diff --git a/R/run_RTL_examples.R b/R/run_RTL_examples.R
index af19aad..0f170c9 100644
--- a/R/run_RTL_examples.R
+++ b/R/run_RTL_examples.R
@@ -195,7 +195,9 @@ x <- x %>%
dplyr::group_by(series)
x <- RTL::returns(df = x, retType = "abs", period.return = 1, spread = TRUE)
x <- RTL::rolladjust(x = x, commodityname = c("cmewti"), rolltype = c("Last.Trade"))
-x <- x %>% dplyr::filter(!grepl("2020-04-20|2020-04-21", date))
+x <- x %>%
+ dplyr::filter(!grepl("2020-04-20|2020-04-21", date)) %>%
+ filter((date>='2010-01-04') & (date <= '2022-12-30'))
ou <- promptBeta(x = x, period = "all", betatype = "all", output = "betas")
write(jsonlite::toJSON(ou), "promptBeta.json")
diff --git a/notebooks/scratchpad.ipynb b/notebooks/scratchpad.ipynb
index 02b59b5..6605d1c 100644
--- a/notebooks/scratchpad.ipynb
+++ b/notebooks/scratchpad.ipynb
@@ -442,7 +442,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 77,
"metadata": {},
"outputs": [
{
@@ -480,27 +480,27 @@
" \n",
" \n",
" | 1 | \n",
- " 0.9508 | \n",
- " 0.8923 | \n",
- " 0.9765 | \n",
+ " 0.9665 | \n",
+ " 0.9518 | \n",
+ " 0.9727 | \n",
"
\n",
" \n",
" | 2 | \n",
- " 0.9223 | \n",
- " 0.8599 | \n",
- " 0.9585 | \n",
+ " 0.9300 | \n",
+ " 0.9012 | \n",
+ " 0.9436 | \n",
"
\n",
" \n",
" | 3 | \n",
- " 0.8971 | \n",
- " 0.8334 | \n",
- " 0.9399 | \n",
+ " 0.8943 | \n",
+ " 0.8512 | \n",
+ " 0.9137 | \n",
"
\n",
" \n",
" | 4 | \n",
- " 0.8746 | \n",
- " 0.8110 | \n",
- " 0.9230 | \n",
+ " 0.8612 | \n",
+ " 0.8057 | \n",
+ " 0.8858 | \n",
"
\n",
" \n",
"\n",
@@ -509,13 +509,13 @@
"text/plain": [
" all bull bear\n",
"0 1.0000 1.0000 1.0000\n",
- "1 0.9508 0.8923 0.9765\n",
- "2 0.9223 0.8599 0.9585\n",
- "3 0.8971 0.8334 0.9399\n",
- "4 0.8746 0.8110 0.9230"
+ "1 0.9665 0.9518 0.9727\n",
+ "2 0.9300 0.9012 0.9436\n",
+ "3 0.8943 0.8512 0.9137\n",
+ "4 0.8612 0.8057 0.8858"
]
},
- "execution_count": 46,
+ "execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
@@ -526,10 +526,12 @@
"dfwide = rt.data.open_data(\"dflong\").unstack(0)\n",
"col_mask = dfwide.columns[dfwide.columns.str.contains(\"CL\")]\n",
"dfwide = dfwide[col_mask]\n",
- "dfwide = dfwide[~dfwide.index.isin([\"2020-04-20\", \"2020-04-21\"])]\n",
- "dfwide = dfwide.loc['2004-01-02':'2022-12-15',:]\n",
+ "dfwide = dfwide.dropna(thresh=10, axis=0)\n",
+ "\n",
"x = rt.returns(df=dfwide, ret_type=\"abs\", period_return=1)\n",
"x = rt.roll_adjust(df=x, commodity_name=\"cmewti\", roll_type=\"Last_Trade\")\n",
+ "x = x[~x.index.isin([\"2020-04-20\", \"2020-04-21\"])]\n",
+ "x = x.loc['2010-01-04':'2022-12-30',:]\n",
"\n",
"ts = (\n",
" rt.prompt_beta(df=x, period=\"all\", beta_type=\"all\", output=\"betas\")\n",
