def test_get_pattern_recognition_invalid_status(mocker):
# MOCK RESPONSE
mock_response = requests.Response()
mock_response.status_code = 400
mocker.patch(target="requests.get", new=mocker.Mock(return_value=mock_response))
result = finnhub_model.get_pattern_recognition(ticker="PM", resolution="D")
assert result.empty
def test_get_pattern_recognition(recorder, mocker):
# MOCK RESPONSE
mock_response = requests.Response()
mock_response.status_code = 200
mocker.patch.object(
target=mock_response,
attribute="json",
side_effect=lambda: json.loads(RESPONSE_SAMPLE),
)
mocker.patch(target="requests.get", new=mocker.Mock(return_value=mock_response))
result_df = finnhub_model.get_pattern_recognition(ticker="PM", resolution="D")
recorder.capture(result_df)
def test_get_pattern_recognition_invalid_json(mocker):
# MOCK RESPONSE
mock_response = requests.Response()
mock_response.status_code = 200
mocker.patch.object(
target=mock_response,
attribute="json",
side_effect=lambda: dict(),
)
mocker.patch(target="requests.get", new=mocker.Mock(return_value=mock_response))
result = finnhub_model.get_pattern_recognition(ticker="PM", resolution="D")
assert result.empty
def test_get_pattern_recognition_invalid_status(mocker):
# MOCK RESPONSE
attrs = {
"status_code": 400,
"json.return_value": {
"error": "mock error message"
},
}
mock_response = mocker.Mock(**attrs)
mocker.patch(target="requests.get",
new=mocker.Mock(return_value=mock_response))
result = finnhub_model.get_pattern_recognition(ticker="PM", resolution="D")
assert result.empty
def plot_pattern_recognition(ticker: str, resolution: str, export: str):
"""Plot pattern recognition signal
Parameters
----------
ticker : str
Ticker to display pattern recognition on top of the data
resolution : str
Resolution of data to get pattern recognition from
export: str
Format of export file
"""
pattern = finnhub_model.get_pattern_recognition(ticker, resolution)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"pr",
pattern,
)
l_segments = []
for i in pattern:
a_part = ("", "")
if "aprice" in pattern[i]:
if pattern[i]["aprice"] != 0 and not math.isnan(
pattern[i]["aprice"]):
a_part = (
datetime.utcfromtimestamp(
pattern[i]["atime"]).strftime("%Y-%m-%d"),
pattern[i]["aprice"],
)
b_part = ("", "")
if "bprice" in pattern[i]:
if pattern[i]["bprice"] != 0 and not math.isnan(
pattern[i]["bprice"]):
b_part = (
datetime.utcfromtimestamp(
pattern[i]["btime"]).strftime("%Y-%m-%d"),
pattern[i]["bprice"],
)
c_part = ("", "")
if "cprice" in pattern[i]:
if pattern[i]["cprice"] != 0 and not math.isnan(
pattern[i]["cprice"]):
c_part = (
datetime.utcfromtimestamp(
pattern[i]["ctime"]).strftime("%Y-%m-%d"),
pattern[i]["cprice"],
)
d_part = ("", "")
if "dprice" in pattern[i]:
if pattern[i]["dprice"] != 0 and not math.isnan(
pattern[i]["dprice"]):
d_part = (
datetime.utcfromtimestamp(
pattern[i]["dtime"]).strftime("%Y-%m-%d"),
pattern[i]["dprice"],
)
segment = (a_part, b_part, c_part, d_part)
l_segment = list(segment)
while ("", "") in l_segment:
l_segment.remove(("", ""))
segm = tuple(l_segment)
l_segments.append(segm)
start_time = 999999999999
for i in pattern:
if pattern[i]["atime"] < start_time:
start_time = pattern[i]["atime"]
df_stock = yf.download(
ticker,
start=datetime.utcfromtimestamp(start_time).strftime("%Y-%m-%d"),
progress=False,
)
df_stock["date_id"] = (df_stock.index.date -
df_stock.index.date.min()).astype("timedelta64[D]")
df_stock["date_id"] = df_stock["date_id"].dt.days + 1
df_stock["OC_High"] = df_stock[["Open", "Close"]].max(axis=1)
df_stock["OC_Low"] = df_stock[["Open", "Close"]].min(axis=1)
mc = mpf.make_marketcolors(up="green",
down="red",
edge="black",
wick="black",
volume="in",
ohlc="i")
s = mpf.make_mpf_style(marketcolors=mc, gridstyle=":", y_on_right=False)
mpf.plot(
df_stock,
type="candle",
volume=False,
