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5c3862c297
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5c3862c297 | ||
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3559a90ad2 | ||
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File diff suppressed because one or more lines are too long
@ -68,7 +68,7 @@ pub fn get_period_indices_hv(dfw: PyDataFrame, est_freq: &str) -> PyResult<Vec<u
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cids,
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cids,
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weights = None,
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weights = None,
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signs = None,
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signs = None,
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weight_xcats = None,
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weight_xcat = None,
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normalize_weights = false,
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normalize_weights = false,
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start = None,
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start = None,
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end = None,
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end = None,
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@ -84,7 +84,7 @@ pub fn linear_composite(
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cids: Vec<String>,
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cids: Vec<String>,
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weights: Option<Vec<f64>>,
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weights: Option<Vec<f64>>,
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signs: Option<Vec<f64>>,
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signs: Option<Vec<f64>>,
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weight_xcats: Option<Vec<String>>,
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weight_xcat: Option<String>,
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normalize_weights: bool,
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normalize_weights: bool,
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start: Option<String>,
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start: Option<String>,
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end: Option<String>,
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end: Option<String>,
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@ -101,7 +101,7 @@ pub fn linear_composite(
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cids,
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cids,
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weights,
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weights,
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signs,
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signs,
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weight_xcats,
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weight_xcat,
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normalize_weights,
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normalize_weights,
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start,
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start,
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end,
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end,
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@ -1,6 +1,6 @@
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use crate::utils::dateutils::{get_bdates_from_col, get_min_max_real_dates};
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use crate::utils::dateutils::{get_bdates_from_col, get_min_max_real_dates};
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use crate::utils::qdf::pivots::*;
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use crate::utils::qdf::pivots::*;
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use crate::utils::qdf::reduce_df::*;
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use crate::utils::qdf::reduce_dataframe;
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use chrono::NaiveDate;
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use chrono::NaiveDate;
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use ndarray::{s, Array, Array1, Zip};
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use ndarray::{s, Array, Array1, Zip};
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use polars::prelude::*;
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use polars::prelude::*;
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@ -1,6 +1,6 @@
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use crate::utils::qdf::check_quantamental_dataframe;
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use crate::utils::qdf::check_quantamental_dataframe;
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use crate::utils::qdf::pivots::*;
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use crate::utils::qdf::pivots::{pivot_dataframe_by_ticker, pivot_wide_dataframe_to_qdf};
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use crate::utils::qdf::reduce_df::*;
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use crate::utils::qdf::reduce_df::reduce_dataframe;
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use polars::prelude::*;
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use polars::prelude::*;
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use std::collections::HashMap;
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use std::collections::HashMap;
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const TOLERANCE: f64 = 1e-8;
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const TOLERANCE: f64 = 1e-8;
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@ -108,14 +108,42 @@ fn _form_agg_nan_mask_series(nan_mask_dfw: &DataFrame) -> Result<Series, PolarsE
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Ok(combined.into_series())
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Ok(combined.into_series())
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}
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}
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/// Form the weights DataFrame
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fn _form_agg_weights_dfw(
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fn _form_agg_weights_dfw(
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agg_weights_map: &HashMap<String, Vec<f64>>,
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agg_weights_map: &HashMap<String, (WeightValue, f64)>,
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data_dfw: DataFrame,
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dfw: &DataFrame,
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) -> Result<DataFrame, PolarsError> {
|
) -> Result<DataFrame, PolarsError> {
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let mut weights_dfw = DataFrame::new(vec![])?;
|
let mut weights_dfw = DataFrame::new(vec![])?;
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for (agg_targ, weight_signs) in agg_weights_map.iter() {
|
for (agg_targ, weight_signs) in agg_weights_map.iter() {
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let wgt = weight_signs[0] * weight_signs[1];
|
// let wgt = weight_signs[0] * weight_signs[1];
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let wgt_series = Series::new(agg_targ.into(), vec![wgt; data_dfw.height()]);
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let wgt_series = match &weight_signs.0 {
