def get_tweets(self, ticker, search_terms=None):
"""Get tweets about a ticker
:param ticker:
:param search_terms: optional additional search term
:return: string consisting of text from tweets
"""
if not search_terms or search_terms is None:
query = "{} stock".format(ticker)
else:
query = "{} stock {}".format(ticker, search_terms)
results = self.api.GetSearch(query, result_type="recent", count=100)
result_dicts = [
dict(created_at=time_formatter(float(
result.created_at_in_seconds)),
user=result.user.name,
text=result.text) for result in results
if "RT" not in result.text
]
texts = [res["text"] for res in result_dicts]
text = "\n\n".join(texts)
return text
def sec_filings(self,
barcount=64,
poolsize=5,
form_type="8-K",
backdate=None):
"""Return tradeable asets with recent SEC filings.
:param barcount:
:param poolsize:
:param form_type:
:param backdate:
:return:
"""
if not self.edgar_token or self.edgar_token is None:
raise NotImplementedError
if not backdate or backdate is None:
# backdate = time_formatter(time.time() - 604800, time_format="%Y-%m-%d")
# using a longer window only for debugging purposes -- just to make sure I have results quickly
backdate = time_formatter(time.time() - (604800 * 26),
time_format="%Y-%m-%d")
date = time_formatter(time.time(), time_format="%Y-%m-%d")
# Filter the assets down to just those on NASDAQ.
active_assets = self.get_assets()
assets = self.extract_tradeable_assets(active_assets)
print("Going through assets looking for firms with recent SEC filings")
for i in assets:
self.get_filings(i,
backdate=backdate,
date=date,
form_type=form_type)
if len(self.recent_filings.keys()) >= poolsize:
break
else:
continue
self.tradeable_assets_by_filings(barcount)
return self.assets_by_filing
def get_filings(self, asset, backdate, date, form_type="8-K"):
"""Given a trading entity, get SEC filings in the date range.
:param asset:
:param backdate:
:param date:
:param form_type:
:return:
"""
filings = self.ei.get_sec_filings(asset.symbol,
backdate,
date,
form_type=form_type)
# If none are found, lengthen the lookback window a couple times
if filings["total"] is 0:
print(
"No recent filings found for {}. Looking back 2 weeks".format(
asset.symbol))
backdate = time_formatter(time.time() - (604800 * 2),
time_format="%Y-%m-%d")
filings = self.ei.get_sec_filings(asset.symbol,
backdate,
date,
form_type=form_type)
if filings["total"] is 0:
print("No filings found. Looking back 4 weeks")
backdate = time_formatter(time.time() - (604800 * 4),
time_format="%Y-%m-%d")
filings = self.ei.get_sec_filings(asset.symbol,
backdate,
date,
form_type=form_type)
if filings["total"] > 0:
print("\tAdded:", asset.symbol, " symbols:",
len(self.recent_filings.keys()) + 1)
filings = json.dumps(filings)
self.recent_filings[asset.symbol] = filings
def get_vzo_dmi_apz(self, ticker, backdate=None):
"""Given a ticker symbol and a historic, calculate various indicators from then to now.
Currently, VZO, DMI and APZ oscillator are implemented.
:param ticker: a stock ticker symbol
:param backdate: start of the historic data lookup period. If none, defaults to the last 13 weeks (1 quarter)
:return: dataframe built from barset objects, including indicators
"""
if not ticker or ticker is None:
raise ValueError("Invalid ticker value")
if not backdate or backdate is None:
backdate = time_formatter(time.time() - (604800 * 13))
bars = self.get_bars(ticker, backdate)
data = set_candlestick_df(bars)
print(data["close"].iloc[-1], data["close"].iloc[-2], data["close"].iloc[-3])
# get VZO - bullish trend range 5-40%; oversold - -40%; extremely oversold -60%
try:
vzo = self.get_vzo(data)
except IndicatorException:
raise IndicatorException
else:
pass
# get DMI - buy sign when + line crosses over - line
try:
dmi = self.get_dmi(data)
except IndicatorException:
raise IndicatorException
else:
pass
# get APZ - volatility indicator
try:
apz = self.get_apz(data)
except IndicatorException:
raise IndicatorException
else:
pass
data["ticker"] = ticker
data["vzo"] = vzo
data["dmi_p"] = dmi["DI+"]
data["dmi_m"] = dmi["DI-"]
data["apz_u"] = apz["UPPER"]
data["apz_l"] = apz["LOWER"]
return data
def get_edgar_signals(self):
"""Loop through tickers and append EDGAR signals to that ticker's dataframe"""
if not self.dataframe or self.dataframe is None:
raise NotImplementedError(
"Dataframe should only be none if AssetSelector is using it")
for ticker in self.dataframe.keys():
fdate = self.dataframe[ticker]["time"].iloc[-7].strftime(
"%Y-%m-%d")
tdate = time_formatter(time.time(), time_format="%Y-%m-%d")
self.edgar_scores[ticker] = self.calculate_edgar_signal(
ticker, fdate, tdate)
return self.edgar_scores
def get_macd_mfi_stoch(self, ticker, backdate=None):
"""Given a ticker symbol and a historic, calculate various indicators from then to now.
