
1609dbf
 
4b6a96c
1609dbf
cf27d1b
b7f33b4
005b424
 
 
 
 
 
 
 
 
 
 
1609dbf
 
8dcb148
1609dbf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
cf27d1b
4b6a96c
 
 
1609dbf
 
4b6a96c
1609dbf
 
 
 
4b6a96c
 
 
 
 
1609dbf
 
 
 
4b6a96c
1609dbf
4b6a96c
1609dbf
4b6a96c
1609dbf
 
 
4b6a96c
1609dbf
 
 
 
 
 
cf27d1b
4b6a96c
 
 
 
c5f202a
4b6a96c
 
1609dbf
 
 
 
 
 
4b6a96c
 
 
 
 
 
1609dbf
 
cf27d1b
1609dbf
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
import pandas as pd
import pytz
import cloudscraper
import yfinance as yf

from typing import List
from types import SimpleNamespace


def clean_etf_data(df):
    """
    Clean ETF data
    """
    # Copy original
    df_original = df.copy()
    # Set date as index
    df_original["Date"] = pd.to_datetime(df_original["Date"])

    # Format outflow to negative value
    df = df.drop(columns="Date")
    df.replace(to_replace=r"\(([0-9.]+)\)", value=r"-\1", regex=True, inplace=True)

    # Replace '-' with 0
    df.replace("-", 0, inplace=True)

    # Convert from strings to numeric
    df = df.apply(pd.to_numeric)
    df["Date"] = df_original["Date"]

    return df, df_original


def extract_date_index(df):
    """
    Extract index from dataframe as Date
    """
    # Convert Series to DataFrame
    if isinstance(df, pd.Series):
        df = df.to_frame()
    df = df.reset_index(names="Date")
    # Set date as index
    df.Date = pd.to_datetime(df.Date)

    return df

FETCH_HEADER = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:98.0) Gecko/20100101 Firefox/98.0",
        "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
        "Accept-Language": "en-US,en;q=0.5",
        "Accept-Encoding": "gzip, deflate",
        "Connection": "keep-alive",
        "Upgrade-Insecure-Requests": "1",
        "Sec-Fetch-Dest": "document",
        "Sec-Fetch-Mode": "navigate",
        "Sec-Fetch-Site": "none",
        "Sec-Fetch-User": "?1",
        "Cache-Control": "max-age=0",
    }

def scrape(url: str):
    scraper = cloudscraper.create_scraper()
    return scraper.get(url)

def fetch_btc_etf():
    url = "https://farside.co.uk/bitcoin-etf-flow-all-data/"
    r = scrape(url)
    print(r.status_code)
    if r.status_code != 200:
        print(r.content)
        btc_etf_flow=pd.DataFrame.from_dict({"Date":["11 Jan 2024", "12 Jan 2024"], "Total": [0,0]})
        btc_etf_flow, btc_etf_flow_original = clean_etf_data(btc_etf_flow)
        return SimpleNamespace(
            url=url,
            flow=btc_etf_flow,
            orig=btc_etf_flow_original,
            funds=[],
        )
    # Get Bitcoin spot ETF history
    btc_etf_flow = pd.read_html(
        r.content,
        attrs={"class": "etf"},
        skiprows=[1],
    )[0]
    # Remove summary lines
    btc_etf_flow = btc_etf_flow.iloc[:-4]
    # Extract symbols of ETF funds
    btc_etf_funds = btc_etf_flow.drop(columns=["Date", "Total"]).columns.to_list()

    btc_etf_flow, btc_etf_flow_original = clean_etf_data(btc_etf_flow)

    return SimpleNamespace(
        url=url,
        flow=btc_etf_flow,
        orig=btc_etf_flow_original,
        funds=btc_etf_funds,
    )


def fetch_eth_etf():
    url = "https://farside.co.uk/ethereum-etf-flow-all-data/"
    r = scrape(url)
    print(r.status_code)
    if r.status_code != 200:
        print(r.content)
        eth_etf_flow=pd.DataFrame.from_dict({"Date":["11 Jan 2024", "12 Jan 2024"], "Total": [0,0]})
        eth_etf_flow, eth_etf_flow_original = clean_etf_data(eth_etf_flow)
        return SimpleNamespace(
            url=url,
            flow=eth_etf_flow,
            orig=eth_etf_flow_original,
            funds=[],
        )
    # Get Ethereum spot ETF history
    eth_etf_flow = pd.read_html(
        r.content,
        attrs={"class": "etf"},
        skiprows=[2, 3],
    )[0]
    # Drop column index level 2
    eth_etf_flow.columns = eth_etf_flow.columns.droplevel(2)
    # Extract symbols of ETF funds
    eth_etf_funds = (
        eth_etf_flow.drop(columns="Total").columns[1:].get_level_values(1).to_list()
    )
    # Merge multi-index columns
    eth_etf_flow.columns = eth_etf_flow.columns.map(" - ".join)
    # Name first column "Date"
    eth_etf_flow.rename(
        columns={
            "Unnamed: 0_level_0 - Unnamed: 0_level_1": "Date",
            "Total - Unnamed: 10_level_1": "Total",
        },
        inplace=True,
    )
    # Remove summary lines
    eth_etf_flow = eth_etf_flow.iloc[:-1]
    eth_etf_flow, eth_etf_flow_original = clean_etf_data(eth_etf_flow)

    return SimpleNamespace(
        url=url,
        flow=eth_etf_flow,
        orig=eth_etf_flow_original,
        funds=eth_etf_funds,
    )


def fetch_etf_volumes(funds: List[str], start_time=None):
    etf_volumes = pd.DataFrame()
    for fund in funds:
        etf_volumes[fund] = yf.download(
            str(fund),
            interval="1d",
            period="max",
            start=start_time,
        )["Volume"]
    etf_volumes = extract_date_index(etf_volumes)

    return etf_volumes


def fetch_asset_price(ticker: str, start_time=None):
    price = yf.download(ticker, interval="1d", period="max", start=start_time)["Close"]
    price = extract_date_index(price)
    price.rename(columns={"Close": "Price"}, inplace=True)

    return price


def fetch(asset):
    if asset == "BTC":
        df = fetch_btc_etf()
    else:
        df = fetch_eth_etf()

    etf_flow, etf_funds, etf_url = df.flow, df.funds, df.url
    tz = pytz.timezone("America/New_York")

    etf_flow, etf_funds = df.flow, df.funds
    tz = pytz.timezone("America/New_York")
    start_time = tz.localize(etf_flow.Date[0])
    etf_volumes = fetch_etf_volumes(etf_funds, start_time=start_time)
    price = fetch_asset_price(f"{asset}-USD", start_time=start_time)

    etf_flow_individual = etf_flow.drop(columns="Total")
    etf_flow_total = etf_flow[["Date", "Total"]]

    cum_flow_individual = etf_flow_individual.drop(columns="Date").cumsum()
    cum_flow_individual["Date"] = etf_flow_individual.Date
    cum_flow_total = pd.DataFrame(
        {
            "Date": etf_flow_total.Date,
            "Total": etf_flow_total.Total.cumsum(),
        }
    )

    return SimpleNamespace(
        url=etf_url,
        etf_flow=etf_flow,
        etf_volumes=etf_volumes,
        price=price,
        etf_flow_individual=etf_flow_individual,
        etf_flow_total=etf_flow_total,
        cum_flow_individual=cum_flow_individual,
        cum_flow_total=cum_flow_total,
    )