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Danglewood
2023-02-20 20:27:57 -08:00
parent c8bef7e0ac
commit 7fa5edc252
2 changed files with 61 additions and 41 deletions
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49
openbb_terminal/common/technical_analysis/volatility_model.py
View File

@@ -167,14 +167,13 @@ def atr(
).dropna()
)
@log_start_end(log=logger)
def rvol(
data:pd.DataFrame,
lower_q:float = 0.25,
upper_q: float = 0.75
data: pd.DataFrame, lower_q: float = 0.25, upper_q: float = 0.75
) -> pd.DataFrame:
"""Returns a DataFrame of realized volatility quantiles.
Paramters
---------
data: pd.DataFrame
@@ -183,28 +182,28 @@ def rvol(
The lower quantile to calculate the realized volatility over time for.
upper_q: float (default = 0.75)
The upper quantile to calculate the realized volatility over time for.
Returns
-------
pd.DataFrame: rvol
DataFrame of realized volatility quantiles.
Examples
--------
df = rvol(symbol = "AAPL")
df = rvol(symbol = "AAPL", lower_q = 0.10, upper_q = 0.90)
"""
if lower_q > 1:
lower_q = lower_q/100
lower_q = lower_q / 100
if upper_q > 1:
upper_q = upper_q/100
lower_q_label = str((int(lower_q*100)))
upper_q_label = str((int(upper_q*100)))
upper_q = upper_q / 100
lower_q_label = str((int(lower_q * 100)))
upper_q_label = str((int(upper_q * 100)))
rvol_cones: pd.DataFrame = pd.DataFrame()
quantiles = [lower_q, upper_q]
windows = [3,10,30,60,90,120,150,180,210,240,300,360]
windows = [3, 10, 30, 60, 90, 120, 150, 180, 210, 240, 300, 360]
data = data
min_ = []
max_ = []
@@ -212,7 +211,7 @@ def rvol(
top_q = []
bottom_q = []
realized = []
data = data.sort_index(ascending = False)
data = data.sort_index(ascending=False)
def realized_vol(data, window=30):
"""Helper function for calculating realized volatility."""
@@ -220,24 +219,32 @@ def rvol(
log_return = (data["Close"] / data["Close"].shift(1)).apply(np.log)
return log_return.rolling(window=window, center=False).std() * np.sqrt(252)
for window in windows:
# Looping to build a dataframe with realized volatility over each window.
estimator = realized_vol(window=window,data=data)
# Looping to build a dataframe with realized volatility over each window.
estimator = realized_vol(window=window, data=data)
min_.append(estimator.min())
max_.append(estimator.max())
median.append(estimator.median())
top_q.append(estimator.quantile(quantiles[1]))
bottom_q.append(estimator.quantile(quantiles[0]))
realized.append(estimator[-1])
df = [realized, min_, bottom_q, median, top_q, max_]
pd.DataFrame(df).columns = windows
df_windows = list(windows)
df = pd.DataFrame(df, columns=df_windows)
df = df.rename(index = {0:'Realized', 1: 'Min', 2:'Lower 'f"{lower_q_label}"'%', 3:'Median', 4:'Upper 'f"{upper_q_label}"'%', 5: 'Max'})
df = df.rename(
index={
0: "Realized",
1: "Min",
2: "Lower " f"{lower_q_label}" "%",
3: "Median",
4: "Upper " f"{upper_q_label}" "%",
5: "Max",
}
)
rvol_cones = df.copy()
return rvol_cones.transpose()

53
openbb_terminal/common/technical_analysis/volatility_view.py
View File

@@ -367,18 +367,19 @@ def display_atr(
sheet_name,
)
@log_start_end(log=logger)
def display_rvol(
data: pd.DataFrame,
symbol: str = "",
lower_q:float = 0.25,
lower_q: float = 0.25,
upper_q: float = 0.75,
export: str = "",
sheet_name: Optional[str] = None,
external_axes: Optional[List[plt.Axes]] = None
external_axes: Optional[List[plt.Axes]] = None,
):
"""Plots the realized volatility quantiles for the loaded ticker.
Parameters
----------
data: pd.DataFrame
@@ -400,34 +401,46 @@ def display_rvol(
--------
df_ta = openbb.stocks.load('XLY')
openbb.ta.rvol_chart(data = df_ta, symbol = 'XLY')
df_ta = openbb.stocks.load('XLE')
openbb.ta.rvol_chart(data = df_ta, symbol = "XLE", lower_q = 0.10, upper_q = 0.90)
"""
df_ta = volatility_model.rvol(data, lower_q = lower_q, upper_q = upper_q)
lower_q_label = str((int(lower_q*100)))
upper_q_label = str((int(upper_q*100)))
plt.figure(figsize = [14,7])
plt.autoscale(enable = True, axis = 'both', tight = True)
df_ta = volatility_model.rvol(data, lower_q=lower_q, upper_q=upper_q)
lower_q_label = str((int(lower_q * 100)))
upper_q_label = str((int(upper_q * 100)))
plt.figure(figsize=[14, 7])
plt.autoscale(enable=True, axis="both", tight=True)
plt.plot(df_ta.index, df_ta.Min, "-o", linewidth=1, label="Min")
plt.plot(df_ta.index, df_ta.Max, "-o", linewidth=1, label="Max")
plt.plot(df_ta.index, df_ta.Median, "-o", linewidth=1, label="Median")
plt.plot(df_ta.index, df_ta['Upper 'f"{upper_q_label}"'%'], "-o", linewidth=1, label = 'Upper 'f"{upper_q_label}"'%')
plt.plot(df_ta.index, df_ta['Lower 'f"{lower_q_label}"'%'], "-o", linewidth=1, label = 'Lower 'f"{lower_q_label}"'%')
plt.plot(
df_ta.index,
df_ta["Upper " f"{upper_q_label}" "%"],
"-o",
linewidth=1,
label="Upper " f"{upper_q_label}" "%",
)
plt.plot(
df_ta.index,
df_ta["Lower " f"{lower_q_label}" "%"],
"-o",
linewidth=1,
label="Lower " f"{lower_q_label}" "%",
)
plt.plot(df_ta.index, df_ta.Realized, "o-.", linewidth=1, label="Realized")
plt.ylabel(ylabel = 'Volatility', labelpad = -910, loc = 'top', rotation = 'horizontal')
plt.xlabel(xlabel = 'Window of Time (in days)', labelpad = 20, y = 0)
plt.title(label = 'Realized Volatility of 'f"{symbol}", loc= 'center', y = 1.0)
plt.legend(loc='best', ncol=6, fontsize = 'x-small')
plt.tick_params(axis='y', which='both', labelleft=False, labelright=True)
plt.ylabel(ylabel="Volatility", labelpad=-910, loc="top", rotation="horizontal")
plt.xlabel(xlabel="Window of Time (in days)", labelpad=20, y=0)
plt.title(label="Realized Volatility of " f"{symbol}", loc="center", y=1.0)
plt.legend(loc="best", ncol=6, fontsize="x-small")
plt.tick_params(axis="y", which="both", labelleft=False, labelright=True)
plt.xticks(df_ta.index)
plt.tight_layout(pad = 2.0)
plt.tight_layout(pad=2.0)
if external_axes is None:
theme.visualize_output()
export_data(
export,
os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),