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clean up menu and docstrings

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This commit is contained in:
Jamie
2021-05-14 14:57:19 -04:00
parent 62b074b48f
commit 637a75661a
6 changed files with 0 additions and 327 deletions
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3
gamestonk_terminal/prediction_techniques/arima_view.py
View File

@@ -41,9 +41,6 @@ def arima(other_args: List[str], s_ticker: str, df_stock: pd.DataFrame):
df_stock: pd.DataFrame
Dataframe of prices
Returns
-------
"""
parser = argparse.ArgumentParser(
add_help=False,

3
gamestonk_terminal/prediction_techniques/ets_view.py
View File

@@ -61,9 +61,6 @@ def exponential_smoothing(other_args: List[str], s_ticker: str, df_stock: pd.Dat
df_stock: pd.DataFrame
Loaded stock dataframe
Returns
-------
"""
parser = argparse.ArgumentParser(
add_help=False,

7
gamestonk_terminal/prediction_techniques/pred_controller.py
View File

@@ -17,7 +17,6 @@ from gamestonk_terminal.prediction_techniques import (
knn_view,
neural_networks_view,
regression_view,
sma_view,
)
@@ -29,7 +28,6 @@ class PredictionTechniquesController:
"help",
"q",
"quit",
"sma",
"ets",
"knn",
"linear",
@@ -81,7 +79,6 @@ class PredictionTechniquesController:
print(" q quit this menu, and shows back to main menu")
print(" quit quit to abandon program")
print("")
print(" sma simple moving average")
print(" ets exponential smoothing (e.g. Holt-Winters)")
print(" knn k-Nearest Neighbors")
print(" linear linear regression (polynomial 1)")
@@ -125,10 +122,6 @@ class PredictionTechniquesController:
"""Process Quit command - quit the program"""
return True
def call_sma(self, other_args: List[str]):
"""Process sma command"""
sma_view.simple_moving_average(other_args, self.ticker, self.stock)
def call_ets(self, other_args: List[str]):
"""Process ets command"""
ets_view.exponential_smoothing(other_args, self.ticker, self.stock)

4
gamestonk_terminal/prediction_techniques/pred_helper.py
View File

@@ -29,10 +29,6 @@ from gamestonk_terminal import feature_flags as gtff
from gamestonk_terminal.config_plot import PLOT_DPI
def get_backtesting_data():
pass
register_matplotlib_converters()
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"

3
gamestonk_terminal/prediction_techniques/regression_view.py
View File

@@ -54,9 +54,6 @@ def regression(
polynomial: int
Order of polynomial
Returns
-------
"""
parser = argparse.ArgumentParser(
add_help=False,