title=f"\n{ticker}",
alines=l_segments,
xrotation=10,
style=s,
figratio=(10, 7),
figscale=1.10,
figsize=plot_autoscale(),
update_width_config=dict(candle_linewidth=1.0,
candle_width=0.8,
volume_linewidth=1.0),
)
for ix in range(len(pattern.columns)):
print(f"From {l_segments[ix][0][0]} to {l_segments[ix][-1][0]}")
print(
f"Pattern: {pattern[0]['patternname']} ({pattern[0]['patterntype']})",
"\n")
def plot_pattern_recognition(
ticker: str,
resolution: str,
export: str,
external_axes: Optional[List[plt.Axes]] = None,
):
"""Plot pattern recognition signal
Parameters
----------
ticker : str
Ticker to display pattern recognition on top of the data
resolution : str
Resolution of data to get pattern recognition from
export: str
Format of export file
external_axes : Optional[List[plt.Axes]], optional
External axes (1 axis is expected in the list), by default None
"""
pattern = finnhub_model.get_pattern_recognition(ticker, resolution)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"pr",
pattern,
)
l_segments = []
for i in pattern:
a_part = ("", "")
if "aprice" in pattern[i]:
if pattern[i]["aprice"] != 0 and not math.isnan(
pattern[i]["aprice"]):
a_part = (
datetime.utcfromtimestamp(
pattern[i]["atime"]).strftime("%Y-%m-%d"),
pattern[i]["aprice"],
)
b_part = ("", "")
if "bprice" in pattern[i]:
if pattern[i]["bprice"] != 0 and not math.isnan(
pattern[i]["bprice"]):
b_part = (
datetime.utcfromtimestamp(
pattern[i]["btime"]).strftime("%Y-%m-%d"),
pattern[i]["bprice"],
)
c_part = ("", "")
if "cprice" in pattern[i]:
if pattern[i]["cprice"] != 0 and not math.isnan(
pattern[i]["cprice"]):
c_part = (
datetime.utcfromtimestamp(
pattern[i]["ctime"]).strftime("%Y-%m-%d"),
pattern[i]["cprice"],
)
d_part = ("", "")
if "dprice" in pattern[i]:
if pattern[i]["dprice"] != 0 and not math.isnan(
pattern[i]["dprice"]):
d_part = (
datetime.utcfromtimestamp(
pattern[i]["dtime"]).strftime("%Y-%m-%d"),
pattern[i]["dprice"],
)
segment = (a_part, b_part, c_part, d_part)
l_segment = list(segment)
while ("", "") in l_segment:
l_segment.remove(("", ""))
segm = tuple(l_segment)
l_segments.append(segm)
start_time = 999999999999
for i in pattern:
if pattern[i]["atime"] < start_time:
start_time = pattern[i]["atime"]
df_stock = yf.download(
ticker,
start=datetime.utcfromtimestamp(start_time).strftime("%Y-%m-%d"),
progress=False,
)
df_stock["date_id"] = (df_stock.index.date -
df_stock.index.date.min()).astype("timedelta64[D]")
df_stock["date_id"] = df_stock["date_id"].dt.days + 1
df_stock["OC_High"] = df_stock[["Open", "Close"]].max(axis=1)
df_stock["OC_Low"] = df_stock[["Open", "Close"]].min(axis=1)
candle_chart_kwargs = {
"type": "candle",
"style": theme.mpf_style,
"volume": False,
"alines": l_segments,
"xrotation": theme.xticks_rotation,
"scale_padding": {
"left": 0.3,
"right": 1,
"top": 0.8,
"bottom": 0.8
},
"update_width_config": {
"candle_linewidth": 0.6,
"candle_width": 0.8,
"volume_linewidth": 0.8,
"volume_width": 0.8,
},
"warn_too_much_data": 10000,
}
# This plot has 2 axes
if not external_axes:
candle_chart_kwargs["returnfig"] = True
candle_chart_kwargs["figratio"] = (10, 7)
candle_chart_kwargs["figscale"] = 1.10
candle_chart_kwargs["figsize"] = plot_autoscale()
(fig, ax) = mpf.plot(df_stock, **candle_chart_kwargs)
fig.suptitle(
f"\n{ticker}",
x=0.055,
y=0.965,
horizontalalignment="left",
)
theme.visualize_output(force_tight_layout=False)
else:
if len(external_axes) != 1:
logger.error("Expected list of one axis item.")
console.print("[red]Expected list of 1 axis items./n[/red]")
return
(ax, ) = external_axes
candle_chart_kwargs["ax"] = ax
mpf.plot(df_stock, **candle_chart_kwargs)
for ix in range(len(pattern.columns)):
console.print(
f"From {l_segments[ix][0][0]} to {l_segments[ix][-1][0]}")
console.print(
f"Pattern: {pattern[0]['patternname']} ({pattern[0]['patterntype']})",
"\n")
