def plot_macd(symbol, period=default_periods, refresh=False, start_date=config.start_date, end_date=config.end_date):
"""Calculates the macd for the given symbol, saves this data in a .csv file, and plots this data
The MACD is a lagging trend indicator.
Parameters:
symbol : str
period : int or list of int, optional
Must contain 3 values. First value is signal line, second is fast line, third is slow line.\
refresh : bool, optional
start_date : date, optional
end_date : date, optional
Returns:
figure, axes
A figure and axes containing the macd for the given symbol
"""
if not utils.refresh(utils.get_file_path(config.ta_data_path, table_filename, symbol=symbol), refresh=refresh):
df = pd.read_csv(utils.get_file_path(config.ta_data_path, table_filename, symbol=symbol), index_col="Date", parse_dates=["Date"])[start_date:end_date]
else:
if utils.refresh(utils.get_file_path(config.prices_data_path, prices.price_table_filename, symbol=symbol), refresh=refresh):
prices.download_data_from_yahoo(symbol, start_date=start_date, end_date=end_date)
df = pd.read_csv(utils.get_file_path(config.prices_data_path, prices.price_table_filename, symbol=symbol), usecols=["Date", "Close"], index_col="Date", parse_dates=["Date"])[start_date:end_date]
if len(period) != 3:
raise ValueError("MACD requires 3 periods")
if len(df) < period[-1]:
raise ta.InsufficientDataException("Not enough data to compute a period length of " + str(period))
fig, ax = plt.subplots(2, figsize=config.figsize)
ax[0].plot(df.index, df["Close"], label="Price")
utils.prettify_ax(ax[0], title=symbol + "Price", start_date=start_date, end_date=end_date)
macd_column_name = "MACD" + str(period[1]) + "-" + str(period[2])
signal_column_name = "MACD" + str(period[0])
if macd_column_name not in df.columns or signal_column_name not in df.columns:
df = df.join(macd(symbol, period, refresh=False, start_date=start_date, end_date=end_date))
# if len(df) > period[0] and len(df) > period[1] and len(df) > period[2]: # to prevent AttributeError when the column is all None
ax[1].plot(df.index, df[macd_column_name], label="MACD")
ax[1].plot(df.index, df[signal_column_name], label="Signal")
ax[1].plot(df.index, (df[macd_column_name] - df[signal_column_name]), label="Histogram")
# Can't overlay a histogram with line plots so the histogram has to also be a line plot
# ax[1].bar(df.index, np.histogram(np.isfinite(df[signal_column_name] - df[macd_column_name])), normed=True, alpha=config.alpha) # ValueError: incompatible sizes: argument 'height' must be length 3876 or scalar
utils.prettify_ax(ax[1], title=symbol + "MACD", center=True, start_date=start_date, end_date=end_date)
utils.prettify_fig(fig)
fig.savefig(utils.get_file_path(config.ta_graphs_path, get_signal_name(period) + graph_filename, symbol=symbol))
utils.debug(fig)
return fig, ax
def plot_ema(symbol,
period=default_periods,
refresh=False,
start_date=config.start_date,
end_date=config.end_date):
"""Calculates the exponential moving agerage for each period for the given symbol, saves this data in a .csv file, and plots this data
The EMA is a lagging trend indicator.
Parameters:
symbol : str
period : int or list of int, optional
refresh : bool, optional
start_date : date, optional
end_date : date, optional
Returns:
figure, axes
A figure and axes containing the exponential moving agerage for the given symbol
"""
if not utils.refresh(utils.get_file_path(
config.ta_data_path, table_filename, symbol=symbol),
refresh=refresh):
df = pd.read_csv(utils.get_file_path(config.ta_data_path,
table_filename,
symbol=symbol),
index_col="Date",
parse_dates=["Date"])[start_date:end_date]
else:
if utils.refresh(utils.get_file_path(config.prices_data_path,
prices.price_table_filename,
symbol=symbol),
refresh=refresh):
prices.download_data_from_yahoo(symbol,
start_date=start_date,
end_date=end_date)
df = pd.read_csv(utils.get_file_path(config.prices_data_path,
prices.price_table_filename,
symbol=symbol),
usecols=["Date", "Close"],
index_col="Date",
parse_dates=["Date"])[start_date:end_date]
if isinstance(period, int):
period = [period]
period.sort()
if len(df) < period[-1]:
raise ta.InsufficientDataException(
"Not enough data to compute a period length of " + str(period[-1]))
fig, ax = plt.subplots(figsize=config.figsize)
ax.plot(df.index, df["Close"], label="Price")
for p in period:
column_name = "EMA" + str(p)
if column_name not in df.columns:
df = df.join(
ema(symbol,
p,
refresh=False,
start_date=start_date,
end_date=end_date))
# if len(df) > p: # to prevent AttributeError when the column is all None
ax.plot(df.index, df[column_name], label=column_name)
utils.prettify_ax(ax,
title=symbol + "EMA" + "-".join(str(p) for p in period),
start_date=start_date,
end_date=end_date)
utils.prettify_fig(fig)
fig.savefig(
utils.get_file_path(config.ta_graphs_path,
"-".join(str(p) for p in period) + graph_filename,
symbol=symbol))
utils.debug(fig)
return fig, ax
def plot_bb(symbol,
period=default_period,
std=default_std,
refresh=False,
start_date=config.start_date,
end_date=config.end_date):
"""Calculates the bollinger bands for each period for the given symbol, saves this data in a .csv file, and plots this data
The BB is a lagging volatility indicator.