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WeightValue::F64(val) => {
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|
let wgt = val * weight_signs.1;
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Series::new(agg_targ.into(), vec![wgt; dfw.height()])
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}
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|
WeightValue::Str(vstr) => {
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|
// vstr column from data_dfw, else raise wieght specification error
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|
if !dfw.get_column_names().contains(&&PlSmallStr::from(vstr)) {
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|
return Err(PolarsError::ComputeError(
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|
format!(
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|
"The column {} does not exist in the DataFrame. {:?}",
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|
vstr, agg_weights_map
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|
)
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|
.into(),
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));
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}
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|
let vstr_series = dfw.column(vstr)?;
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let multiplied_series = vstr_series * weight_signs.1;
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|
let mut multiplied_series =
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|
multiplied_series.as_series().cloned().ok_or_else(|| {
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|
PolarsError::ComputeError(
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||||||
|
"Failed to convert multiplied_series to Series".into(),
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|
)
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|
})?;
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|
multiplied_series.rename(agg_targ.into());
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|
multiplied_series
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|
}
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};
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weights_dfw.with_column(wgt_series)?;
|
weights_dfw.with_column(wgt_series)?;
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}
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}
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Ok(weights_dfw)
|
Ok(weights_dfw)
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@ -143,14 +171,14 @@ fn perform_single_group_agg(
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dfw: &DataFrame,
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dfw: &DataFrame,
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agg_on: &String,
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agg_on: &String,
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agg_targs: &Vec<String>,
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agg_targs: &Vec<String>,
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agg_weights_map: &HashMap<String, Vec<f64>>,
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agg_weights_map: &HashMap<String, (WeightValue, f64)>,
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normalize_weights: bool,
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normalize_weights: bool,
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complete: bool,
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complete: bool,
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) -> Result<Column, PolarsError> {
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) -> Result<Column, PolarsError> {
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let data_dfw = _form_agg_data_dfw(dfw, agg_targs)?;
|
let data_dfw = _form_agg_data_dfw(dfw, agg_targs)?;
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let nan_mask_dfw = _form_agg_nan_mask_dfw(&data_dfw)?;
|
let nan_mask_dfw = _form_agg_nan_mask_dfw(&data_dfw)?;
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let nan_mask_series = _form_agg_nan_mask_series(&nan_mask_dfw)?;
|
let nan_mask_series = _form_agg_nan_mask_series(&nan_mask_dfw)?;
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let weights_dfw = _form_agg_weights_dfw(agg_weights_map, data_dfw.clone())?;
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let weights_dfw = _form_agg_weights_dfw(agg_weights_map, dfw)?;
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let weights_dfw = match normalize_weights {
|
let weights_dfw = match normalize_weights {
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true => normalize_weights_with_nan_mask(weights_dfw, nan_mask_dfw)?,
|
true => normalize_weights_with_nan_mask(weights_dfw, nan_mask_dfw)?,
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false => weights_dfw,
|
false => weights_dfw,
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@ -192,7 +220,7 @@ fn perform_single_group_agg(
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fn perform_multiplication(
|
fn perform_multiplication(
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dfw: &DataFrame,
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dfw: &DataFrame,
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mult_targets: &HashMap<String, Vec<String>>,
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mult_targets: &HashMap<String, Vec<String>>,
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weights_map: &HashMap<String, HashMap<String, Vec<f64>>>,
|
weights_map: &HashMap<String, HashMap<String, (WeightValue, f64)>>,
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complete: bool,
|
complete: bool,
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normalize_weights: bool,
|
normalize_weights: bool,
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) -> Result<DataFrame, PolarsError> {
|
) -> Result<DataFrame, PolarsError> {
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@ -200,6 +228,7 @@ fn perform_multiplication(
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// let mut new_dfw = DataFrame::new(vec![real_date])?;
|
// let mut new_dfw = DataFrame::new(vec![real_date])?;
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let mut new_dfw = DataFrame::new(vec![])?;
|
let mut new_dfw = DataFrame::new(vec![])?;
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assert!(!mult_targets.is_empty(), "agg_targs is empty");
|
assert!(!mult_targets.is_empty(), "agg_targs is empty");
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|
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for (agg_on, agg_targs) in mult_targets.iter() {
|
for (agg_on, agg_targs) in mult_targets.iter() {
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// perform_single_group_agg
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// perform_single_group_agg
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let cols_len = new_dfw.get_column_names().len();
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let cols_len = new_dfw.get_column_names().len();
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@ -288,76 +317,122 @@ fn get_mul_targets(
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Ok(mul_targets)
|
Ok(mul_targets)
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}
|
}
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|
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|
/// Builds a map of the shape:
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|
/// `HashMap<String, HashMap<String, (WeightValue, f64)>>`
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|
/// where only one of `weights` or `weight_xcats` can be provided.
