mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-27 12:40:35 +00:00
Refactor code to allow daily vs intraday data
This commit is contained in:
didier
@@ -11,6 +11,14 @@
|
||||
|
||||
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
|
||||
|
||||
"""
|
||||
|
||||
from alpha_vantage.timeseries import TimeSeries
|
||||
@@ -27,7 +35,7 @@ import stock_market_technical_analysis as smta
|
||||
import stock_market_fundamental_analysis as smfa
|
||||
|
||||
# -----------------------------------------------------------------------------------------------------------------------
|
||||
def print_help(s_ticker, s_start, b_is_market_open):
|
||||
def print_help(s_ticker, s_start, s_interval, b_is_market_open):
|
||||
""" Print help
|
||||
"""
|
||||
print("What do you want to do?")
|
||||
@@ -40,10 +48,12 @@ def print_help(s_ticker, s_start, b_is_market_open):
|
||||
print(" help help to see this menu again")
|
||||
print(" quit to abandon the program")
|
||||
|
||||
s_intraday = (f'Intraday {s_interval}', 'Daily')[s_interval == "1440min"]
|
||||
|
||||
if s_ticker and s_start:
|
||||
print(f"\nStock: {s_ticker} (from {s_start.strftime('%Y-%m-%d')})")
|
||||
print(f"\n{s_intraday} Stock: {s_ticker} (from {s_start.strftime('%Y-%m-%d')})")
|
||||
elif s_ticker:
|
||||
print(f"\nStock: {s_ticker}")
|
||||
print(f"\n{s_intraday} Stock: {s_ticker}")
|
||||
else:
|
||||
print("\nStock: ?")
|
||||
|
||||
@@ -55,7 +65,8 @@ def print_help(s_ticker, s_start, b_is_market_open):
|
||||
print(" sma simple moving average [daily]")
|
||||
print(" ema exponential moving average [daily]")
|
||||
print(" macd moving average convergence/divergence [daily]")
|
||||
print(" vwap volume weighted average price [intraday]")
|
||||
if s_interval != "1440min":
|
||||
print(" vwap volume weighted average price [intraday]")
|
||||
print(" stoch stochastic oscillator [daily]")
|
||||
print(" rsi relative strength index [daily]")
|
||||
|
||||
@@ -86,6 +97,8 @@ def main():
|
||||
s_ticker = ""
|
||||
s_start = ""
|
||||
df_stock = pd.DataFrame()
|
||||
b_intraday = False
|
||||
s_interval = "1440min"
|
||||
|
||||
# Set stock by default to speed up testing
|
||||
s_ticker = "TSLA"
|
||||
@@ -104,7 +117,7 @@ def main():
|
||||
|
||||
# Print first welcome message and help
|
||||
print("\nWelcome to Didier's Stock Market Bot\n")
|
||||
print_help(s_ticker, s_start, b_is_stock_market_open())
|
||||
print_help(s_ticker, s_start, s_interval, b_is_stock_market_open())
|
||||
print("\n")
|
||||
|
||||
# Loop forever and ever
|
||||
@@ -136,17 +149,17 @@ def main():
|
||||
|
||||
# ---------------------------------------------------- LOAD ----------------------------------------------------
|
||||
elif ns_known_args.cmd == 'load':
|
||||
[s_ticker, s_start, df_stock] = smm.load(l_args, s_ticker, s_start, df_stock)
|
||||
[s_ticker, s_start, s_interval, df_stock] = smm.load(l_args, s_ticker, s_start, s_interval, df_stock)
|
||||
continue
|
||||
|
||||
# ---------------------------------------------------- VIEW ----------------------------------------------------
|
||||
elif ns_known_args.cmd == 'view':
|
||||
smm.view(l_args, s_ticker, s_start, df_stock)
|
||||
smm.view(l_args, s_ticker, s_start, s_interval, df_stock)
|
||||
continue
|
||||
|
||||
# ---------------------------------------------------- HELP ----------------------------------------------------
|
||||
elif ns_known_args.cmd == 'help':
|
||||
print_help(s_ticker, s_start, b_is_stock_market_open())
|
||||
print_help(s_ticker, s_start, s_interval, b_is_stock_market_open())
|
||||
|
||||
# ----------------------------------------------------- QUIT ----------------------------------------------------
|
||||
elif ns_known_args.cmd == 'quit':
|
||||
@@ -170,32 +183,32 @@ def main():
|
||||
|
||||
# ---------------------------------------------------- SMA ----------------------------------------------------
|
||||
elif ns_known_args.cmd == 'sma':
|
||||
smta.sma(l_args, s_ticker, df_stock)
|
||||
smta.sma(l_args, s_ticker, s_interval, df_stock)
|
||||
continue
|
||||
|
||||
# ---------------------------------------------------- EMA ----------------------------------------------------
|
