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Add sma and ema technical analysis commands

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This commit is contained in:
didier
2020-12-21 23:58:17 +00:00
parent 580e29d9ed
commit 62ad5ac1c2
1 changed files with 134 additions and 15 deletions
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149
stock_market_bot.py
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@@ -2,13 +2,22 @@
Is this not informative enough?
Load a specific stock
Then apply a specific TA on it
Do this because AlphaVantage only allows 5 API calls per minutes.
This way we only need 1 call, and can apply TA to the result without
using an API request.
Finviz for data
"""
from alpha_vantage.timeseries import TimeSeries
import argparse
import config_bot as cfg
import pandas as pd
import pandas_ta as ta
from datetime import datetime
import matplotlib
import matplotlib.pyplot as plt
@@ -21,8 +30,13 @@ def print_help():
""" Print help
"""
print("What do you want to do?")
print("gainers show latest top gainers")
print("view view a specific stock tick")
print("\n--- Screeners:")
print("gainers show latest top gainers")
print("\n--- Technical Analysis:")
print("sma simple moving average")
print("ema exponential moving average")
print("\n--- Others:")
print("help help to see this menu again")
print("quit to abandon the program")
@@ -44,9 +58,9 @@ def valid_date(s):
# -----------------------------------------------------------------------------------------------------------------------
def plot_dataframe(df, symbol):
def plot_view_stock(df, symbol):
pfig, axVolume = plt.subplots()
plt.bar(df.index, df['5. volume'].values, color='k')
plt.bar(df.index, df['5. volume'].values, color='k', alpha=0.8)
plt.ylabel('Volume')
axPrice = axVolume.twinx()
plt.plot(df.index, df.iloc[:, :-1])
@@ -61,6 +75,21 @@ def plot_dataframe(df, symbol):
plt.show()
# -----------------------------------------------------------------------------------------------------------------------
def plot_stock_ta(df_stock, s_stock, df_ta, s_ta):
plt.plot(df_stock.index, df_stock['4. close'].values)
plt.plot(df_ta.index, df_ta.values)
plt.title(f"{s_ta} on {s_stock}")
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.xlabel('Time')
plt.ylabel('Share Price ($)')
plt.legend([s_stock, s_ta])
plt.grid(b=True, which='major', color='#666666', linestyle='-')
plt.minorticks_on()
plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
plt.show()
# -----------------------------------------------------------------------------------------------------------------------
def main():
""" Main function of the program
@@ -71,12 +100,10 @@ def main():
#parser.add_argument('-b', action="store", dest="b", help='example of string')
#parser.add_argument('-c', action="store", dest="c", type=int, help='example of int')
main_parser = argparse.ArgumentParser(prog='stock_market_bot', # program name
# disable default help flag so that help can be an option
add_help=False)
main_parser = argparse.ArgumentParser(prog='stock_market_bot', add_help=False)
# Add list of arguments that the main parser accepts
main_parser.add_argument('cmd', choices=['quit', 'help', 'gainers' ,'view'])
main_parser.add_argument('cmd', choices=['quit', 'help', 'gainers' ,'view', 'sma', 'ema'])
# Print first welcome message and help
print("\nWelcome to Didier's Stock Market Bot\n")
@@ -114,6 +141,7 @@ def main():
df_gainers = pd.read_html('https://finance.yahoo.com/screener/predefined/day_gainers')[0]
print(df_gainers.head(ns_parser.n_gainers).to_string(index=False))
# ---------------------------------------------------- VIEW ----------------------------------------------------
elif ns_known_args.cmd == 'view':
parser = argparse.ArgumentParser(prog='view',
@@ -124,7 +152,7 @@ def main():
parser.add_argument('--type', action="store", dest="n_type", type=check_positive, default=4,
help='1234 corresponds to types: 1. open; 2. high; 3.low; 4. close \
while 14 corresponds to types: 1.open; 4. close')
parser.add_argument("-s", "--start", type=s_start_date, dest="start_date",
parser.add_argument("-s", "--start", type=valid_date, dest="s_start_date",
help="The starting date (format YYYY-MM-DD) of the stock")
try:
@@ -138,11 +166,9 @@ def main():
# Currently only supports daily stocks
# TODO: Intra-daily perhaps
ts = TimeSeries(key=cfg.API_KEY,
output_format='pandas')
ts = TimeSeries(key=cfg.API_KEY, output_format='pandas')
try:
df, meta_data = ts.get_daily(symbol=ns_parser.s_ticker,
outputsize='full')
df_stock, d_stock_metadata = ts.get_daily(symbol=ns_parser.s_ticker, outputsize='full')
except:
print("Either the ticker or the API_KEY are invalids. Try again!")