diff --git a/pytest/data/promptBeta.json b/pytest/data/promptBeta.json
index 038920a..149d199 100644
--- a/pytest/data/promptBeta.json
+++ b/pytest/data/promptBeta.json
@@ -1 +1 @@
-[{"all":1,"bull":1,"bear":1,"contract":1},{"all":0.9506,"bull":0.8923,"bear":0.9778,"contract":2},{"all":0.921,"bull":0.8597,"bear":0.9574,"contract":3},{"all":0.8953,"bull":0.8327,"bear":0.9387,"contract":4},{"all":0.8724,"bull":0.8099,"bear":0.9213,"contract":5},{"all":0.8521,"bull":0.7902,"bear":0.9057,"contract":6},{"all":0.8331,"bull":0.7718,"bear":0.8913,"contract":7},{"all":0.8154,"bull":0.755,"bear":0.8778,"contract":8},{"all":0.7984,"bull":0.7391,"bear":0.8646,"contract":9},{"all":0.7824,"bull":0.7242,"bear":0.8518,"contract":10},{"all":0.767,"bull":0.7101,"bear":0.8393,"contract":11},{"all":0.7527,"bull":0.6966,"bear":0.8276,"contract":12},{"all":0.7392,"bull":0.6841,"bear":0.8166,"contract":13},{"all":0.7265,"bull":0.6725,"bear":0.8058,"contract":14},{"all":0.7143,"bull":0.6613,"bear":0.7955,"contract":15},{"all":0.7024,"bull":0.6503,"bear":0.7853,"contract":16},{"all":0.6909,"bull":0.6399,"bear":0.7752,"contract":17},{"all":0.6798,"bull":0.6296,"bear":0.7652,"contract":18},{"all":0.6689,"bull":0.6198,"bear":0.7553,"contract":19},{"all":0.6583,"bull":0.6105,"bear":0.7451,"contract":20},{"all":0.6481,"bull":0.6018,"bear":0.7353,"contract":21},{"all":0.6383,"bull":0.5932,"bear":0.7257,"contract":22},{"all":0.6289,"bull":0.5849,"bear":0.7165,"contract":23},{"all":0.6199,"bull":0.577,"bear":0.7078,"contract":24},{"all":0.6112,"bull":0.5694,"bear":0.6994,"contract":25},{"all":0.6029,"bull":0.562,"bear":0.6916,"contract":26},{"all":0.5949,"bull":0.555,"bear":0.684,"contract":27},{"all":0.5872,"bull":0.5481,"bear":0.6767,"contract":28},{"all":0.5806,"bull":0.5417,"bear":0.6702,"contract":29},{"all":0.5735,"bull":0.5358,"bear":0.6673,"contract":30},{"all":0.5666,"bull":0.5322,"bear":0.6611,"contract":31},{"all":0.56,"bull":0.5268,"bear":0.6556,"contract":32},{"all":0.5544,"bull":0.5197,"bear":0.6494,"contract":33},{"all":0.5486,"bull":0.516,"bear":0.6438,"contract":34},{"all":0.5432,"bull":0.5118,"bear":0.6362,"contract":35},{"all":0.5374,"bull":0.5086,"bear":0.6328,"contract":36}]
+[{"all":1,"bull":1,"bear":1,"contract":1},{"all":0.9665,"bull":0.9518,"bear":0.9727,"contract":2},{"all":0.93,"bull":0.9012,"bear":0.9436,"contract":3},{"all":0.8943,"bull":0.8512,"bear":0.9137,"contract":4},{"all":0.8612,"bull":0.8057,"bear":0.8858,"contract":5},{"all":0.831,"bull":0.7662,"bear":0.8594,"contract":6},{"all":0.8036,"bull":0.732,"bear":0.8355,"contract":7},{"all":0.779,"bull":0.703,"bear":0.8137,"contract":8},{"all":0.756,"bull":0.6773,"bear":0.7934,"contract":9},{"all":0.7349,"bull":0.6551,"bear":0.