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hotfix/fix-technical-output: Include Dates in Output (#5754)

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* fixes output to include dates

* ichimoku

* add trading_periods param to cones

* add param to integration tests

---------

Co-authored-by: Igor Radovanovic <74266147+IgorWounds@users.noreply.github.com>
This commit is contained in:
Danglewood
2023-11-22 13:09:55 -08:00
committed by GitHub
parent 4400cd9a8f
commit 5cc5cce263
4 changed files with 66 additions and 31 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
openbb_platform/extensions/technical/integration/test_technical_api.py
View File

@@ -889,6 +889,7 @@ def test_technical_cg(params, data_type):
"upper_q": "",
"model": "",
"is_crypto": "",
"trading_periods": "",
},
"equity",
),
@@ -899,7 +900,8 @@ def test_technical_cg(params, data_type):
"lower_q": "0.3",
"upper_q": "0.7",
"model": "Parkinson",
"is_crypto": "true",
"is_crypto": "True",
"trading_periods": "",
},
"crypto",
),

4
openbb_platform/extensions/technical/integration/test_technical_python.py
View File

@@ -832,6 +832,7 @@ def test_technical_cg(params, data_type, obb):
"upper_q": "",
"model": "",
"is_crypto": "",
"trading_periods": "",
},
"stocks",
),
@@ -842,7 +843,8 @@ def test_technical_cg(params, data_type, obb):
"lower_q": "0.3",
"upper_q": "0.7",
"model": "Parkinson",
"is_crypto": "true",
"is_crypto": "True",
"trading_periods": "",
},
"crypto",
),

6
openbb_platform/extensions/technical/openbb_technical/helpers.py
View File

@@ -378,6 +378,7 @@ def calculate_cones(
"Rogers-Satchell",
"Yang-Zhang",
],
trading_periods: Optional[int] = None,
) -> pd.DataFrame:
"""Calculate Cones."""
estimator = pd.DataFrame()
@@ -412,7 +413,10 @@ def calculate_cones(
for window in windows:
estimator = model_functions[model]( # type: ignore
window=window, data=data, is_crypto=is_crypto
window=window,
data=data,
is_crypto=is_crypto,
trading_periods=trading_periods,
)
if estimator.empty:

83
openbb_platform/extensions/technical/openbb_technical/technical_router.py
View File

