def buy_signals(strategies: List[str] = typer.Argument(...), ):
with typer.progressbar(tse.all_symbols()) as progress:
result = tse.all_symbols()
for symbol in progress:
ticker = tse.Ticker(symbol)
df = ticker.history
for strategy in strategies:
strategy = trade_strategies[strategy](df=df, )
strategy.analyze()
if not strategy.is_buy_signal() and symbol in result:
result.remove(symbol)
for i, symbol in enumerate(result):
print(i + 1)
print(symbol)
print(tse.Ticker(symbol).url)
print("------------------------------------")
def downloadCsvs():
print("to download Csv ...")
tickers = tse.download(symbols='all', write_to_csv=True, include_jdate=True)
records_dict = download_client_types_records(symbols='all', write_to_csv=True, include_jdate=True)
clear()
for symbol in all_symbols():
symbol1 = renameSymbol(symbol)
df = pd.read_csv('client_types_data/' + symbol1 + '.csv', index_col=False, low_memory=False,
error_bad_lines=False)
df = df.sort_values(by='date', ascending=True)
symbol1 = renameSymbol(symbol)
df.to_csv('client_types_data/' + symbol1 + '.csv', index=False)
print(symbol)
print("finish download csv")
timeVolume()
def possibleQueueSell():
possibleSell = []
for symbol in all_symbols():
symbol1 = renameSymbol(symbol)
fileNameTicker = 'tickers_data/' + symbol1 + '.csv'
fileNameVolume = 'client_types_data/' + symbol1 + '.csv'
if os.path.isfile(fileNameTicker) and os.path.isfile(fileNameVolume):
ticker = pd.read_csv(fileNameTicker, index_col=False, low_memory=False, error_bad_lines=False)
df = pd.read_csv(fileNameVolume, index_col=False, low_memory=False, error_bad_lines=False)
if ticker['close'].iloc[-1] is not None and today == df['date'].iloc[-1]:
if ticker['adjClose'].iloc[-1] > ticker['close'].iloc[-1]:
percent = (ticker['adjClose'].iloc[-1] - ticker['close'].iloc[-1]) * 100 / ticker['close'].iloc[-1]
if percent > 3:
cell = {"symbol": symbol,
"close": ticker['close'].iloc[-1],
"closeP": ticker['adjClose'].iloc[-1],
"percent": float("{:.2f}".format(round(percent, 2)))}
possibleSell.append(cell)
return possibleSell
"""
access properties of Ticker for all symbols
"""
import datetime
import random
from collections import defaultdict
from pytse_client import Ticker, all_symbols
if __name__ == '__main__':
symbols_errors = defaultdict(list)
random_symbols = random.sample(all_symbols(), 2)
for index, symbol in enumerate(random_symbols):
print(f"{symbol} item {index}/{len(random_symbols)}")
try:
ticker = Ticker(symbol)
ticker.title
ticker.url
ticker.group_name
ticker.eps
ticker.p_e_ratio
ticker.group_p_e_ratio
ticker.base_volume
ticker.last_price
ticker.adj_close
ticker.shareholders
ticker.total_shares
ticker.get_shareholders_history(from_when=datetime.timedelta(
days=20))
ticker.get_ticker_real_time_info_response()
except Exception as e:
"""
This is a test file to ensure all symbols in symbols_name.json file are
downloadable.