307
gamestonk_terminal/prediction_techniques/sma_view.py
View File

@@ -1,307 +0,0 @@
""" SMA View"""
__docformat__ = "numpy"
import argparse
from typing import List
import datetime
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
from gamestonk_terminal.helper_funcs import (
check_positive,
parse_known_args_and_warn,
valid_date,
patch_pandas_text_adjustment,
get_next_stock_market_days,
plot_autoscale,
)
from gamestonk_terminal.prediction_techniques.pred_helper import (
print_pretty_prediction,
price_prediction_backtesting_color,
print_prediction_kpis,
get_backtesting_data,
)
from gamestonk_terminal.config_plot import PLOT_DPI
from gamestonk_terminal import feature_flags as gtff
register_matplotlib_converters()
def simple_moving_average(other_args: List[str], s_ticker: str, df_stock: pd.DataFrame):
"""
Run an SMA model
Parameters
----------
other_args: List[str]
Argparse arguments
s_ticker: str
Stock ticker
df_stock: pd.DataFrame
Dataframe of stock prices
Returns
-------
"""
parser = argparse.ArgumentParser(
add_help=False,
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=20,
help="length of SMA window.",
)
parser.add_argument(
"-d",
"--days",
action="store",
dest="n_days",
type=check_positive,
default=5,
help="prediction days.",
)
parser.add_argument(
"-e",
"--end",
action="store",
type=valid_date,
dest="s_end_date",
default=None,
help="The end date (format YYYY-MM-DD) to select - Backtesting",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
# BACKTESTING
if ns_parser.s_end_date:
df_future, df_stock, bt_flag = get_backtesting_data(
df_stock, ns_parser.s_end_date, ns_parser.n_days
)
if not bt_flag:
return
# Prediction data
l_predictions = list() # type: List
for pred_day in range(ns_parser.n_days):
if pred_day < ns_parser.n_length:
l_ma_stock = df_stock["5. adjusted close"].values[
-ns_parser.n_length + pred_day :
]
else:
l_ma_stock = list()
l_predictions.append(np.mean(np.append(l_ma_stock, l_predictions)))
l_pred_days = get_next_stock_market_days(
last_stock_day=df_stock["5. adjusted close"].index[-1],
n_next_days=ns_parser.n_days,
)
df_pred = pd.Series(l_predictions, index=l_pred_days, name="Price")
# Plotting
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
# BACKTESTING
if ns_parser.s_end_date:
plt.title(
f"BACKTESTING: {ns_parser.n_length} Moving Average on {s_ticker} - {ns_parser.n_days} days prediction"
)
else:
plt.title(
f"{ns_parser.n_length} Moving Average on {s_ticker} - {ns_parser.n_days} days prediction"
)
plt.xlim(
df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1]
)
plt.xlabel("Time")
plt.ylabel("Share Price ($)")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
# df_ma = df_stock["5. adjusted close"].rolling(window=ns_parser.n_length).mean()
# plt.plot(df_ma.index, df_ma, lw=2, linestyle="--", c="tab:orange")
plt.plot(
[df_stock.index[-1], df_pred.index[0]],
[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
lw=1,
c="tab:green",
linestyle="--",
)
plt.plot(df_pred.index, df_pred, lw=2, c="tab:green")
plt.axvspan(
df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
)
_, _, ymin, ymax = plt.axis()
plt.vlines(
df_stock.index[-1], ymin, ymax, linewidth=1, linestyle="--", color="k"
)
# BACKTESTING
if ns_parser.s_end_date:
plt.plot(
df_future.index,
df_future["5. adjusted close"],
lw=2,
c="tab:blue",
ls="--",
)
plt.plot(
[df_stock.index[-1], df_future.index[0]],
[
df_stock["5. adjusted close"].values[-1],
df_future["5. adjusted close"].values[0],
],
lw=1,
c="tab:blue",
linestyle="--",
)
if gtff.USE_ION:
plt.ion()
plt.show()
# BACKTESTING
if ns_parser.s_end_date:
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.subplot(211)
plt.plot(
df_future.index,
df_future["5. adjusted close"],
lw=2,
c="tab:blue",
ls="--",
)
plt.plot(df_pred.index, df_pred, lw=2, c="green")
plt.scatter(
df_future.index, df_future["5. adjusted close"], c="tab:blue", lw=3
)
plt.plot(
[df_stock.index[-1], df_future.index[0]],
[
df_stock["5. adjusted close"].values[-1],
df_future["5. adjusted close"].values[0],
],
lw=2,
c="tab:blue",
ls="--",
)
plt.scatter(df_pred.index, df_pred, c="green", lw=3)
plt.plot(
[df_stock.index[-1], df_pred.index[0]],
[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
lw=2,
c="green",
ls="--",
)
plt.title("BACKTESTING: Real data price versus Prediction")
plt.xlim(df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1))
plt.xticks(
[df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1)],
visible=True,
)
plt.ylabel("Share Price ($)")
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.legend(["Real data", "Prediction data"])
plt.xticks([])
plt.subplot(212)
plt.axhline(y=0, color="k", linestyle="--", linewidth=2)
plt.plot(
df_future.index,
100
* (df_pred.values - df_future["5. adjusted close"].values)
/ df_future["5. adjusted close"].values,
lw=2,
c="red",
)
plt.scatter(
df_future.index,
100
* (df_pred.values - df_future["5. adjusted close"].values)
/ df_future["5. adjusted close"].values,
c="red",
lw=5,
)
plt.title("BACKTESTING: Error between Real data and Prediction [%]")
plt.plot(
[df_stock.index[-1], df_future.index[0]],
[
0,
100
* (df_pred.values[0] - df_future["5. adjusted close"].values[0])
/ df_future["5. adjusted close"].values[0],
],
lw=2,
ls="--",
c="red",
)
plt.xlim(df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1))
plt.xticks(
[df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1)],
visible=True,
)
plt.xlabel("Time")
plt.ylabel("Prediction Error (%)")
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.legend(["Real data", "Prediction data"])
if gtff.USE_ION:
plt.ion()
plt.show()
# Refactor prediction dataframe for backtesting print
df_pred.name = "Prediction"
df_pred = df_pred.to_frame()
df_pred["Real"] = df_future["5. adjusted close"]
if gtff.USE_COLOR:
patch_pandas_text_adjustment()
print("Time Real [$] x Prediction [$]")
print(
df_pred.apply(
price_prediction_backtesting_color, axis=1
).to_string()
)
else:
print(df_pred[["Real", "Prediction"]].round(2).to_string())
print("")
print_prediction_kpis(df_pred["Real"].values, df_pred["Prediction"].values)
else:
# Print prediction data
print_pretty_prediction(df_pred, df_stock["5. adjusted close"].values[-1])
print("")
except Exception as e:
print(e)
print("")