||||
elif ns_known_args.cmd == 'ema':
|
||||
smta.ema(l_args, s_ticker, df_stock)
|
||||
smta.ema(l_args, s_ticker, s_interval, df_stock)
|
||||
continue
|
||||
|
||||
# ---------------------------------------------------- MACD ----------------------------------------------------
|
||||
elif ns_known_args.cmd == 'macd':
|
||||
smta.macd(l_args, s_ticker, df_stock)
|
||||
smta.macd(l_args, s_ticker, s_interval, df_stock)
|
||||
continue
|
||||
|
||||
# ---------------------------------------------------- VWAP ----------------------------------------------------
|
||||
elif ns_known_args.cmd == 'vwap':
|
||||
smta.vwap(l_args, s_ticker, s_start)
|
||||
smta.vwap(l_args, s_ticker, s_interval, df_stock)
|
||||
continue
|
||||
|
||||
# ---------------------------------------------------- STOCH ----------------------------------------------------
|
||||
elif ns_known_args.cmd == 'stoch':
|
||||
smta.stoch(l_args, s_ticker, df_stock)
|
||||
smta.stoch(l_args, s_ticker, s_interval, df_stock)
|
||||
continue
|
||||
|
||||
# ---------------------------------------------------- RSI ----------------------------------------------------
|
||||
elif ns_known_args.cmd == 'rsi':
|
||||
smta.rsi(l_args, s_ticker, df_stock)
|
||||
smta.rsi(l_args, s_ticker, s_interval, df_stock)
|
||||
continue
|
||||
|
||||
# --------------------------------------------------------------------------------------------------------------
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
import argparse
|
||||
from alpha_vantage.timeseries import TimeSeries
|
||||
import re
|
||||
import config_bot as cfg
|
||||
import pandas as pd
|
||||
from stock_market_helper_funcs import *
|
||||
@@ -26,67 +27,62 @@ def gainers(l_args):
|
||||
print("")
|
||||
|
||||
# ----------------------------------------------------- LOAD -----------------------------------------------------
|
||||
def load(l_args, s_ticker, s_start, df_stock):
|
||||
parser = argparse.ArgumentParser(prog='load',
|
||||
description=""" Given a stock market ticker, and an optional starting time,
|
||||
load a stock in order to perform analysis""")
|
||||
parser.add_argument('-t', "--ticker", action="store", dest="s_ticker", required=True, help='Stock ticker')
|
||||
parser.add_argument('-s', "--start", type=valid_date, dest="s_start_date",
|
||||
help="The starting date (format YYYY-MM-DD) of the stock")
|
||||
def load(l_args, s_ticker, s_start, s_interval, df_stock):
|
||||
parser = argparse.ArgumentParser(prog='load', description=""" Load a stock in order to perform analysis""")
|
||||
parser.add_argument('-t', "--ticker", action="store", dest="s_ticker", required=True, help="Stock ticker")
|
||||
parser.add_argument('-s', "--start", type=valid_date, dest="s_start_date", help="The starting date (format YYYY-MM-DD) of the stock")
|
||||
parser.add_argument('-i', "--interval", action="store", dest="n_interval", type=int, default=1440, choices=[1,5,15,30,60], help="Intraday stock minutes")
|
||||
|
||||
try:
|
||||
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
|
||||
except SystemExit:
|
||||
print("")
|
||||
return [s_ticker, s_start, df_stock]
|
||||
return [s_ticker, s_start, s_interval, df_stock]
|
||||
|
||||
if l_unknown_args:
|
||||
print(f"The following args couldn't be interpreted: {l_unknown_args}")
|
||||
|
||||
# Update values:
|
||||
s_ticker = ns_parser.s_ticker
|
||||
s_start = ns_parser.s_start_date
|
||||
s_interval = str(ns_parser.n_interval)+'min'
|
||||
|
||||
try:
|
||||
# Currently only supports daily stocks
|
||||
ts = TimeSeries(key=cfg.API_KEY_ALPHAVANTAGE, output_format='pandas')
|
||||
df_stock, d_stock_metadata = ts.get_daily_adjusted(symbol=ns_parser.s_ticker, outputsize='full')
|
||||
# Daily
|
||||
if s_interval == "1440min":
|
||||
df_stock, d_stock_metadata = ts.get_daily_adjusted(symbol=ns_parser.s_ticker, outputsize='full')
|
||||
# Intraday
|
||||
else:
|
||||
df_stock, d_stock_metadata = ts.get_intraday(symbol=ns_parser.s_ticker, outputsize='full', interval=s_interval)
|
||||
|
||||
except:
|
||||
print("Either the ticker or the API_KEY are invalids. Try again!")