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|
/// If neither is provided, weights default to 1.0.
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|
/// Each tuple is `(WeightValue, f64) = (weight, sign)`.
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fn form_weights_and_signs_map(
|
fn form_weights_and_signs_map(
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cids: Vec<String>,
|
cids: Vec<String>,
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xcats: Vec<String>,
|
xcats: Vec<String>,
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weights: Option<Vec<f64>>,
|
weights: Option<Vec<f64>>,
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|
weight_xcat: Option<String>,
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signs: Option<Vec<f64>>,
|
signs: Option<Vec<f64>>,
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) -> Result<HashMap<String, HashMap<String, Vec<f64>>>, Box<dyn std::error::Error>> {
|
) -> Result<HashMap<String, HashMap<String, (WeightValue, f64)>>, Box<dyn std::error::Error>> {
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let _agg_xcats_for_cid = agg_xcats_for_cid(cids.clone(), xcats.clone());
|
// For demonstration, we pretend to load or infer these from helpers:
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|
let agg_xcats_for_cid = agg_xcats_for_cid(cids.clone(), xcats.clone());
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let (agg_on, agg_targ) = get_agg_on_agg_targs(cids.clone(), xcats.clone());
|
let (agg_on, agg_targ) = get_agg_on_agg_targs(cids.clone(), xcats.clone());
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|
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// if weights are None, create a vector of 1s of the same length as agg_targ
|
// Determine if each weight option has non-empty values.
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let weights = weights.unwrap_or(vec![1.0 / agg_targ.len() as f64; agg_targ.len()]);
|
let weights_provided = weights.as_ref().map_or(false, |v| !v.is_empty());
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let signs = signs.unwrap_or(vec![1.0; agg_targ.len()]);
|
let weight_xcats_provided = weight_xcat.as_ref().map_or(false, |v| !v.is_empty());
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|
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// check that the lengths of weights and signs match the length of agg_targ
|
// Enforce that only one of weights or weight_xcats is specified.
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check_weights_signs_lengths(
|
if weights_provided && weight_xcats_provided {
|
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weights.clone(),
|
return Err("Only one of `weights` and `weight_xcats` may be specified.".into());
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signs.clone(),
|
}
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_agg_xcats_for_cid,
|
|
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agg_targ.len(),
|
|
||||||
)?;
|
|
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|
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let mut weights_map = HashMap::new();
|
// 1) Build the "actual_weights" vector as WeightValue.
