finance-api/notebooks/sma.ipynb

In [25]:

import sys
import os


import pandas as pd
import matplotlib.pyplot as plt


# Prevent scientific notation in pandas output.
pd.set_option('display.float_format', lambda x: '%.3f' % x)


# Add the parent directory to sys.path
sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), '..')))


from database import Database
from model.ticker import Ticker


DB_PATH = "sqlite:///../finance.db"


db = Database(database_url=DB_PATH)

In [26]:

year = 2010
year_2010 = f"SELECT * FROM sma WHERE strftime('%Y', sma.date) = '{year}'"

In [27]:

year = 2000
year_2000 = f"SELECT * FROM sma WHERE strftime('%Y', sma.date) = '{year}'"

In [28]:

year = 2020
year_2020 = f"SELECT * FROM sma WHERE strftime('%Y', sma.date) = '{year}'"

In [29]:

df_2010 = pd.read_sql(year_2010, db.engine)
df_2010['date'] = pd.to_datetime(df_2010['date'], format='%Y-%m-%d %H:%M:%S.%f')

In [30]:

df_2000 = pd.read_sql(year_2000, db.engine)
df_2000['date'] = pd.to_datetime(df_2000['date'], format='%Y-%m-%d %H:%M:%S.%f')

In [31]:

df_2020 = pd.read_sql(year_2020, db.engine)
df_2020['date'] = pd.to_datetime(df_2020['date'], format='%Y-%m-%d %H:%M:%S.%f')

In [32]:

symbol = 'HPAR-fake'

hpar_2000 = df_2000[df_2000['symbol'] == symbol]
hpar_2010 = df_2010[df_2010['symbol'] == symbol]
hpar_2020 = df_2020[df_2020['symbol'] == symbol]

In [33]:

hpar_2000

Out[33]:

	id	date	symbol	sma_5	sma_10	sma_20	sma_50	sma_100	sma_200
0	1	2000-01-03	HPAR-fake	NaN	NaN	NaN	NaN	NaN	NaN
1	2	2000-01-04	HPAR-fake	NaN	NaN	NaN	NaN	NaN	NaN
2	3	2000-01-05	HPAR-fake	NaN	NaN	NaN	NaN	NaN	NaN
3	4	2000-01-06	HPAR-fake	NaN	NaN	NaN	NaN	NaN	NaN
4	5	2000-01-07	HPAR-fake	99.581	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...
255	256	2000-12-25	HPAR-fake	59.474	59.585	60.467	63.515	67.022	73.605
256	257	2000-12-26	HPAR-fake	59.345	59.454	60.366	63.277	66.897	73.417
257	258	2000-12-27	HPAR-fake	59.264	59.338	60.278	63.063	66.769	73.238
258	259	2000-12-28	HPAR-fake	59.412	59.436	60.205	62.887	66.644	73.064
259	260	2000-12-29	HPAR-fake	59.502	59.465	60.078	62.721	66.512	72.893

260 rows × 9 columns

In [34]:

hpar_2010

Out[34]:

	id	date	symbol	sma_5	sma_10	sma_20	sma_50	sma_100	sma_200
0	2610	2010-01-01	HPAR-fake	51.884	52.313	52.161	53.432	51.925	53.475
1	2611	2010-01-04	HPAR-fake	51.872	52.260	52.077	53.417	51.946	53.449
2	2612	2010-01-05	HPAR-fake	51.959	52.312	52.072	53.405	51.973	53.428
3	2613	2010-01-06	HPAR-fake	52.078	52.271	52.069	53.409	52.005	53.407
4	2614	2010-01-07	HPAR-fake	52.306	52.229	52.119	53.403	52.030	53.391
...	...	...	...	...	...	...	...	...	...
256	2866	2010-12-27	HPAR-fake	49.988	50.002	48.291	46.279	45.890	47.166
257	2867	2010-12-28	HPAR-fake	49.941	50.039	48.499	46.411	45.926	47.165
258	2868	2010-12-29	HPAR-fake	49.998	50.082	48.723	46.551	45.963	47.166
259	2869	2010-12-30	HPAR-fake	50.082	50.165	48.949	46.695	46.005	47.170
260	2870	2010-12-31	HPAR-fake	50.299	50.207	49.212	46.852	46.059	47.180

261 rows × 9 columns

In [35]:

hpar_2020

Out[35]:

	id	date	symbol	sma_5	sma_10	sma_20	sma_50	sma_100	sma_200
0	5218	2020-01-01	HPAR-fake	51.882	52.465	52.040	50.035	50.018	51.250
1	5219	2020-01-02	HPAR-fake	51.558	52.415	52.081	50.111	50.063	51.197
2	5220	2020-01-03	HPAR-fake	51.527	52.330	52.115	50.209	50.117	51.147
3	5221	2020-01-06	HPAR-fake	51.618	52.193	52.117	50.298	50.171	51.102
4	5222	2020-01-07	HPAR-fake	51.828	52.109	52.120	50.398	50.237	51.065
...	...	...	...	...	...	...	...	...	...
257	5475	2020-12-25	HPAR-fake	63.722	62.907	64.027	65.468	62.996	61.716
258	5476	2020-12-28	HPAR-fake	64.075	62.987	63.950	65.420	63.055	61.731
259	5477	2020-12-29	HPAR-fake	64.307	63.241	63.903	65.383	63.123	61.752
260	5478	2020-12-30	HPAR-fake	64.618	63.615	63.860	65.360	63.187	61.767
261	5479	2020-12-31	HPAR-fake	64.922	64.063	63.843	65.348	63.255	61.776

262 rows × 9 columns

In [55]:

# Plot the SMAs
plt.figure(figsize=(12, 6))

plt.plot(hpar_2020.index, hpar_2020['sma_200'], label="sma_200_days")
plt.plot(hpar_2020.index, hpar_2020['sma_50'], label="sma_50_days")
plt.plot(hpar_2020.index, hpar_2020['sma_5'], label="sma_5_days")

plt.xlabel('Date')
plt.ylabel('SMA Value')
plt.title(f'5, 50, 200 Day Moving Averages For {symbol}')
plt.legend()
plt.grid(True)
plt.show()

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