This process takes a long time so it's not part of the unit test
"""
from pytse_client import download, download_client_types_records, all_symbols
if __name__ == "__main__":
download(all_symbols())
download_client_types_records(all_symbols())
def max_Volume_sell():
sell10 = []
sell20 = []
sell30 = []
sell45 = []
sell60 = []
sell10Ind = []
sell20Ind = []
sell30Ind = []
for symbol in all_symbols():
symbol1 = renameSymbol(symbol)
fileNameTicker = 'tickers_data/' + symbol1 + '.csv'
fileNameVolume = 'client_types_data/' + symbol1 + '.csv'
if os.path.isfile(fileNameTicker) and os.path.isfile(fileNameVolume):
try:
ticker = pd.read_csv(fileNameTicker, index_col=False)
df = pd.read_csv(fileNameVolume, index_col=False)
df = df.fillna(0).astype({"individual_buy_vol": int})
df = df.fillna(0).astype({"individual_buy_count": int})
df = df.fillna(0).astype({"corporate_buy_vol": int})
df = df.fillna(0).astype({"corporate_buy_count": int})
df = df.fillna(0).astype({"corporate_sell_vol": int})
df = df.fillna(0).astype({"individual_sell_vol": int})
if not ticker.empty and ticker.size > 2:
if ticker.iloc[-1].close is not None and df['individual_buy_vol'].size > 1 and today == \
df['date'].iloc[
-1]:
maxNowSell = int(df['individual_sell_vol'].iloc[-1]) + int(df['corporate_sell_vol'].iloc[-1])
max10Sell = int(max(df['individual_sell_vol'][-10:-1] + df['corporate_sell_vol'][-10:-1]))
max20Sell = int(max(df['individual_sell_vol'][-20:-1] + df['corporate_sell_vol'][-20:-1]))
max30Sell = int(max(df['individual_sell_vol'][-30:-1] + df['corporate_sell_vol'][-30:-1]))
max45Sell = int(max(df['individual_sell_vol'][-45:-1] + df['corporate_sell_vol'][-45:-1]))
max60Sell = int(max(df['individual_sell_vol'][-60:-1] + df['corporate_sell_vol'][-60:-1]))
maxNowIndividualSell = int(df['individual_sell_vol'].iloc[-1])
max10IndividualSell = int(max(df['individual_sell_vol'][-10:-1]))
max20IndividualSell = int(max(df['individual_sell_vol'][-20:-1]))
max30IndividualSell = int(max(df['individual_sell_vol'][-30:-1]))
if maxNowSell > max10Sell:
percent = (maxNowSell - max10Sell) / maxNowSell
y10Sell = {"symbol": symbol, "vol": maxNowSell,
"percent": float("{:.2f}".format(round(percent, 2)))}
sell10.append(y10Sell)
if maxNowSell > max20Sell:
percent = (maxNowSell - max20Sell) / maxNowSell
y20Sell = {"symbol": symbol, "vol": maxNowSell,
"percent": float("{:.2f}".format(round(percent, 2)))}
sell20.append(y20Sell)
if maxNowSell > max30Sell:
percent = (maxNowSell - max30Sell) / maxNowSell
y30Sell = {"symbol": symbol, "vol": maxNowSell,
"percent": float("{:.2f}".format(round(percent, 2)))}
sell30.append(y30Sell)
if maxNowSell > max45Sell:
percent = (maxNowSell - max45Sell) / maxNowSell
y45Sell = {"symbol": symbol, "vol": maxNowSell,
"percent": float("{:.2f}".format(round(percent, 2)))}
sell45.append(y45Sell)
if maxNowSell > max60Sell:
percent = (maxNowSell - max60Sell) / maxNowSell
y60Sell = {"symbol": symbol, "vol": maxNowSell,
"percent": float("{:.2f}".format(round(percent, 2)))}
sell60.append(y60Sell)
if maxNowIndividualSell > max10IndividualSell:
percent = (maxNowIndividualSell - max10IndividualSell) / maxNowSell
y10IndividualSell = {"symbol": symbol, "vol": maxNowIndividualSell,
"percent": float("{:.2f}".format(round(percent, 2)))}
sell10Ind.append(y10IndividualSell)
if maxNowIndividualSell > max20IndividualSell:
percent = (maxNowIndividualSell - max20IndividualSell) / maxNowSell
y20IndividualSell = {"symbol": symbol, "vol": maxNowIndividualSell,
"percent": float("{:.2f}".format(round(percent, 2)))}
sell20Ind.append(y20IndividualSell)
if maxNowIndividualSell > max30IndividualSell:
percent = (maxNowIndividualSell - max30IndividualSell) / maxNowSell
y30IndividualSell = {"symbol": symbol, "vol": maxNowIndividualSell,
