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Improve overall code menu ordering and comments

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This commit is contained in:
didier
2020-12-28 21:54:15 +00:00
parent d646813b46
commit e687a4a42e
3 changed files with 300 additions and 211 deletions
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106
stock_market_cli.py
View File

@@ -10,14 +10,8 @@
using an API request.
Finviz for data
Check AlphaVantage for TA as well.
Add s_ylabel and s_legend to plot_ta or plot_stock_ta
Daily Stock vs Intraday 5min Stock
Only show menu that allow daily TA or intraday TA,
i.e. if daily Stock don't show VWAP bc doesn't make sense!
Fix the 'help' params
Parse overview stock with pip request
Multiple TA with kind stock
"""
@@ -59,37 +53,42 @@ def print_help(s_ticker, s_start, s_interval, b_is_market_open):
print("\nStock: ?")
if s_ticker:
print("\nFundamental Analysis:")
print("- details - ")
print("\nFundamental Analysis:") # https://github.com/JerBouma/FundamentalAnalysis
print("details:")
print(" profile profile of the company [FMP]")
print(" rating rating of the company from strong sell to strong buy [FMP]")
print(" quote quote of the company [FMP]")
print(" enterprise enterprise value of the company over time [FMP]")
print(" dcf discounted cash flow of the company over time [FMP]")
print("- financial statement -")
print("financial statement:")
print(" income income statements of the company [default: AV, FMP]")
print(" balance balance sheet of the company [default: AV, FMP]")
print(" cash cash flow of the company [default: AV, FMP]")
print("- ratios -")
print(" metrics key metrics of the company")
print(" ratios financial ratios of the company")
print(" growth financial statement growth of the company")
print("ratios:")
print(" metrics key metrics of the company [FMP]")
print(" ratios financial ratios of the company [FMP]")
print(" growth financial statement growth of the company [FMP]")
print("\nTechnical Analysis:")
print(" sma simple moving average")
print("\nTechnical Analysis:") # https://github.com/twopirllc/pandas-ta
print("overlap:")
print(" ema exponential moving average")
print(" macd moving average convergence/divergence")
print(" stoch stochastic oscillator")
print(" rsi relative strength index")
print(" adx average directional movement index")
print(" cci commodity channel index")
print(" aroon aroon indicator")
print(" bbands bollinger bands")
print(" ad chaikin accumulation/distribution line values")
print(" obv on balance volume")
print(" sma simple moving average")
if s_interval != "1440min":
print(" vwap volume weighted average price")
print("momentum:")
print(" cci commodity channel index")
print(" macd moving average convergence/divergence")
print(" rsi relative strength index")
print(" stoch stochastic oscillator")
print("trend:")
print(" adx average directional movement index")
print(" aroon aroon indicator")
print("volatility:")
print(" bbands bollinger bands")
print("volume:")
print(" ad chaikin accumulation/distribution line values")
print(" obv on balance volume")
'''
print("\nPrediction:")
print(" ma")
@@ -244,62 +243,55 @@ def main():
# -------------------------------------------- TECHNICAL ANALYSIS ----------------------------------------------
# --------------------------------------------------------------------------------------------------------------
# ---------------------------------------------------- SMA ----------------------------------------------------
elif ns_known_args.cmd == 'sma':
smta.sma(l_args, s_ticker, s_interval, df_stock)
continue
# ---------------------------------------------------- EMA ----------------------------------------------------
# -------------------------------------------------- OVERLAP --------------------------------------------------
elif ns_known_args.cmd == 'ema':
smta.ema(l_args, s_ticker, s_interval, df_stock)
continue
# ---------------------------------------------------- MACD ----------------------------------------------------
elif ns_known_args.cmd == 'macd':
smta.macd(l_args, s_ticker, s_interval, df_stock)
elif ns_known_args.cmd == 'sma':
smta.sma(l_args, s_ticker, s_interval, df_stock)
continue
# ---------------------------------------------------- VWAP ----------------------------------------------------
elif ns_known_args.cmd == 'vwap':
smta.vwap(l_args, s_ticker, s_interval, df_stock)
continue
# ---------------------------------------------------- STOCH ----------------------------------------------------
elif ns_known_args.cmd == 'stoch':
smta.stoch(l_args, s_ticker, s_interval, df_stock)
continue
# ---------------------------------------------------- RSI ----------------------------------------------------
elif ns_known_args.cmd == 'rsi':
smta.rsi(l_args, s_ticker, s_interval, df_stock)
continue
# ---------------------------------------------------- ADX ----------------------------------------------------
elif ns_known_args.cmd == 'adx':
smta.adx(l_args, s_ticker, s_interval, df_stock)
continue
# ---------------------------------------------------- CCI ----------------------------------------------------
# --------------------------------------------------- MOMENTUM ---------------------------------------------------
elif ns_known_args.cmd == 'cci':
