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December 23, 2019 00:55
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Download data from Alphavantage http://www.alphavantage.co/ using Python, Requests and Pandas
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import requests | |
from pandas.io.common import urlencode | |
from pandas.tseries.frequencies import to_offset | |
ALPHAVANTAGE_API_URL = "http://www.alphavantage.co/query" | |
ALPHAVANTAGE_API_KEY_DEFAULT = "demo" | |
def _init_session(session): | |
if session is None: | |
session = requests.Session() | |
return session | |
def _url(url, params): | |
if params is not None and len(params) > 0: | |
return url + "?" + urlencode(params) | |
else: | |
return url | |
def get_ts_data(symbol, interval=None, outputsize=None, api_key=None, adjusted=False, session=None): | |
session = _init_session(session) | |
# apikey | |
if api_key is None: | |
api_key = ALPHAVANTAGE_API_KEY_DEFAULT | |
# function | |
d_functions = { | |
to_offset("D").freqstr: "TIME_SERIES_DAILY", | |
to_offset("W").freqstr: "TIME_SERIES_WEEKLY", | |
to_offset("M").freqstr: "TIME_SERIES_MONTHLY", | |
} | |
try: | |
if adjusted and to_offset(interval).freqstr == "D": | |
function_api = "TIME_SERIES_DAILY_ADJUSTED" | |
else: | |
function_api = d_functions[to_offset(interval).freqstr] | |
except KeyError: | |
function_api = "TIME_SERIES_INTRADAY" | |
# interval | |
if interval is None: | |
interval = "D" | |
d_intervals = { | |
to_offset("1T").freqstr: "1min", | |
to_offset("5T").freqstr: "5min", | |
to_offset("15T").freqstr: "15min", | |
to_offset("30T").freqstr: "30min", | |
to_offset("H").freqstr: "60min", | |
to_offset("D").freqstr: "daily", | |
to_offset("W").freqstr: "weekly", | |
to_offset("M").freqstr: "monthly" | |
} | |
try: | |
interval_api = d_intervals[to_offset(interval).freqstr] | |
except KeyError: | |
interval_api = "60min" | |
# outputsize | |
if outputsize is None: | |
outputsize = "compact" | |
params = { | |
"function": function_api, | |
"symbol": symbol, | |
"interval": interval_api, | |
"outputsize": outputsize, | |
"apikey": api_key | |
} | |
r = session.get(ALPHAVANTAGE_API_URL, params=params) | |
url_long= _url(ALPHAVANTAGE_API_URL, params) | |
# print(url_long) | |
if r.status_code == requests.codes.ok: | |
dat = r.json() | |
metadata = dat["Meta Data"] | |
key_dat = list(dat.keys())[1] # ugly | |
ts = dat[key_dat] | |
df = pd.DataFrame(ts).T | |
df = df.rename(columns={ | |
"1. open": "Open", | |
"2. high": "High", | |
"3. low": "Low", | |
"4. close": "Close", | |
"5. volume": "Volume", | |
}) | |
for col in ["Open", "High", "Low", "Close"]: | |
if col in df.columns: | |
df[col] = df[col].astype(float) | |
df["Volume"] = df["Volume"].astype(int) | |
df.index = pd.to_datetime(df.index) | |
df.index.name = "Date" | |
return df, metadata | |
else: | |
params["apikey"] = "HIDDEN" | |
raise Exception(r.status_code, r.reason, url_long) | |
def get_sector_performances(api_key=None, session=None): | |
session = _init_session(session) | |
# apikey | |
if api_key is None: | |
api_key = ALPHAVANTAGE_API_KEY_DEFAULT | |
params = { | |
"function": "SECTOR", | |
"apikey": api_key | |
} | |
url_long = _url(ALPHAVANTAGE_API_URL, params) | |
print(url_long) | |
r = session.get(ALPHAVANTAGE_API_URL, params=params) | |
if r.status_code == requests.codes.ok: | |
dat = r.json() | |
metadata = dat["Meta Data"] | |
del dat["Meta Data"] | |
df = pd.DataFrame(dat) | |
for col in df.columns: | |
df[col] = df[col].str.strip("%").astype(float) | |
return df, metadata | |
else: | |
params["apikey"] = "HIDDEN" | |
raise Exception(r.status_code, r.reason, url_long) | |
import os | |
import pandas as pd | |
pd.set_option("max_rows", 10) | |
import datetime | |
import requests_cache | |
expire_after = datetime.timedelta(days=1) | |
session = requests_cache.CachedSession(cache_name='cache', backend='sqlite', expire_after=expire_after) | |
api_key = os.environ.get("ALPHAVANTAGE_API_KEY") # api_key = "YOURAPIKEY" | |
df, metadata = get_ts_data("MSFT", interval="W", api_key=api_key, session=session) | |
print(df) | |
print(df.dtypes) | |
print(metadata) | |
df, metadata = get_ts_data("MSFT", interval="15Min", api_key=api_key, session=session) | |
print(df) | |
print(df.dtypes) | |
print(metadata) | |
df, metadata = get_ts_data("MSFT", interval="D", api_key=api_key, session=session) | |
print(df) | |
print(df.dtypes) | |
print(metadata) | |
df, metadata = get_ts_data("MSFT", interval="D", outputsize="full", api_key=api_key, session=session) | |
print(df) | |
print(df.dtypes) | |
print(metadata) | |
df, metadata = get_sector_performances(api_key=api_key, session=session) | |
print(df) | |
print(df.dtypes) | |
print(metadata) |
ToDo: create a GitHub project (similar to https://github.com/femtotrader/python-eodhistoricaldata )
CSV output is now available https://www.alphavantage.co/documentation/
Optional: datatype
By default, datatype=json. Strings json and csv are accepted
When i tried to run this code shows this message D:\Code> python alphavantage.py Traceback (most recent call last): File "alphavantage.py", line 133, in <module> import requests_cache ModuleNotFoundError: No module named 'requests_cache'
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From pydata/pandas-datareader#315