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|
let actual_weights: Vec<WeightValue> = if weights_provided {
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|
weights.unwrap().into_iter().map(WeightValue::F64).collect()
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|
} else if weight_xcats_provided {
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|
vec![WeightValue::Str(weight_xcat.unwrap()); agg_targ.len()]
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|
} else {
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|
// Default to numeric 1.0 if neither is provided
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|
vec![WeightValue::F64(1.0); agg_targ.len()]
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||||||
|
};
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||||||
|
|
||||||
|
// 2) Build the "signs" vector; default to 1.0 if not provided
|
||||||
|
let signs = signs.unwrap_or_else(|| vec![1.0; agg_targ.len()]);
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|
|
||||||
|
// 3) Optional: check lengths & zero values (only numeric weights).
|
||||||
|
check_weights_signs_lengths(&actual_weights, &signs, agg_xcats_for_cid, agg_targ.len())?;
|
||||||
|
|
||||||
|
// 4) Build the final nested HashMap
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||||||
|
let mut weights_map: HashMap<String, HashMap<String, (WeightValue, f64)>> = HashMap::new();
|
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|
|
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for agg_o in agg_on {
|
for agg_o in agg_on {
|
||||||
let mut agg_t_map = HashMap::new();
|
let mut agg_t_map = HashMap::new();
|
||||||
for (i, agg_t) in agg_targ.iter().enumerate() {
|
for (i, agg_t) in agg_targ.iter().enumerate() {
|
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let ticker = match _agg_xcats_for_cid {
|
// Format the ticker
|
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true => format!("{}_{}", agg_o, agg_t),
|
let ticker = if agg_xcats_for_cid {
|
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false => format!("{}_{}", agg_t, agg_o),
|
format!("{}_{}", agg_o, agg_t)
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|
} else {
|
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|
format!("{}_{}", agg_t, agg_o)
|
||||||
};
|
};
|
||||||
let weight_signs = vec![weights[i], signs[i]];
|
// Build the tuple (WeightValue, f64)
|
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agg_t_map.insert(ticker, weight_signs);
|
let weight_sign_tuple = match &actual_weights[i] {
|
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|
WeightValue::F64(val) => (WeightValue::F64(*val).clone(), signs[i]),
|
||||||
|
WeightValue::Str(vstr) => {
|
||||||
|
let new_str = format!("{}_{}", agg_t, vstr);
|
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|
(WeightValue::Str(new_str), signs[i])
|
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|
}
|
||||||
|
};
|
||||||
|
agg_t_map.insert(ticker, weight_sign_tuple);
|
||||||
}
|
}
|
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weights_map.insert(agg_o.clone(), agg_t_map);
|
weights_map.insert(agg_o.clone(), agg_t_map);
|
||||||
}
|
}
|
||||||
|
|
||||||
Ok(weights_map)
|
Ok(weights_map)
|
||||||
}
|
}
|
||||||
|
/// Checks that the given slices have the expected length and that:
|
||||||
|
/// - numeric weights are non-zero,
|
||||||
|
/// - signs are non-zero.
|
||||||
fn check_weights_signs_lengths(
|
fn check_weights_signs_lengths(
|
||||||
weights_vec: Vec<f64>,
|
weights_vec: &[WeightValue],
|
||||||
signs_vec: Vec<f64>,
|
signs_vec: &[f64],
|
||||||
_agg_xcats_for_cid: bool,
|
agg_xcats_for_cid: bool,
|
||||||
agg_targ_len: usize,
|
agg_targ_len: usize,
|
||||||
) -> Result<(), Box<dyn std::error::Error>> {
|
) -> Result<(), Box<dyn std::error::Error>> {
|
||||||
// for vx, vname in ...
|
// For diagnostics, decide what to call the dimension
|
||||||
let agg_targ = match _agg_xcats_for_cid {
|
let agg_targ = if agg_xcats_for_cid { "xcats" } else { "cids" };
|
||||||
true => "xcats",
|
|
||||||
false => "cids",
|
// 1) Check numeric weights for zeroes.