smta.cci(l_args, s_ticker, s_interval, df_stock)
continue
# --------------------------------------------------- AROON ---------------------------------------------------
elif ns_known_args.cmd == 'macd':
smta.macd(l_args, s_ticker, s_interval, df_stock)
continue
elif ns_known_args.cmd == 'rsi':
smta.rsi(l_args, s_ticker, s_interval, df_stock)
continue
elif ns_known_args.cmd == 'stoch':
smta.stoch(l_args, s_ticker, s_interval, df_stock)
continue
# ---------------------------------------------------- TREND ----------------------------------------------------
elif ns_known_args.cmd == 'adx':
smta.adx(l_args, s_ticker, s_interval, df_stock)
continue
elif ns_known_args.cmd == 'aroon':
smta.aroon(l_args, s_ticker, s_interval, df_stock)
continue
# --------------------------------------------------- BBANDS ---------------------------------------------------
# -------------------------------------------------- VOLATILITY --------------------------------------------------
elif ns_known_args.cmd == 'bbands':
smta.bbands(l_args, s_ticker, s_interval, df_stock)
continue
# ----------------------------------------------------- AD -----------------------------------------------------
# ---------------------------------------------------- VOLUME ----------------------------------------------------
elif ns_known_args.cmd == 'ad':
smta.ad(l_args, s_ticker, s_interval, df_stock)
continue
# ---------------------------------------------------- OBV -----------------------------------------------------
elif ns_known_args.cmd == 'obv':
smta.obv(l_args, s_ticker, s_interval, df_stock)
continue

140
stock_market_fundamental_analysis.py
View File

@@ -175,13 +175,21 @@ def discounted_cash_flow(l_args, s_ticker):
print(df_fa)
print("")
except:
print("")
print("ERROR!\n")
# ---------------------------------------------------- INCOME_STATEMENT ----------------------------------------------------
def income_statement(l_args, s_ticker):
parser = argparse.ArgumentParser(prog='income',
description=""" """)
description="""Collects a complete income statement over time. This can be either quarterly
or annually. The following fields are expected: Accepted date, Cost and expenses, Cost of
revenue, Depreciation and amortization, Ebitda, Ebitdaratio, Eps, Epsdiluted, Filling date,
Final link, General and administrative expenses, Gross profit, Gross profit ratio, Income
before tax, Income before tax ratio, Income tax expense, Interest expense, Link, Net income,
Net income ratio, Operating expenses, Operating income, Operating income ratio, Other expenses,
Period, Research and development expenses, Revenue, Selling and marketing expenses, Total other
income expenses net, Weighted average shs out, Weighted average shs out dil [Default source:
Alpha Vantage API; Other possible source: Financial Modeling Prep API]""")
parser.add_argument('-n', "--num", action="store", dest="n_num", type=check_positive, default=1, help='Number of latest info')
parser.add_argument('-q', action="store_true", default=False, dest="b_quarter", help='Quarter fundamental data')
@@ -216,6 +224,14 @@ def income_statement(l_args, s_ticker):
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
print(df_fa.drop(index=['Final link', 'Link']).to_string())
pd.set_option('display.max_colwidth', -1)
print("")
print(df_fa.loc['Final link'].to_frame().to_string())
print("")
print(df_fa.loc['Link'].to_frame().to_string())
# Use Alpha Vantage API
else:
fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE, output_format='pandas')
@@ -232,17 +248,29 @@ def income_statement(l_args, s_ticker):
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
print(df_fa)
print(df_fa)
print("")
except:
print("")
print("ERROR!\n")
return
# ---------------------------------------------------- BALANCE_SHEET ----------------------------------------------------
def balance_sheet(l_args, s_ticker):
parser = argparse.ArgumentParser(prog='balance',
description=""" """)
description="""Collects a complete balance sheet statement over time. This can be
either quarterly or annually. The following fields are expected: Accepted date,
Account payables, Accumulated other comprehensive income loss, Cash and cash
equivalents, Cash and short term investments, Common stock, Deferred revenue,
Deferred revenue non current, Deferred tax liabilities non current, Filling date,
Final link, Goodwill, Goodwill and intangible assets, Intangible assets, Inventory,
Link, Long term debt, Long term investments, Net debt, Net receivables, Other assets,
Other current assets, Other current liabilities, Other liabilities, Other non current
assets, Other non current liabilities, Othertotal stockholders equity, Period, Property
plant equipment net, Retained earnings, Short term debt, Short term investments, Tax assets,
Tax payables, Total assets, Total current assets, Total current liabilities, Total debt,
Total investments, Total liabilities, Total liabilities and stockholders equity, Total
non current assets, Total non current liabilities, and Total stockholders equity.