"percent": float("{:.2f}".format(round(percent, 2)))}
sell30Ind.append(y30IndividualSell)
except:
logging.exception(symbol + symbol1)
return sell10, sell20, sell30, sell45, sell60, sell10Ind, sell20Ind, sell30Ind
def max_Volume_buy():
buy10 = []
buy20 = []
buy30 = []
buy45 = []
buy60 = []
buy10Ind = []
buy20Ind = []
buy30Ind = []
for symbol in all_symbols():
symbol1 = renameSymbol(symbol)
fileNameTicker = 'tickers_data/' + symbol1 + '.csv'
fileNameVolume = 'client_types_data/' + symbol1 + '.csv'
if os.path.isfile(fileNameVolume) and os.path.isfile(fileNameTicker):
ticker = pd.read_csv(fileNameTicker, index_col=False, low_memory=False, error_bad_lines=False)
df = pd.read_csv(fileNameVolume, index_col=False, low_memory=False, error_bad_lines=False)
if symbol1 == "فولاد":
print(symbol)
try:
df = df.fillna(0).astype({"individual_buy_vol": int})
except:
logging.exception(symbol1)
df = df.fillna(0).astype({"individual_buy_vol": int})
df = df.fillna(0).astype({"individual_buy_count": int})
df = df.fillna(0).astype({"corporate_buy_vol": int})
df = df.fillna(0).astype({"corporate_buy_count": int})
df = df.fillna(0).astype({"corporate_sell_vol": int})
df = df.fillna(0).astype({"individual_sell_vol": int})
if not ticker.empty and ticker.size > 2:
if ticker.iloc[-1].close is not None and df['individual_buy_vol'].size > 1 and today == df['date'].iloc[
-1]:
maxNow = int(df['individual_buy_vol'].iloc[-1]) + int(df['corporate_buy_vol'].iloc[-1])
max10 = int(max(df['individual_buy_vol'][-10:-1] + df['corporate_buy_vol'][-10:-1]))
max20 = int(max(df['individual_buy_vol'][-20:-1] + df['corporate_buy_vol'][-20:-1]))
max30 = int(max(df['individual_buy_vol'][-30:-1] + df['corporate_buy_vol'][-30:-1]))
max45 = int(max(df['individual_buy_vol'][-45:-1] + df['corporate_buy_vol'][-45:-1]))
max60 = int(max(df['individual_buy_vol'][-60:-1] + df['corporate_buy_vol'][-60:-1]))
maxNowIndividual = int(df['individual_buy_vol'].iloc[-1])
max10Individual = int(max(df['individual_buy_vol'][-10:-1]))
max20Individual = int(max(df['individual_buy_vol'][-20:-1]))
max30Individual = int(max(df['individual_buy_vol'][-30:-1]))
if maxNow > max10:
percent = (maxNow - max10) / maxNow
y10 = {"symbol": symbol, "vol": maxNow, "percent": float("{:.2f}".format(round(percent, 2)))}
buy10.append(y10)
if maxNow > max20:
percent = (maxNow - max20) / maxNow
y20 = {"symbol": symbol, "vol": maxNow, "percent": float("{:.2f}".format(round(percent, 2)))}
buy20.append(y20)
if maxNow > max30:
percent = (maxNow - max30) / maxNow
y30 = {"symbol": symbol, "vol": maxNow, "percent": float("{:.2f}".format(round(percent, 2)))}
buy30.append(y30)
if maxNow > max45:
percent = (maxNow - max45) / maxNow
y45 = {"symbol": symbol, "vol": maxNow, "percent": float("{:.2f}".format(round(percent, 2)))}
buy45.append(y45)
if maxNow > max60:
percent = (maxNow - max60) / maxNow
y60 = {"symbol": symbol, "vol": maxNow, "percent": float("{:.2f}".format(round(percent, 2)))}
buy60.append(y60)
if maxNowIndividual > max10Individual:
percent = (maxNowIndividual - max10Individual) / maxNow
y10Individual = {"symbol": symbol, "vol": maxNowIndividual,
"percent": float("{:.2f}".format(round(percent, 2)))}
buy10Ind.append(y10Individual)
if maxNowIndividual > max20Individual:
percent = (maxNowIndividual - max20Individual) / maxNow
y20Individual = {"symbol": symbol, "vol": maxNowIndividual,
"percent": float("{:.2f}".format(round(percent, 2)))}
buy20Ind.append(y20Individual)
if maxNowIndividual > max30Individual:
percent = (maxNowIndividual - max30Individual) / maxNow
y30Individual = {"symbol": symbol, "vol": maxNowIndividual,
"percent": float("{:.2f}".format(round(percent, 2)))}
buy30Ind.append(y30Individual)
return buy10, buy20, buy30, buy45, buy60, buy10Ind, buy20Ind, buy30Ind