Currently, MACD, MFI and stochastic oscillators are implemented.
:param ticker: a stock ticker symbol
:param backdate: start of the historic data lookup period. If none, defaults to the last 13 weeks (1 quarter)
:return: dataframe built from barset objects, including indicators
"""
if not ticker or ticker is None:
raise ValueError("Invalid ticker value")
if not backdate or backdate is None:
backdate = time_formatter(time.time() - (604800 * 13))
bars = self.get_bars(ticker, backdate)
data = set_candlestick_df(bars)
# get MACD
try:
macd = self.get_macd(data)
except IndicatorException:
raise IndicatorException
else:
_macds = macd["MACD"]
_signals = macd["SIGNAL"]
# get money flow index
try:
mfi = self.get_mfi(data)
except IndicatorException:
raise IndicatorException
else:
pass
# get stochastic oscillator
try:
stoch = self.get_stoch(data)
except IndicatorException:
raise IndicatorException
else:
pass
data["ticker"] = ticker
data["macd"] = _macds
data["signal"] = _signals
data["mfi"] = mfi
data["stoch"] = stoch
return data
def tradeable_assets_by_filings(self, barcount=64):
"""Populate tradeable assets based on discovered SEC filings.
:return:
"""
for i in self.recent_filings.keys():
# I think I need my original 13 week window here for consistency with get_assets_by_candlestick_pattern
backdate = time_formatter(time.time() - (604800 * 13))
barset = self.get_barset(i.symbol, "1D", backdate)
if num_bars(barset[i.symbol], barcount) is False:
continue
df = self.extract_bar_data(barset, i.symbol)
self.assets_by_filing[i.symbol] = df
def get_bars(self, ticker, backdate=None):
"""Get bars for a ticker symbol
:param ticker: a stock ticker symbol
:param backdate: start of the historic data lookup period. If none, defaults to the last 13 weeks (1 quarter)
:return: dataframe built from barset objects, including indicators
"""
if not ticker or ticker is None:
raise ValueError("Invalid ticker value")
if not backdate or backdate is None:
backdate = time_formatter(time.time() - (604800 * 13))
bars = None
try:
bars = self.api.get_barset(ticker, "1D", after=backdate)[ticker]
except HTTPError:
print("Retrying...")
time.sleep(3)
try:
bars = self.api.get_barset(ticker, "1D", after=backdate)[ticker]
except HTTPError:
raise HTTPError
finally:
return bars
def compare_macd_vmacd(self, ticker, backdate=None):
"""Compare different MACD indicators."""
if not ticker or ticker is None:
raise ValueError("Invalid ticker value")
if not backdate or backdate is None:
backdate = time_formatter(time.time() - (604800 * 13))
bars = self.get_bars(ticker, backdate)
data = set_candlestick_df(bars)
# get MACD
try:
macd = self.get_macd(data)
except IndicatorException:
raise IndicatorException
else:
_macds = macd["MACD"]
_signals = macd["SIGNAL"]
# get VW_MACD
try:
vwmacd = self.get_vwmacd(data)
except IndicatorException:
raise IndicatorException
else:
_vmacds = vwmacd["MACD"]
_vsignals = vwmacd["SIGNAL"]
data["ticker"] = ticker
data["macd"] = _macds
data["signal"] = _signals
data["vwmacd"] = _vmacds
data["vwsignal"] = _vsignals
return data
def __init__(self, alpaca_api_interface, backdate=None, edgar_token=None):
if not alpaca_api_interface or alpaca_api_interface is None:
raise ValueError("Alpaca API interface instance required.")