[Default source: Alpha Vantage API; Other possible source: Financial Modeling Prep API]""")
parser.add_argument('-n', "--num", action="store", dest="n_num", type=check_positive, default=1, help='Number of informations')
parser.add_argument('-q', action="store_true", default=False, dest="b_quarter", help='Quarter fundamental data')
@@ -272,6 +300,13 @@ def balance_sheet(l_args, s_ticker):
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
print(df_fa.drop(index=['Final link', 'Link']).to_string())
pd.set_option('display.max_colwidth', -1)
print("")
print(df_fa.loc['Final link'].to_frame().to_string())
print("")
print(df_fa.loc['Link'].to_frame().to_string())
# Use Alpha Vantage API
else:
@@ -289,18 +324,29 @@ def balance_sheet(l_args, s_ticker):
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
print(df_fa)
print(df_fa)
print("")
except:
print("")
print("ERROR!\n")
return
# ---------------------------------------------------- CASH_FLOW ----------------------------------------------------
def cash_flow(l_args, s_ticker):
parser = argparse.ArgumentParser(prog='cash',
description=""" """)
description="""Collects a complete cash flow statement over time. This can be either
quarterly or annually. The following fields are expected: Accepted date, Accounts payables,
Accounts receivables, Acquisitions net, Capital expenditure, Cash at beginning of period,
Cash at end of period, Change in working capital, Common stock issued, Common stock repurchased,
Debt repayment, Deferred income tax, Depreciation and amortization, Dividends paid,
Effect of forex changes on cash, Filling date, Final link, Free cash flow, Inventory,
Investments in property plant and equipment, Link, Net cash provided by operating activities,
Net cash used for investing activites, Net cash used provided by financing activities, Net
change in cash, Net income, Operating cash flow, Other financing activites, Other investing
activites, Other non cash items, Other working capital, Period, Purchases of investments,
Sales maturities of investments, Stock based compensation. [Default source: Alpha Vantage API;
Other possible source: Financial Modeling Prep API]""")
parser.add_argument('-n', "--num", action="store", dest="n_num", type=check_positive, default=1, help='Number of informations')
parser.add_argument('-q', action="store_true", default=False, dest="b_quarter", help='Quarter fundamental data')
@@ -335,6 +381,13 @@ def cash_flow(l_args, s_ticker):
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
print(df_fa.drop(index=['Final link', 'Link']).to_string())
pd.set_option('display.max_colwidth', -1)
print("")
print(df_fa.loc['Final link'].to_frame().to_string())
print("")
print(df_fa.loc['Link'].to_frame().to_string())
# Use Alpha Vantage API
else:
fd = FundamentalData(key=cfg.API_KEY_ALPHAVANTAGE, output_format='pandas')
@@ -351,18 +404,33 @@ def cash_flow(l_args, s_ticker):
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
print(df_fa)
print(df_fa)
print("")
except:
print("")
print("ERROR!\n")
return
# ---------------------------------------------------- KEY_METRICS ----------------------------------------------------
def key_metrics(l_args, s_ticker):
parser = argparse.ArgumentParser(prog='metrics',
description=""" """)
description="""Lists the key metrics (in total 57 metrics) of a company over time
(annual and quarterly). This includes, among other things, Return on Equity (ROE),
Working Capital, Current Ratio and Debt to Assets. The following fields are expected:
Average inventory, Average payables, Average receivables, Book value per share, Capex
per share, Capex to depreciation, Capex to operating cash flow, Capex to revenue, Cash
per share, Current ratio, Days of inventory on hand, Days payables outstanding, Days sales
outstanding, Debt to assets, Debt to equity, Dividend yield, Earnings yield, Enterprise value,
Enterprise value over EBITDA, Ev to free cash flow, Ev to operating cash flow, Ev to sales,
Free cash flow per share, Free cash flow yield, Graham net net, Graham number, Income quality,
Intangibles to total assets, Interest debt per share, Inventory turnover, Market cap, Net
current asset value, Net debt to EBITDA, Net income per share, Operating cash flow per share,
Payables turnover, Payout ratio, Pb ratio, Pe ratio, Pfcf ratio, Pocfratio, Price to sales ratio,
Ptb ratio, Receivables turnover, Research and ddevelopement to revenue, Return on tangible assets,
Revenue per share, Roe, Roic, Sales general and administrative to revenue, Shareholders equity per
share, Stock based compensation to revenue, Tangible book value per share, and Working capital.