|
||||
return [s_ticker, s_start, df_stock]
|
||||
return [s_ticker, s_start, s_interval, df_stock]
|
||||
|
||||
s_ticker = ns_parser.s_ticker
|
||||
|
||||
if ns_parser.s_start_date:
|
||||
s_start = ns_parser.s_start_date
|
||||
|
||||
# Slice dataframe from the starting date YYYY-MM-DD selected
|
||||
if ns_parser.s_start_date:
|
||||
df_stock = df_stock[ns_parser.s_start_date:]
|
||||
s_intraday = (f'Intraday {s_interval}', 'Daily')[s_interval == "1440min"]
|
||||
|
||||
if s_start:
|
||||
print(f"Loading {s_ticker} stock with starting period {s_start.strftime('%Y-%m-%d')} for analysis.\n")
|
||||
# Slice dataframe from the starting date YYYY-MM-DD selected
|
||||
df_stock = df_stock[ns_parser.s_start_date:]
|
||||
print(f"Loading {s_intraday} {s_ticker} stock with starting period {s_start.strftime('%Y-%m-%d')} for analysis.\n")
|
||||
else:
|
||||
print(f"Loading {s_ticker} stock for analysis.\n")
|
||||
print(f"Loading {s_intraday} {s_ticker} stock for analysis.\n")
|
||||
|
||||
return [s_ticker, s_start, df_stock]
|
||||
return [s_ticker, s_start, s_interval, df_stock]
|
||||
|
||||
|
||||
# ----------------------------------------------------- VIEW -----------------------------------------------------
|
||||
def view(l_args, s_ticker, s_start, df_stock):
|
||||
parser = argparse.ArgumentParser(prog='view',
|
||||
description='Given a stock market ticker, allows to see historical data of \
|
||||
the corresponding stock. Note that the alpha_vantage key is necessary \
|
||||
for this to work!')
|
||||
def view(l_args, s_ticker, s_start, s_interval, df_stock):
|
||||
parser = argparse.ArgumentParser(prog='view', description='Visualise historical data of a stock. An alpha_vantage key is necessary.')
|
||||
if s_ticker:
|
||||
parser.add_argument('-t', "--ticker", action="store", dest="s_ticker", default=s_ticker, help='Stock ticker')
|
||||
else:
|
||||
parser.add_argument('-t', "--ticker", action="store", dest="s_ticker", required=True, help='Stock ticker')
|
||||
|
||||
parser.add_argument('-i', "--intraday", action="store_true", dest="b_intraday", default=False, help='Stock ticker')
|
||||
parser.add_argument("--interval", action="store", dest="n_interval", type=int, default=15, choices=[1,5,15,30,60], help='Stock ticker')
|
||||
|
||||
parser.add_argument('-s', "--start", type=valid_date, dest="s_start_date",
|
||||
help="The starting date (format YYYY-MM-DD) of the stock")
|
||||
parser.add_argument('-s', "--start", type=valid_date, dest="s_start_date", help="The starting date (format YYYY-MM-DD) of the stock")
|
||||
parser.add_argument('-i', "--interval", action="store", dest="n_interval", type=int, default=0, choices=[1,5,15,30,60], help="Intraday stock minutes")
|
||||
parser.add_argument("--type", action="store", dest="n_type", type=check_positive, default=5, # in case it's daily
|
||||
help='12345 corresponds to types: 1. open; 2. high; 3.low; 4. close; \
|
||||
while 14 corresponds to types: 1.open; 4. close')
|
||||
help='1234 corresponds to types: 1. open; 2. high; 3.low; 4. close; while 14 corresponds to types: 1.open; 4. close')
|
||||
|
||||
try:
|
||||
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
|
||||
@@ -97,41 +93,34 @@ def view(l_args, s_ticker, s_start, df_stock):
|
||||
if l_unknown_args:
|
||||
print(f"The following args couldn't be interpreted: {l_unknown_args}")
|