7743,"contract":10},{"all":0.7149,"bull":0.6344,"bear":0.7562,"contract":11},{"all":0.6966,"bull":0.6156,"bear":0.7393,"contract":12},{"all":0.6794,"bull":0.5983,"bear":0.7237,"contract":13},{"all":0.6634,"bull":0.5826,"bear":0.709,"contract":14},{"all":0.6479,"bull":0.5674,"bear":0.6946,"contract":15},{"all":0.6328,"bull":0.5528,"bear":0.6801,"contract":16},{"all":0.6181,"bull":0.539,"bear":0.6655,"contract":17},{"all":0.6038,"bull":0.5253,"bear":0.6513,"contract":18},{"all":0.59,"bull":0.5121,"bear":0.6375,"contract":19},{"all":0.5765,"bull":0.4995,"bear":0.6236,"contract":20},{"all":0.5635,"bull":0.4872,"bear":0.6103,"contract":21},{"all":0.551,"bull":0.4755,"bear":0.5973,"contract":22},{"all":0.5388,"bull":0.4639,"bear":0.5845,"contract":23},{"all":0.5272,"bull":0.4527,"bear":0.5724,"contract":24},{"all":0.5157,"bull":0.442,"bear":0.5604,"contract":25},{"all":0.5046,"bull":0.4311,"bear":0.549,"contract":26},{"all":0.4941,"bull":0.4214,"bear":0.5382,"contract":27},{"all":0.484,"bull":0.4115,"bear":0.5279,"contract":28},{"all":0.4743,"bull":0.4023,"bear":0.5184,"contract":29},{"all":0.4648,"bull":0.3934,"bear":0.5087,"contract":30},{"all":0.4558,"bull":0.3852,"bear":0.4991,"contract":31},{"all":0.4469,"bull":0.3772,"bear":0.4898,"contract":32},{"all":0.4386,"bull":0.37,"bear":0.4811,"contract":33},{"all":0.4306,"bull":0.3625,"bear":0.473,"contract":34},{"all":0.4235,"bull":0.3556,"bear":0.4671,"contract":35},{"all":0.4158,"bull":0.3484,"bear":0.4592,"contract":36}]
diff --git a/pytest/test_risktools.py b/pytest/test_risktools.py
index 4fce8e2..1f54bac 100644
--- a/pytest/test_risktools.py
+++ b/pytest/test_risktools.py
@@ -314,21 +314,23 @@ def test_garch():
def test_prompt_beta():
- ac = _load_json("./data/promptBeta.json").round(2).drop("contract", axis=1)
+ ac = _load_json("../pytest/data/promptBeta.json").round(4).drop("contract", axis=1)
dfwide = rt.data.open_data("dflong").unstack(0)
col_mask = dfwide.columns[dfwide.columns.str.contains("CL")]
dfwide = dfwide[col_mask]
- dfwide = dfwide[~dfwide.index.isin(["2020-04-20", "2020-04-21"])]
+ dfwide = dfwide.dropna(thresh=10, axis=0)
+
x = rt.returns(df=dfwide, ret_type="abs", period_return=1)
x = rt.roll_adjust(df=x, commodity_name="cmewti", roll_type="Last_Trade")
+ x = x[~x.index.isin(["2020-04-20", "2020-04-21"])]
+ x = x.loc['2010-01-04':'2022-12-30',:]
ts = (
rt.prompt_beta(df=x, period="all", beta_type="all", output="betas")
.round(4)
.reset_index(drop=True)
)
-
# for some reason the betas are slightly different using the Python sklearn
# LinearRegression model. Make sure that the max of the three columns
# are less than 0.03. Differences are on the order of 0.001 on any individual