[Source: Financial Modeling Prep API]""")
parser.add_argument('-n', "--num", action="store", dest="n_num", type=check_positive, default=1, help='Number of informations')
parser.add_argument('-q', action="store_true", default=False, dest="b_quarter", help='Quarter fundamental data')
@@ -380,7 +448,7 @@ def key_metrics(l_args, s_ticker):
if ns_parser.n_num == 1:
pd.set_option('display.max_colwidth', -1)
else:
pd.options.display.max_colwidth = 40
pd.options.display.max_colwidth = 50
if ns_parser.b_quarter:
df_fa = fa.key_metrics(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
@@ -394,18 +462,35 @@ def key_metrics(l_args, s_ticker):
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
df_fa = df_fa.rename(index={"Enterprise value over e b i t d a": "Enterprise value over EBITDA"})
df_fa = df_fa.rename(index={"Net debt to e b i t d a": "Net debt to EBITDA"})
print(df_fa)
print("")
except:
print("")
print("ERROR!\n")
return
# ---------------------------------------------------- FINANCIAL_RATIOS ----------------------------------------------------
def financial_ratios(l_args, s_ticker):
parser = argparse.ArgumentParser(prog='ratios',
description=""" """)
description="""Includes in-depth ratios (in total 57 ratios) of a company over time
(annual and quarterly). This contains, among other things, Price-to-Book Ratio, Payout
Ratio and Operating Cycle. The following fields are expected: Asset turnover, Capital
expenditure coverage ratio, Cash conversion cycle, Cash flow coverage ratios, Cash flow
to debt ratio, Cash per share, Cash ratio, Company equity multiplier, Current ratio,
Days of inventory outstanding, Days of payables outstanding, Days of sales outstanding,
Debt equity ratio, Debt ratio, Dividend paid and capex coverage ratio, Dividend payout ratio,
Dividend yield, Ebit per revenue, Ebt per ebit, Effective tax rate, Enterprise value multiple,
Fixed asset turnover, Free cash flow operating cash flow ratio, Free cash flow per share, Gross
profit margin, Inventory turnover, Long term debt to capitalization, Net income per EBT, Net
profit margin, Operating cash flow per share, Operating cash flow sales ratio, Operating cycle,
Operating profit margin, Payables turnover, Payout ratio, Pretax profit margin, Price book value
ratio, Price cash flow ratio, Price earnings ratio, Price earnings to growth ratio, Price fair value,
Price sales ratio, Price to book ratio, Price to free cash flows ratio, Price to operating cash flows
ratio, Price to sales ratio, Quick ratio, Receivables turnover, Return on assets, Return on capital
employed, Return on equity, Short term coverage ratios, and Total debt to capitalization.