||||
|
||||
# Update values:
|
||||
s_ticker = ns_parser.s_ticker
|
||||
if ns_parser.s_start_date:
|
||||
s_start = ns_parser.s_start_date
|
||||
|
||||
# A new interval intraday period was given
|
||||
if ns_parser.n_interval != 0:
|
||||
s_interval = str(ns_parser.n_interval)+'min'
|
||||
|
||||
try:
|
||||
# We want to view a different stock than the one we loaded
|
||||
if ns_parser.s_ticker != s_ticker:
|
||||
ts = TimeSeries(key=cfg.API_KEY_ALPHAVANTAGE, output_format='pandas')
|
||||
df_stock, d_stock_metadata = ts.get_daily_adjusted(symbol=ns_parser.s_ticker, outputsize='full')
|
||||
# We want to see intraday data about this stock
|
||||
elif ns_parser.b_intraday:
|
||||
ts = TimeSeries(key=cfg.API_KEY_ALPHAVANTAGE, output_format='pandas')
|
||||
s_interval = str(ns_parser.n_interval)+'min'
|
||||
df_stock, d_stock_metadata = ts.get_intraday(symbol=ns_parser.s_ticker, outputsize='full', interval=s_interval)
|
||||
ts = TimeSeries(key=cfg.API_KEY_ALPHAVANTAGE, output_format='pandas')
|
||||
# Daily
|
||||
if s_interval == "1440min":
|
||||
df_stock, d_stock_metadata = ts.get_daily_adjusted(symbol=s_ticker, outputsize='full')
|
||||
# Intraday
|
||||
else:
|
||||
df_stock, d_stock_metadata = ts.get_intraday(symbol=s_ticker, outputsize='full', interval=s_interval)
|
||||
|
||||
except:
|
||||
print("Either the ticker or the API_KEY are invalids. Try again!")
|
||||
return
|
||||
|
||||
# Slice dataframe from the starting date YYYY-MM-DD selected
|
||||
if ns_parser.s_start_date:
|
||||
df_stock = df_stock[ns_parser.s_start_date:]
|
||||
df_stock = df_stock[s_start:]
|
||||
|
||||
# Intraday data
|
||||
if ns_parser.b_intraday:
|
||||
# The default doesn't exist for intradaily data
|
||||
if ns_parser.n_type == 5:
|
||||
ln_col_idx = [3]
|
||||
else:
|
||||
ln_col_idx = [int(x)-1 for x in list(str(ns_parser.n_type))]
|
||||
# Check that the types given are not bigger than 3, as there are only 4 types (0-3)
|
||||
if len([i for i in ln_col_idx if i > 3]) > 0:
|
||||
print("An index bigger than 3 was given, which is wrong. Try again")
|
||||
return
|
||||
|
||||
# Append last column of df to be filtered which corresponds to: 5. Volume
|
||||
ln_col_idx.append(4)
|
||||
# Daily data
|
||||
else:
|
||||
# The default doesn't exist for intradaily data
|
||||
# Daily
|
||||
if s_interval == "1440min":
|
||||
# The default doesn't exist for intradaily data
|
||||
ln_col_idx = [int(x)-1 for x in list(str(ns_parser.n_type))]
|
||||
if 4 not in ln_col_idx:
|
||||
ln_col_idx.append(4)
|
||||
@@ -141,5 +130,19 @@ def view(l_args, s_ticker, s_start, df_stock):
|
||||
return
|
||||
# Append last column of df to be filtered which corresponds to: 6. Volume
|
||||
ln_col_idx.append(5)
|
||||
# Intraday
|
||||
else:
|
||||
# The default doesn't exist for intradaily data
|
||||
if ns_parser.n_type == 5:
|
||||
ln_col_idx = [3]
|
||||
else:
|
||||
ln_col_idx = [int(x)-1 for x in list(str(ns_parser.n_type))]
|
||||
# Check that the types given are not bigger than 3, as there are only 4 types (0-3)
|
||||
if len([i for i in ln_col_idx if i > 3]) > 0:
|
||||
print("An index bigger than 3 was given, which is wrong. Try again")
|
||||
return
|
||||
# Append last column of df to be filtered which corresponds to: 5. Volume
|
||||
ln_col_idx.append(4)
|
||||
|
||||
# Plot view of the stock
|
||||
plot_view_stock(df_stock.iloc[:, ln_col_idx], ns_parser.s_ticker)