if not backdate or backdate is None:
backdate = time_formatter(time.time() - (604800 * 13))
self.api = alpaca_api_interface
self.backdate = backdate
self.edgar_token = None
if edgar_token is not None:
self.edgar_token = edgar_token
self.ei = EdgarInterface(self.edgar_token)
self.raw_assets = None
self.tradeable_assets = None
self.bulls = dict()
self.bears = dict()
self.recent_filings = dict()
self.assets_by_filing = dict()
self.populate()
def cluster_prep(self, ticker, backdate=None):
"""Compare different MACD indicators."""
if not ticker or ticker is None:
raise ValueError("Invalid ticker value")
if not backdate or backdate is None:
backdate = time_formatter(time.time() - (604800 * 13))
bars = self.get_bars(ticker, backdate)
data = set_candlestick_df(bars)
# get Bollinger bands
try:
bbands = self.get_bbands(data)
except IndicatorException:
raise IndicatorException
else:
_bb_up = bbands["BB_UPPER"]
_bb_mid = bbands["BB_MIDDLE"]
_bb_low = bbands["BB_LOWER"]
# get MACD
try:
macd = self.get_macd(data)
except IndicatorException:
raise IndicatorException
else:
_macds = macd["MACD"]
_signals = macd["SIGNAL"]
# get VW_MACD
try:
vwmacd = self.get_vwmacd(data)
except IndicatorException:
raise IndicatorException
else:
_vmacds = vwmacd["MACD"]
_vsignals = vwmacd["SIGNAL"]
# get money flow index
try:
mfi = self.get_mfi(data)
except IndicatorException:
raise IndicatorException
else:
pass
# get stochastic oscillator
try:
stoch = self.get_stoch(data)
except IndicatorException:
raise IndicatorException
else:
pass
# get VZO - bullish trend range 5-40%; oversold - -40%; extremely oversold -60%
try:
vzo = self.get_vzo(data)
except IndicatorException:
raise IndicatorException
else:
pass
# get ADX - trending if > 20; > 40 is strong trend; > 50 is very strongtrend
# Welp this uses the broken indicator, therefore it is also broken.
# try:
# adx = self.get_adx(data)
# except IndicatorException:
# raise IndicatorException
# else:
# pass
# get APZ - volatility indicator -- might be useful in sentiment comparison
try:
apz = self.get_apz(data)
except IndicatorException:
raise IndicatorException
else:
pass
# get DMI - buy sign when + line crosses over - line
try:
dmi = TA.DMI(data)
except IndicatorException:
raise IndicatorException
else:
pass
# ¯\_(ツ)_/¯ seems to be a bug in finta DMI implementation - returning lists of NaN
# TODO: investigate - https://github.com/bbeale/finta
# Seems to be appending the values I am expecting it to return directly to my dataframe instead of returning them.
# Hmm...
# data["dmi_p"] = dmi["DMp"]
# data["dmi_m"] = dmi["DMm"]
# data["ticker"] = ticker
data["bb_up"] = _bb_up
data["bb_mid"] = _bb_mid
data["bb_low"] = _bb_low
data["macd"] = _macds
data["signal"] = _signals
data["vwmacd"] = _vmacds
data["vwsignal"] = _vsignals
data["mfi"] = mfi
data["stoch"] = stoch
data["vzo"] = vzo
# data["adx"] = adx
data["apz_u"] = apz["UPPER"]
data["apz_l"] = apz["LOWER"]
# Percent changes
data["bb_up_ptc"] = _bb_up.pct_change()
data["bb_mid_ptc"] = _bb_mid.pct_change()
data["bb_low_ptc"] = _bb_low.pct_change()
data["macd_ptc"] = _macds.pct_change()
data["signal_ptc"] = _signals.pct_change()
data["vwmacd_ptc"] = _vmacds.pct_change()
data["vwsignal_ptc"] = _vsignals.pct_change()
data["mfi_ptc"] = mfi.pct_change()
data["stoch_ptc"] = stoch.pct_change()
data["vzo_ptc"] = vzo.pct_change()
# data["adx_ptc"] = adx.pct_change()
data["apz_u_ptc"] = apz["UPPER"].pct_change()
data["apz_l_ptc"] = apz["LOWER"].pct_change()
return data