[Source: Financial Modeling Prep API]""")
parser.add_argument('-n', "--num", action="store", dest="n_num", type=check_positive, default=1, help='Number of informations')
parser.add_argument('-q', action="store_true", default=False, dest="b_quarter", help='Quarter fundamental data')
@@ -437,18 +522,32 @@ def financial_ratios(l_args, s_ticker):
df_fa.index = [''.join(' ' + char if char.isupper() else char.strip() for char in idx).strip() for idx in df_fa.index.tolist()]
df_fa.index = [s_val.capitalize() for s_val in df_fa.index]
df_fa.columns.name = "Fiscal Date Ending"
df_fa = df_fa.rename(index={"Net income per e b t": "Net income per EBT"})
print(df_fa)
print("")
except:
print("")
print("ERROR!\n")
return
# ---------------------------------------------------- FINANCIAL_STATEMENT_GROWTH ----------------------------------------------------
def financial_statement_growth(l_args, s_ticker):
parser = argparse.ArgumentParser(prog='growth',
description=""" """)
description="""Measures the growth of several financial statement items and ratios over time
(annual and quarterly). These are, among other things, Revenue Growth (3, 5 and 10 years),
inventory growth and operating cash flow growth (3, 5 and 10 years). The following fields
are expected: Asset growth, Book valueper share growth, Debt growth, Dividendsper share growth,
Ebitgrowth, Epsdiluted growth, Epsgrowth, Five y dividendper share growth per share, Five y net
income growth per share, Five y operating c f growth per share, Five y revenue growth per share,
Five y shareholders equity growth per share, Free cash flow growth, Gross profit growth, Inventory
growth, Net income growth, Operating cash flow growth, Operating income growth, Rdexpense growth,
Receivables growth, Revenue growth, Sgaexpenses growth, Ten y dividendper share growth per share,
Ten y net income growth per share, Ten y operating c f growth per share, Ten y revenue growth per
share, Ten y shareholders equity growth per share, Three y dividendper share growth per share,
Three y net income growth per share, Three y operating c f growth per share, Three y revenue growth
per share, Three y shareholders equity growth per share, Weighted average shares diluted growth,
and Weighted average shares growth [Source: Financial Modeling Prep API]""")
parser.add_argument('-n', "--num", action="store", dest="n_num", type=check_positive, default=1, help='Number of informations')
parser.add_argument('-q', action="store_true", default=False, dest="b_quarter", help='Quarter fundamental data')
@@ -466,7 +565,7 @@ def financial_statement_growth(l_args, s_ticker):
if ns_parser.n_num == 1:
pd.set_option('display.max_colwidth', -1)
else:
pd.options.display.max_colwidth = 40
pd.options.display.max_colwidth = 50
if ns_parser.b_quarter:
df_fa = fa.financial_statement_growth(s_ticker, cfg.API_KEY_FINANCIALMODELINGPREP, period='quarter')
@@ -484,6 +583,5 @@ def financial_statement_growth(l_args, s_ticker):
print(df_fa)
print("")
except:
print("")
print("ERROR!\n")
return

265
stock_market_technical_analysis.py
View File

@@ -5,44 +5,6 @@ import pandas_ta as ta
import config_bot as cfg
from stock_market_helper_funcs import *
# ----------------------------------------------------- SMA -----------------------------------------------------
def sma(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(prog='sma',
description=""" Moving Averages are used to smooth the data in an array to
help eliminate noise and identify trends. The Simple Moving Average is literally
the simplest form of a moving average. Each output value is the average of the
previous n values. In a Simple Moving Average, each value in the time period carries
equal weight, and values outside of the time period are not included in the average.
This makes it less responsive to recent changes in the data, which can be useful for
filtering out those changes. """)
parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=10, help='length')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
except SystemExit:
print("")
return
if l_unknown_args:
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
# Daily
if s_interval == "1440min":
df_ta = ta.sma(df_stock['5. adjusted close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
plot_stock_ta(df_stock['5. adjusted close'], s_ticker, df_ta, "SMA")
# Intraday
else:
df_ta = ta.sma(df_stock['4. close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, "SMA")
except:
print("")
return
# ----------------------------------------------------- EMA -----------------------------------------------------
def ema(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(prog='ema',
@@ -77,8 +39,110 @@ def ema(l_args, s_ticker, s_interval, df_stock):
df_ta = ta.ema(df_stock['4. close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, "EMA")
except:
print("ERROR!\n")
return
# ----------------------------------------------------- SMA -----------------------------------------------------
def sma(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(prog='sma',
description=""" Moving Averages are used to smooth the data in an array to
help eliminate noise and identify trends. The Simple Moving Average is literally
the simplest form of a moving average. Each output value is the average of the
previous n values. In a Simple Moving Average, each value in the time period carries
equal weight, and values outside of the time period are not included in the average.