|
||||
|
||||
@@ -6,7 +6,7 @@ import config_bot as cfg
|
||||
from stock_market_helper_funcs import *
|
||||
|
||||
# ----------------------------------------------------- SMA -----------------------------------------------------
|
||||
def sma(l_args, s_ticker, df_stock):
|
||||
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
|
||||
@@ -28,15 +28,21 @@ def sma(l_args, s_ticker, df_stock):
|
||||
print(f"The following args couldn't be interpreted: {l_unknown_args}")
|
||||
|
||||
try:
|
||||
df_ta = ta.sma(df_stock['5. adjusted close'], timeperiod=ns_parser.n_time_period).dropna()
|
||||
plot_stock_ta(df_stock['5. adjusted close'], s_ticker, df_ta, f"{ns_parser.n_time_period} SMA")
|
||||
# Daily
|
||||
if s_interval == "1440min":
|
||||
df_ta = ta.sma(df_stock['5. adjusted close'], timeperiod=ns_parser.n_time_period).dropna()
|
||||
plot_stock_ta(df_stock['5. adjusted close'], s_ticker, df_ta, f"{ns_parser.n_time_period} SMA")
|
||||
# Intraday
|
||||
else:
|
||||
df_ta = ta.sma(df_stock['4. close'], timeperiod=ns_parser.n_time_period).dropna()
|
||||
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, f"{ns_parser.n_time_period} SMA")
|
||||
except:
|
||||
print("")
|
||||
return
|
||||
|
||||
|
||||
# ----------------------------------------------------- EMA -----------------------------------------------------
|
||||
def ema(l_args, s_ticker, df_stock):
|
||||
def ema(l_args, s_ticker, s_interval, df_stock):
|
||||
parser = argparse.ArgumentParser(prog='ema',
|
||||
description=""" The Exponential Moving Average is a staple of technical
|
||||
analysis and is used in countless technical indicators. In a Simple Moving
|
||||
@@ -59,15 +65,21 @@ def ema(l_args, s_ticker, df_stock):
|
||||
print(f"The following args couldn't be interpreted: {l_unknown_args}")
|
||||
|
||||
try:
|
||||
df_ta = ta.ema(df_stock['5. adjusted close'], timeperiod=ns_parser.n_time_period).dropna()
|
||||
plot_stock_ta(df_stock['5. adjusted close'], s_ticker, df_ta, f"{ns_parser.n_time_period} EMA")
|
||||
# Daily
|
||||
if s_interval == "1440min":
|
||||
df_ta = ta.ema(df_stock['5. adjusted close'], timeperiod=ns_parser.n_time_period).dropna()
|
||||
plot_stock_ta(df_stock['5. adjusted close'], s_ticker, df_ta, f"{ns_parser.n_time_period} EMA")
|
||||
# Intraday
|
||||
else:
|
||||
df_ta = ta.ema(df_stock['4. close'], timeperiod=ns_parser.n_time_period).dropna()
|
||||
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, f"{ns_parser.n_time_period} EMA")
|
||||
except:
|
||||
print("")
|
||||
return
|
||||
|
||||
|
||||
# ----------------------------------------------------- MACD -----------------------------------------------------
|
||||
def macd(l_args, s_ticker, df_stock):
|
||||
def macd(l_args, s_ticker, s_interval, df_stock):
|
||||
parser = argparse.ArgumentParser(prog='macd',
|
||||
description=""" The Moving Average Convergence Divergence (MACD) is the difference
|
||||
between two Exponential Moving Averages. The Signal line is an Exponential Moving
|
||||
@@ -98,12 +110,17 @@ def macd(l_args, s_ticker, df_stock):
|
||||
print(f"The following args couldn't be interpreted: {l_unknown_args}")
|
||||
|
||||
try:
|
||||
df_ta = ta.macd(df_stock['5. adjusted close'],
|
||||
fast=ns_parser.n_fast,
|
||||
slow=ns_parser.n_slow,
|
||||
signal=ns_parser.n_signal,
|
||||