Parameters:
symbol : str
period : int, optional
std : int, optional
refresh : bool, optional
start_date : date, optional
end_date : date, optional
Returns:
figure, axes
A figure and axes containing the bollinger bands for the given symbol
"""
if not utils.refresh(utils.get_file_path(
config.ta_data_path, table_filename, symbol=symbol),
refresh=refresh):
df = pd.read_csv(utils.get_file_path(config.ta_data_path,
table_filename,
symbol=symbol),
index_col="Date",
parse_dates=["Date"])[start_date:end_date]
else:
if utils.refresh(utils.get_file_path(config.prices_data_path,
prices.price_table_filename,
symbol=symbol),
refresh=refresh):
prices.download_data_from_yahoo(symbol,
start_date=start_date,
end_date=end_date)
df = pd.read_csv(utils.get_file_path(config.prices_data_path,
prices.price_table_filename,
symbol=symbol),
usecols=["Date", "Close"],
index_col="Date",
parse_dates=["Date"])[start_date:end_date]
if len(df) < period:
raise ta.InsufficientDataException(
"Not enough data to compute a period length of " + str(period))
fig, ax = plt.subplots(figsize=config.figsize)
ax.plot(df.index, df["Close"], label="Price")
if "Lower" not in df.columns or "Upper" not in df.columns:
df = df.join(
bb(symbol,
period,
std,
refresh=False,
start_date=start_date,
end_date=end_date))
# if len(df) > p: # to prevent AttributeError when the column is all None
ax.plot(df.index, df["Lower"], label="Lower", color="skyblue")
ax.plot(df.index, df["Upper"], label="Upper", color="skyblue")
ax.fill_between(df.index, df["Lower"], df["Upper"], color='lightskyblue')
utils.prettify_ax(ax,
title=symbol + "BB",
start_date=start_date,
end_date=end_date)
utils.prettify_fig(fig)
fig.savefig(
utils.get_file_path(config.ta_graphs_path,
get_signal_name(period, std) + graph_filename,
symbol=symbol))
utils.debug(fig)
return fig, ax
def plot_rsi(symbol,
period=default_period,
thresholds=default_thresholds,
refresh=False,
start_date=config.start_date,
end_date=config.end_date):
"""Calculates the relative strength index for each period for the given symbol, saves this data in a .csv file, and plots this data
The RSI is a leading momentum indicator.
Parameters:
symbol : str
period : int, optional
thresholds: dict
Must contain keys "Low" and "High", both with a value between 0 and 100
refresh : bool, optional
start_date : date, optional
end_date : date, optional
Returns:
figure, axes
A figure and axes containing the relative strength index for the given symbol
"""
if not utils.refresh(utils.get_file_path(
config.ta_data_path, table_filename, symbol=symbol),
refresh=refresh):
df = pd.read_csv(utils.get_file_path(config.ta_data_path,
table_filename,
symbol=symbol),
index_col="Date",
parse_dates=["Date"])[start_date:end_date]
else:
if utils.refresh(utils.get_file_path(config.prices_data_path,
prices.price_table_filename,
symbol=symbol),
refresh=refresh):
prices.download_data_from_yahoo(symbol,
start_date=start_date,
end_date=end_date)
df = pd.read_csv(utils.get_file_path(config.prices_data_path,
prices.price_table_filename,
symbol=symbol),
usecols=["Date", "Close"],
index_col="Date",
parse_dates=["Date"])[start_date:end_date]
if len(df) < period:
raise ta.InsufficientDataException(
"Not enough data to compute a period length of " + str(period))
fig, ax = plt.subplots(2, figsize=config.figsize)
ax[0].plot(df.index, df["Close"], label="Price")
utils.prettify_ax(ax[0],
title=symbol + "Price",
start_date=start_date,
end_date=end_date)
if "RSI" + str(period) not in df.columns:
df = df.join(
rsi(symbol,
period,
refresh=False,
start_date=start_date,
end_date=end_date))
# if len(df) > period: # to prevent AttributeError when the column is all None
ax[1].plot(df.index, df["RSI" + str(period)], label="RSI" + str(period))
ax[1].plot(df.index, [thresholds["Low"]] * len(df.index),
label="Oversold",
color="red")
ax[1].plot(df.index, [50] * len(df.index), color="black")
ax[1].plot(df.index, [thresholds["High"]] * len(df.index),
label="Overbought",
color="red")
utils.prettify_ax(ax[1],
title=symbol + "RSI" + str(period),
start_date=start_date,
end_date=end_date)
utils.prettify_fig(fig)
fig.savefig(
utils.get_file_path(config.ta_graphs_path,
get_signal_name(period) + graph_filename,
symbol=symbol))
utils.debug(fig)
return fig, ax