This makes it less responsive to recent changes in the data, which can be useful for
filtering out those changes. """)
parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=10, help='length')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
except SystemExit:
print("")
return
if l_unknown_args:
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
# Daily
if s_interval == "1440min":
df_ta = ta.sma(df_stock['5. adjusted close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
plot_stock_ta(df_stock['5. adjusted close'], s_ticker, df_ta, "SMA")
# Intraday
else:
df_ta = ta.sma(df_stock['4. close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, "SMA")
except:
print("ERROR!\n")
return
# ----------------------------------------------------- VWAP -----------------------------------------------------
def vwap(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(prog='vwap',
description=""" The Volume Weighted Average Price that measures the average typical price
by volume. It is typically used with intraday charts to identify general direction. """)
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
except SystemExit:
print("")
return
if l_unknown_args:
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
df_ta = ta.vwap(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['4. close'],
volume=df_stock['5. volume'], offset=ns_parser.n_offset)
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, "VWAP")
except:
print("ERROR!\n")
return
# ----------------------------------------------------- CCI -----------------------------------------------------
def cci(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(prog='cci',
description=""" The CCI is designed to detect beginning and ending market trends.
The range of 100 to -100 is the normal trading range. CCI values outside of this
range indicate overbought or oversold conditions. You can also look for price
divergence in the CCI. If the price is making new highs, and the CCI is not,
then a price correction is likely. """)
parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=14, help='length')
parser.add_argument('-s', "--scalar", action="store", dest="n_scalar", type=check_positive, default=0.015, help='scalar')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
except SystemExit:
print("")
return
if l_unknown_args:
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
# Daily
if s_interval == "1440min":
df_ta = ta.cci(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['5. adjusted close'],
length=ns_parser.n_length, scalar=ns_parser.n_scalar, offset=ns_parser.n_offset).dropna()
plot_stock_and_ta(df_stock['5. adjusted close'], s_ticker, df_ta, "CCI")
# Intraday
else:
df_ta = ta.cci(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['4. close'],
length=ns_parser.n_length, scalar=ns_parser.n_scalar, offset=ns_parser.n_offset).dropna()
plot_stock_and_ta(df_stock['4. close'], s_ticker, df_ta, "CCI")
except:
print("ERROR!\n")
return
# ----------------------------------------------------- MACD -----------------------------------------------------
@@ -124,16 +188,22 @@ def macd(l_args, s_ticker, s_interval, df_stock):
signal=ns_parser.n_signal, offset=ns_parser.n_offset).dropna()
plot_stock_and_ta(df_stock['4. close'], s_ticker, df_ta, "MACD")
except:
print("")
print("ERROR!\n")
return
# ----------------------------------------------------- VWAP -----------------------------------------------------
def vwap(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(prog='vwap',
description=""" The Volume Weighted Average Price that measures the average typical price
by volume. It is typically used with intraday charts to identify general direction. """)
# ----------------------------------------------------- RSI -----------------------------------------------------
def rsi(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(prog='rsi',
description=""" The Relative Strength Index (RSI) calculates a ratio of the
recent upward price movements to the absolute price movement. The RSI ranges
from 0 to 100. The RSI is interpreted as an overbought/oversold indicator when
the value is over 70/below 30. You can also look for divergence with price. If
the price is making new highs/lows, and the RSI is not, it indicates a reversal. """)
parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=14, help='length')
parser.add_argument('-s', "--scalar", action="store", dest="n_scalar", type=check_positive, default=100, help='scalar')
parser.add_argument('-d', "--drift", action="store", dest="n_drift", type=check_positive, default=1, help='drift')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
@@ -146,12 +216,18 @@ def vwap(l_args, s_ticker, s_interval, df_stock):
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
df_ta = ta.vwap(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['4. close'],
volume=df_stock['5. volume'], offset=ns_parser.n_offset)
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, "VWAP")
# Daily
if s_interval == "1440min":