continue
@@ -156,11 +182,105 @@ def main():
# Slice dataframe from the starting date YYYY-MM-DD selected
if ns_parser.s_start_date:
df = df[ns_parser.s_start_date:]
df_stock = df_stock[ns_parser.s_start_date:]
ln_col_idx.append(4) # Append last column of df to be filtered which corresponds to: 5. Volume
plot_dataframe(df.iloc[:, ln_col_idx], ns_parser.s_ticker)
plot_view_stock(df_stock.iloc[:, ln_col_idx], ns_parser.s_ticker)
# --------------------------------------------------------------------------------------------------------------
# -------------------------------------------- TECHNICAL ANALYSIS ----------------------------------------------
# --------------------------------------------------------------------------------------------------------------
# ---------------------------------------------------- SMA ----------------------------------------------------
elif ns_known_args.cmd == 'sma':
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('-t', action="store", dest="s_ticker", required=True, help='Stock ticker')
parser.add_argument('--period', action="store", dest="n_time_period", type=check_positive, default=20,
help='Number of data points used to calculate each moving average value.')
parser.add_argument("-s", "--start", type=valid_date, dest="s_start_date",
help="The starting date (format YYYY-MM-DD) of the stock")
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
except SystemExit:
print("")
continue
if l_unknown_args:
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try: # Get stock data
ts = TimeSeries(key=cfg.API_KEY, output_format='pandas')
df_stock, d_stock_metadata = ts.get_daily(symbol=ns_parser.s_ticker, outputsize='full')
except:
print("ERROR! Try again!")
print("Check that: 1. The ticker symbol exists and is valid")
print(" 2. Less than 5 AlphaVantage requests have been made in 1 minute")
print(" 3. The API_KEY is (still) valid")
continue
# 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_ta = ta.sma(df_stock['4. close'], timeperiod=ns_parser.n_time_period).dropna()
plot_stock_ta(df_stock, d_stock_metadata['2. Symbol'], df_ta, f"{ns_parser.n_time_period} SMA")
continue
# ---------------------------------------------------- EMA ----------------------------------------------------
elif ns_known_args.cmd == 'ema':
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
Average, each value in the time period carries equal weight, and values outside
of the time period are not included in the average. However, the Exponential
Moving Average is a cumulative calculation, including all data. Past values have
a diminishing contribution to the average, while more recent values have a greater
contribution. This method allows the moving average to be more responsive to changes
in the data. """)
parser.add_argument('-t', action="store", dest="s_ticker", required=True, help='Stock ticker')
parser.add_argument('--period', action="store", dest="n_time_period", type=check_positive, default=20,
help='Number of data points used to calculate each moving average value.')
parser.add_argument("-s", "--start", type=valid_date, dest="s_start_date",
help="The starting date (format YYYY-MM-DD) of the stock")
try:
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
except SystemExit:
print("")
continue
if l_unknown_args:
print(f"The following args couldn't be interpreted: {l_unknown_args}")
try: # Get stock data
ts = TimeSeries(key=cfg.API_KEY, output_format='pandas')
df_stock, d_stock_metadata = ts.get_daily(symbol=ns_parser.s_ticker, outputsize='full')
except:
print("ERROR! Try again!")
print("Check that: 1. The ticker symbol exists and is valid")
print(" 2. Less than 5 AlphaVantage requests have been made in 1 minute")
print(" 3. The API_KEY is (still) valid")
continue
# 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_ta = ta.ema(df_stock['4. close'], timeperiod=ns_parser.n_time_period).dropna()
plot_stock_ta(df_stock, d_stock_metadata['2. Symbol'], df_ta, f"{ns_parser.n_time_period} EMA")
continue
# ---------------------------------------------------- HELP ----------------------------------------------------
@@ -179,4 +299,3 @@ def main():
if __name__ == "__main__":
main()