@@ -69,7 +69,8 @@ def atr(
df_target.ta.atr(length=length, mamode=mamode, drift=drift, offset=offset)
)
results = df_to_basemodel(df_target.join(df_atr, how="left"), index=True)
output = pd.concat([df, df_atr], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -174,7 +175,8 @@ def obv(
df_target = get_target_columns(df, ["close", "volume"])
df_obv = pd.DataFrame(df_target.ta.obv(offset=offset))
results = df_to_basemodel(df_target.join(df_obv, how="left"), index=True)
output = pd.concat([df, df_obv], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -220,7 +222,8 @@ def fisher(
df_target = get_target_columns(df, ["high", "low"])
df_fisher = pd.DataFrame(df_target.ta.fisher(length=length, signal=signal))
results = df_to_basemodel(df_target.join(df_fisher, how="left"), index=True)
output = pd.concat([df, df_fisher], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -269,7 +272,8 @@ def adosc(
df_target = get_target_columns(df, ["high", "low", "close", "volume", "open"])
df_adosc = pd.DataFrame(df_target.ta.adosc(fast=fast, slow=slow, offset=offset))
results = df_to_basemodel(df_target.join(df_adosc, how="left"), index=True)
output = pd.concat([df, df_adosc], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -342,7 +346,8 @@ def bbands(
)
)
results = df_to_basemodel(df_target.join(bbands_df, how="left"), index=True)
output = pd.concat([df, bbands_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -399,7 +404,8 @@ def zlma(
)
).dropna()
results = df_to_basemodel(df_target.join(zlma_df, how="left"), index=True)
output = pd.concat([df, zlma_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -451,7 +457,8 @@ def aroon(
df_target = get_target_columns(df, ["high", "low", "close"])
aroon_df = pd.DataFrame(df_target.ta.aroon(length=length, scalar=scalar)).dropna()
results = df_to_basemodel(df_target.join(aroon_df, how="left"), index=True)
output = pd.concat([df, aroon_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -509,7 +516,8 @@ def sma(
).dropna()
)
results = df_to_basemodel(df_target.join(sma_df, how="left"), index=True)
output = pd.concat([df, sma_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -602,7 +610,8 @@ def vwap(
df_target = get_target_columns(df, ["high", "low", "close", "volume"])
df_vwap = pd.DataFrame(df_target.ta.vwap(anchor=anchor, offset=offset).dropna())
results = df_to_basemodel(df_target.join(df_vwap, how="left"), index=True)
output = pd.concat([df, df_vwap], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -664,8 +673,8 @@ def macd(
prefix=target,
).dropna()
)
results = df_to_basemodel(df_target.join(macd_df, how="left"), index=True)
output = pd.concat([df, macd_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -720,7 +729,8 @@ def hma(
).dropna()
)
results = df_to_basemodel(df_target.join(hma_df, how="left"), index=True)
output = pd.concat([df, hma_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -773,7 +783,8 @@ def donchian(
).dropna()
)
results = df_to_basemodel(df_target.join(donchian_df, how="left"), index=True)
output = pd.concat([df, donchian_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -823,10 +834,10 @@ def ichimoku(
lookahead=lookahead,
)
df_result = df_target.join(df_span.add_prefix("span_"), how="left")
df_result = df.join(df_span.add_prefix("span_"), how="left")
df_result = df_result.join(df_ichimoku, how="left")
results = df_to_basemodel(df_result, index=True)
results = df_to_basemodel(df_result.reset_index())
return OBBject(results=results)
@@ -876,7 +887,8 @@ def clenow(
orient="index",
).transpose()
results = df_to_basemodel(df_clenow)
output = pd.concat([df, df_clenow], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -921,7 +933,8 @@ def ad(data: List[Data], index: str = "date", offset: int = 0) -> OBBject[List[D
df_target = get_target_columns(df, ["high", "low", "close", "volume"])
ad_df = pd.DataFrame(df_target.ta.ad(offset=offset).dropna())
results = df_to_basemodel(df_target.join(ad_df, how="left"), index=True)
output = pd.concat([df, ad_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -970,7 +983,8 @@ def adx(
df_target.ta.adx(length=length, scalar=scalar, drift=drift).dropna()
)
results = df_to_basemodel(df_target.join(adx_df, how="left"), index=True)
output = pd.concat([df, adx_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -1025,7 +1039,8 @@ def wma(
).dropna()
)
results = df_to_basemodel(df_target.join(wma_df, how="left"), index=True)
output = pd.concat([df, wma_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -1065,7 +1080,8 @@ def cci(
df_target = get_target_columns(df, ["close", "high", "low"])
cci_df = pd.DataFrame(df_target.ta.cci(length=length, scalar=scalar).dropna())
results = df_to_basemodel(df_target.join(cci_df, how="left"), index=True)
output = pd.concat([df, cci_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -1125,7 +1141,8 @@ def rsi(
).dropna()
)
results = df_to_basemodel(df_target.join(rsi_df, how="left"), index=True)
output = pd.concat([df, rsi_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -1181,7 +1198,8 @@ def stoch(
).dropna()
)
results = df_to_basemodel(df_target.join(stoch_df, how="left"), index=True)
output = pd.concat([df, stoch_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -1239,8 +1257,8 @@ def kc(
offset=offset,
).dropna()
)
results = df_to_basemodel(df_target.join(kc_df, how="left"), index=True)
output = pd.concat([df, kc_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -1281,7 +1299,8 @@ def cg(
df_target = get_target_columns(df, ["high", "low", "close"])
cg_df = pd.DataFrame(df_target.ta.cg(length=length).dropna())
results = df_to_basemodel(df_target.join(cg_df, how="left"), index=True)
output = pd.concat([df, cg_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)
@@ -1301,6 +1320,7 @@ def cones(
"Yang-Zhang",
] = "STD",
is_crypto: bool = False,
trading_periods: Optional[int] = None,
) -> OBBject[List[Data]]:
"""Calculate the realized volatility quantiles over rolling windows of time.
@@ -1340,7 +1360,8 @@ def cones(
It is a weighted average of the Rogers-Satchell volatility and the open-to-close volatility.
is_crypto : bool, optional
Whether the data is crypto or not. If True, volatility is calculated for 365 days instead of 252
trading_periods : Optional[int] [default: 252]
Number of trading periods in a year.
Returns
-------
OBBject[List[Data]]
@@ -1357,7 +1378,12 @@ def cones(
df = basemodel_to_df(data, index=index)
df_cones = helpers.calculate_cones(
data=df, lower_q=lower_q, upper_q=upper_q, model=model, is_crypto=is_crypto
data=df,
lower_q=lower_q,
upper_q=upper_q,
model=model,
is_crypto=is_crypto,
trading_periods=trading_periods,
)
results = df_to_basemodel(df_cones)
@@ -1413,6 +1439,7 @@ def ema(
).dropna()
)
results = df_to_basemodel(df_target.join(ema_df, how="left"), index=True)
output = pd.concat([df, ema_df], axis=1)
results = df_to_basemodel(output.reset_index())
return OBBject(results=results)