df_ta = ta.rsi(df_stock['5. adjusted close'], length=ns_parser.n_length, scalar=ns_parser.n_scalar,
drift=ns_parser.n_drift, offset=ns_parser.n_offset).dropna()
plot_stock_and_ta(df_stock['5. adjusted close'], s_ticker, df_ta, "RSI")
# Intraday
else:
df_ta = ta.rsi(df_stock['4. close'], length=ns_parser.n_length, scalar=ns_parser.n_scalar,
drift=ns_parser.n_drift, offset=ns_parser.n_offset).dropna()
plot_stock_and_ta(df_stock['4. close'], s_ticker, df_ta, "RSI")
except:
print("")
print("ERROR!\n")
return
@@ -194,46 +270,7 @@ def stoch(l_args, s_ticker, s_interval, df_stock):
d=ns_parser.n_slowdperiod, smooth_k=ns_parser.n_slowkperiod, offset=ns_parser.n_offset).dropna()
plot_stock_and_ta(df_stock['4. close'], s_ticker, df_ta, "STOCH")
except:
print("")
return
# ----------------------------------------------------- RSI -----------------------------------------------------
def rsi(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(prog='rsi',
description=""" The Relative Strength Index (RSI) calculates a ratio of the
recent upward price movements to the absolute price movement. The RSI ranges
from 0 to 100. The RSI is interpreted as an overbought/oversold indicator when
the value is over 70/below 30. You can also look for divergence with price. If
the price is making new highs/lows, and the RSI is not, it indicates a reversal. """)
parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=14, help='length')
parser.add_argument('-s', "--scalar", action="store", dest="n_scalar", type=check_positive, default=100, help='scalar')
parser.add_argument('-d', "--drift", action="store", dest="n_drift", type=check_positive, default=1, help='drift')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
except SystemExit:
print("")
return
if l_unknown_args:
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
# Daily
if s_interval == "1440min":
df_ta = ta.rsi(df_stock['5. adjusted close'], length=ns_parser.n_length, scalar=ns_parser.n_scalar,
drift=ns_parser.n_drift, offset=ns_parser.n_offset).dropna()
plot_stock_and_ta(df_stock['5. adjusted close'], s_ticker, df_ta, "RSI")
# Intraday
else:
df_ta = ta.rsi(df_stock['4. close'], length=ns_parser.n_length, scalar=ns_parser.n_scalar,
drift=ns_parser.n_drift, offset=ns_parser.n_offset).dropna()
plot_stock_and_ta(df_stock['4. close'], s_ticker, df_ta, "RSI")
except:
print("")
print("ERROR!\n")
return
@@ -271,45 +308,7 @@ def adx(l_args, s_ticker, s_interval, df_stock):
scalar=ns_parser.n_scalar, drift=ns_parser.n_drift, offset=ns_parser.n_offset).dropna()
plot_stock_and_ta(df_stock['4. close'], s_ticker, df_ta, "ADX")
except:
print("")
return
# ----------------------------------------------------- CCI -----------------------------------------------------
def cci(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(prog='cci',
description=""" The CCI is designed to detect beginning and ending market trends.
The range of 100 to -100 is the normal trading range. CCI values outside of this
range indicate overbought or oversold conditions. You can also look for price
divergence in the CCI. If the price is making new highs, and the CCI is not,
then a price correction is likely. """)
parser.add_argument('-l', "--length", action="store", dest="n_length", type=check_positive, default=14, help='length')
parser.add_argument('-s', "--scalar", action="store", dest="n_scalar", type=check_positive, default=0.015, help='scalar')
parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
except SystemExit:
print("")
return
if l_unknown_args:
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try:
# Daily
if s_interval == "1440min":
df_ta = ta.cci(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['5. adjusted close'],
length=ns_parser.n_length, scalar=ns_parser.n_scalar, offset=ns_parser.n_offset).dropna()
plot_stock_and_ta(df_stock['5. adjusted close'], s_ticker, df_ta, "CCI")
# Intraday
else:
df_ta = ta.cci(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['4. close'],
length=ns_parser.n_length, scalar=ns_parser.n_scalar, offset=ns_parser.n_offset).dropna()
plot_stock_and_ta(df_stock['4. close'], s_ticker, df_ta, "CCI")
except:
print("")
print("ERROR!\n")
return
@@ -350,7 +349,7 @@ def aroon(l_args, s_ticker, s_interval, df_stock):
else:
plot_stock_and_ta(df_stock['4. close'], s_ticker, df_ta.iloc[:,-1], "AROON")
except:
print("")
print("ERROR!\n")
return
@@ -395,7 +394,7 @@ def bbands(l_args, s_ticker, s_interval, df_stock):
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, "BBANDS")
except:
print("")
print("ERROR!\n")
return
@@ -452,7 +451,7 @@ def ad(l_args, s_ticker, s_interval, df_stock):
plot_stock_and_ta(df_stock['4. close'], s_ticker, df_ta, "AD")
except:
print("")
print("ERROR!\n")
return
@@ -488,5 +487,5 @@ def obv(l_args, s_ticker, s_interval, df_stock):
df_ta = ta.ad(close=df_stock['4. close'], volume=df_stock['5. volume'], offset=ns_parser.n_offset).dropna()
plot_stock_and_ta(df_stock['4. close'], s_ticker, df_ta, "OBV")
except:
print("")
return
print("ERROR!\n")
return