def plot_oi(
ticker: str,
expiry: str,
min_sp: float,
max_sp: float,
calls_only: bool,
puts_only: bool,
export: str,
):
"""Plot open interest
Parameters
----------
ticker: str
Ticker
expiry: str
Expiry date for options
min_sp: float
Min strike to consider
max_sp: float
Max strike to consider
calls_only: bool
Show calls only
puts_only: bool
Show puts only
export: str
Format to export file
"""
options = yfinance_model.get_option_chain(ticker, expiry)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"oi_yf",
options,
)
calls = options.calls
puts = options.puts
current_price = float(yf.Ticker(ticker).info["regularMarketPrice"])
if min_sp == -1:
min_strike = 0.75 * current_price
else:
min_strike = min_sp
if max_sp == -1:
max_strike = 1.25 * current_price
else:
max_strike = max_sp
if calls_only and puts_only:
print("Both flags selected, please select one", "\n")
return
call_oi = calls.set_index("strike")["openInterest"] / 1000
put_oi = puts.set_index("strike")["openInterest"] / 1000
df_opt = pd.merge(call_oi, put_oi, left_index=True, right_index=True)
df_opt = df_opt.rename(columns={
"openInterest_x": "OI_call",
"openInterest_y": "OI_put"
})
max_pain = op_helpers.calculate_max_pain(df_opt)
plt.style.use("classic")
fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)
if not calls_only:
put_oi.plot(
x="strike",
y="openInterest",
label="Puts",
ax=ax,
marker="o",
ls="-",
c="r",
)
if not puts_only:
call_oi.plot(
x="strike",
y="openInterest",
label="Calls",
ax=ax,
marker="o",
ls="-",
c="g",
)
ax.axvline(current_price,
lw=2,
c="k",
ls="--",
label="Current Price",
alpha=0.7)
ax.axvline(max_pain,
lw=3,
c="k",
label=f"Max Pain: {max_pain}",
alpha=0.7)
ax.grid("on")
ax.set_xlabel("Strike Price")
ax.set_ylabel("Open Interest (1k) ")
ax.set_xlim(min_strike, max_strike)
if gtff.USE_ION:
plt.ion()
ax.set_title(f"Open Interest for {ticker.upper()} expiring {expiry}")
plt.legend(loc=0)
fig.tight_layout(pad=1)
plt.show()
plt.style.use("default")
print("")
def plot_volume_open_interest(
ticker: str,
expiry: str,
min_sp: float,
max_sp: float,
min_vol: float,
export: str,
):
"""Plot volume and open interest
Parameters
----------
ticker: str
Stock ticker
expiry: str
Option expiration
min_sp: float
Min strike price
max_sp: float
Max strike price
min_vol: float
Min volume to consider
export: str
Format for exporting data
"""
options = yfinance_model.get_option_chain(ticker, expiry)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"voi_yf",
options,
)
calls = options.calls
puts = options.puts
current_price = float(yf.Ticker(ticker).info["regularMarketPrice"])
# Process Calls Data
df_calls = calls.pivot_table(index="strike",
values=["volume", "openInterest"],
aggfunc="sum").reindex()
df_calls["strike"] = df_calls.index
df_calls["type"] = "calls"
df_calls["openInterest"] = df_calls["openInterest"]
df_calls["volume"] = df_calls["volume"]
df_calls["oi+v"] = df_calls["openInterest"] + df_calls["volume"]
df_calls["spot"] = round(current_price, 2)
df_puts = puts.pivot_table(index="strike",
values=["volume", "openInterest"],
aggfunc="sum").reindex()
df_puts["strike"] = df_puts.index
df_puts["type"] = "puts"
df_puts["openInterest"] = df_puts["openInterest"]
df_puts["volume"] = -df_puts["volume"]
df_puts["openInterest"] = -df_puts["openInterest"]
df_puts["oi+v"] = df_puts["openInterest"] + df_puts["volume"]
df_puts["spot"] = round(current_price, 2)
call_oi = calls.set_index("strike")["openInterest"] / 1000
put_oi = puts.set_index("strike")["openInterest"] / 1000
df_opt = pd.merge(call_oi, put_oi, left_index=True, right_index=True)
df_opt = df_opt.rename(columns={
"openInterest_x": "OI_call",
"openInterest_y": "OI_put"
})
max_pain = op_helpers.calculate_max_pain(df_opt)
if min_vol == -1 and min_sp == -1 and max_sp == -1:
# If no argument provided, we use the percentile 50 to get 50% of upper volume data
volume_percentile_threshold = 50
min_vol_calls = np.percentile(df_calls["oi+v"],
volume_percentile_threshold)
min_vol_puts = np.percentile(df_puts["oi+v"],
volume_percentile_threshold)
df_calls = df_calls[df_calls["oi+v"] > min_vol_calls]
df_puts = df_puts[df_puts["oi+v"] < min_vol_puts]
else:
if min_vol > -1:
df_calls = df_calls[df_calls["oi+v"] > min_vol]
df_puts = df_puts[df_puts["oi+v"] < -min_vol]
if min_sp > -1:
df_calls = df_calls[df_calls["strike"] > min_sp]
df_puts = df_puts[df_puts["strike"] > min_sp]
if max_sp > -1:
df_calls = df_calls[df_calls["strike"] < max_sp]
df_puts = df_puts[df_puts["strike"] < max_sp]
if df_calls.empty and df_puts.empty:
print(
"The filtering applied is too strong, there is no data available for such conditions.\n"
)
return
# Initialize the matplotlib figure
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)
# make x axis symmetric
axis_origin = max(abs(max(df_puts["oi+v"])), abs(max(df_calls["oi+v"])))
ax.set_xlim(-axis_origin, +axis_origin)
sns.set_style(style="darkgrid")
g = sns.barplot(
x="oi+v",
y="strike",
data=df_calls,
label="Calls: Open Interest",
color="lightgreen",
orient="h",
)
g = sns.barplot(
x="volume",
y="strike",
data=df_calls,
label="Calls: Volume",
color="green",
orient="h",
)
g = sns.barplot(
x="oi+v",
y="strike",
data=df_puts,
label="Puts: Open Interest",
color="pink",
orient="h",
)
g = sns.barplot(
x="volume",
y="strike",
data=df_puts,
label="Puts: Volume",
color="red",
orient="h",
)
# draw spot line
s = [float(strike.get_text()) for strike in ax.get_yticklabels()]
spot_index = bisect_left(
s, current_price) # find where the spot is on the graph
spot_line = ax.axhline(spot_index, ls="--", color="dodgerblue", alpha=0.3)
# draw max pain line
max_pain_index = bisect_left(s, max_pain)
max_pain_line = ax.axhline(max_pain_index,
ls="-",
color="black",
alpha=0.3)
max_pain_line.set_linewidth(3)
# format ticklabels without - for puts
g.set_xticks(g.get_xticks())
xlabels = [f"{x:,.0f}".replace("-", "") for x in g.get_xticks()]
g.set_xticklabels(xlabels)
plt.title(
f"{ticker} volumes for {expiry} (open interest displayed only during market hours)"
)
ax.invert_yaxis()
_ = ax.legend()
handles, _ = ax.get_legend_handles_labels()
handles.append(spot_line)
handles.append(max_pain_line)
# create legend labels + add to graph
labels = [
"Calls open interest",
"Calls volume ",
"Puts open interest",
"Puts volume",
"Current stock price",
f"Max pain = {max_pain}",
]
plt.legend(handles=handles[:], labels=labels)
sns.despine(left=True, bottom=True)
if gtff.USE_ION:
plt.ion()
plt.show()
plt.style.use("default")
print("")