offset=ns_parser.n_offset).dropna()
|
||||
|
||||
# Daily
|
||||
if s_interval == "1440min":
|
||||
df_ta = ta.macd(df_stock['5. adjusted close'],
|
||||
fast=ns_parser.n_fast, slow=ns_parser.n_slow,
|
||||
signal=ns_parser.n_signal, offset=ns_parser.n_offset).dropna()
|
||||
# Intraday
|
||||
else:
|
||||
df_ta = ta.macd(df_stock['4. close'],
|
||||
fast=ns_parser.n_fast, slow=ns_parser.n_slow,
|
||||
signal=ns_parser.n_signal, offset=ns_parser.n_offset).dropna()
|
||||
|
||||
plot_ta(s_ticker, df_ta, f"{ns_parser.n_fast}-{ns_parser.n_slow}-{ns_parser.n_signal}-{ns_parser.n_offset} MACD")
|
||||
except:
|
||||
print("")
|
||||
@@ -111,7 +128,7 @@ def macd(l_args, s_ticker, df_stock):
|
||||
|
||||
|
||||
# ----------------------------------------------------- VWAP -----------------------------------------------------
|
||||
def vwap(l_args, s_ticker, s_start):
|
||||
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
|
||||
@@ -129,27 +146,15 @@ def vwap(l_args, s_ticker, s_start):
|
||||
print(f"The following args couldn't be interpreted: {l_unknown_args}")
|
||||
|
||||
try:
|
||||
ts = TimeSeries(key=cfg.API_KEY_ALPHAVANTAGE, output_format='pandas')
|
||||
s_interval = str(ns_parser.n_interval)+'min'
|
||||
df_stock, d_stock_metadata = ts.get_intraday(symbol=s_ticker, outputsize='full', interval=s_interval)
|
||||
|
||||
if s_start:
|
||||
df_stock = df_stock[s_start:]
|
||||
|
||||
df_ta = ta.vwap(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['4. close'], volume=df_stock['5. volume'], interval=s_interval)
|
||||
plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, f"{s_interval} VWAP")
|
||||
|
||||
# ti = TechIndicators(cfg.API_KEY_FINANCIALMODELINGPREP, output_format='pandas')
|
||||
# df_ta, d_ta_metadata = ti.get_vwap(symbol=s_ticker, interval=s_interval)
|
||||
# plot_ta(s_ticker, df_ta, f"{s_interval} VWAP")
|
||||
|
||||
except:
|
||||
print("")
|
||||
return
|
||||
|
||||
|
||||
# ----------------------------------------------------- STOCH -----------------------------------------------------
|
||||
def stoch(l_args, s_ticker, df_stock):
|
||||
def stoch(l_args, s_ticker, s_interval, df_stock):
|
||||
parser = argparse.ArgumentParser(prog='stoch',
|
||||
description=""" The Stochastic Oscillator measures where the close is in relation
|
||||
to the recent trading range. The values range from zero to 100. %D values over 75
|
||||
@@ -193,7 +198,7 @@ def stoch(l_args, s_ticker, df_stock):
|
||||
|
||||
|
||||
# ----------------------------------------------------- RSI -----------------------------------------------------
|
||||
def rsi(l_args, s_ticker, df_stock):
|
||||
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
|
||||
@@ -214,8 +219,13 @@ def rsi(l_args, s_ticker, df_stock):
|
||||
print(f"The following args couldn't be interpreted: {l_unknown_args}")
|
||||
|
||||
try:
|
||||
df_ta = ta.rsi(df_stock['5. adjusted close'], time_period=ns_parser.n_timeperiod).dropna()
|
||||
|
||||
# Daily
|
||||
if s_interval == "1440min":
|
||||
df_ta = ta.rsi(df_stock['5. adjusted close'], time_period=ns_parser.n_timeperiod).dropna()
|
||||
# Intraday
|
||||
else:
|
||||
df_ta = ta.rsi(df_stock['4. close'], time_period=ns_parser.n_timeperiod).dropna()
|
||||
|
||||
plot_ta(s_ticker, df_ta, f"{ns_parser.n_timeperiod} RSI")
|
||||
except:
|
||||
print("")
|
||||
|
||||
Reference in New Issue
Block a user