|
||||||
};
|
for (i, weight) in weights_vec.iter().enumerate() {
|
||||||
for (vx, vname) in vec![
|
if let WeightValue::F64(val) = weight {
|
||||||
(weights_vec.clone(), "weights"),
|
if *val == 0.0 {
|
||||||
(signs_vec.clone(), "signs"),
|
return Err(format!("The weight at index {} is 0.0", i).into());
|
||||||
] {
|
|
||||||
for (i, v) in vx.iter().enumerate() {
|
|
||||||
if *v == 0.0 {
|
|
||||||
return Err(format!("The {} at index {} is 0.0", vname, i).into());
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
if vx.len() != agg_targ_len {
|
}
|
||||||
return Err(format!(
|
// 2) Ensure the weights vector is the expected length.
|
||||||
"The length of {} ({}) does not match the length of {} ({})",
|
if weights_vec.len() != agg_targ_len {
|
||||||
vname,
|
return Err(format!(
|
||||||
vx.len(),
|
"The length of weights ({}) does not match the length of {} ({})",
|
||||||
agg_targ,
|
weights_vec.len(),
|
||||||
agg_targ_len
|
agg_targ,
|
||||||
)
|
agg_targ_len
|
||||||
.into());
|
)
|
||||||
|
.into());
|
||||||
|
}
|
||||||
|
|
||||||
|
// 3) Check signs for zero.
|
||||||
|
for (i, sign) in signs_vec.iter().enumerate() {
|
||||||
|
if *sign == 0.0 {
|
||||||
|
return Err(format!("The sign at index {} is 0.0", i).into());
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
// 4) Ensure the signs vector is the expected length.
|
||||||
|
if signs_vec.len() != agg_targ_len {
|
||||||
|
return Err(format!(
|
||||||
|
"The length of signs ({}) does not match the length of {} ({})",
|
||||||
|
signs_vec.len(),
|
||||||
|
agg_targ,
|
||||||
|
agg_targ_len
|
||||||
|
)
|
||||||
|
.into());
|
||||||
|
}
|
||||||
|
|
||||||
Ok(())
|
Ok(())
|
||||||
}
|
}
|
||||||
fn rename_result_dfw_cols(
|
fn rename_result_dfw_cols(
|
||||||
@ -393,6 +468,36 @@ fn agg_xcats_for_cid(cids: Vec<String>, xcats: Vec<String>) -> bool {
|
|||||||
xcats.len() > 1
|
xcats.len() > 1
|
||||||
}
|
}
|
||||||
|
|
||||||
|
/// Represents a weight value that can be a string, (float, or integer).
|
||||||
|
#[derive(Debug, Clone, PartialEq)]
|
||||||
|
pub enum WeightValue {
|
||||||
|
Str(String),
|
||||||
|
F64(f64),
|
||||||
|
}
|
||||||
|
impl From<String> for WeightValue {
|
||||||
|
fn from(s: String) -> Self {
|
||||||
|
WeightValue::Str(s)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
impl<'a> From<&'a str> for WeightValue {
|
||||||
|
fn from(s: &'a str) -> Self {
|
||||||
|
WeightValue::Str(s.to_string())
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
impl From<f64> for WeightValue {
|
||||||
|
fn from(f: f64) -> Self {
|
||||||
|
WeightValue::F64(f)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
impl From<i32> for WeightValue {
|
||||||
|
fn from(i: i32) -> Self {
|
||||||
|
WeightValue::F64(i as f64)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
/// Weighted linear combinations of cross sections or categories
|
/// Weighted linear combinations of cross sections or categories
|
||||||
/// # Arguments
|
/// # Arguments
|
||||||
/// * `df` - QDF DataFrame
|
/// * `df` - QDF DataFrame
|
||||||
@ -417,7 +522,7 @@ pub fn linear_composite(
|
|||||||
cids: Vec<String>,
|
cids: Vec<String>,
|
||||||
weights: Option<Vec<f64>>,
|
weights: Option<Vec<f64>>,
|
||||||
signs: Option<Vec<f64>>,
|
signs: Option<Vec<f64>>,
|
||||||
weight_xcats: Option<Vec<String>>,
|
weight_xcat: Option<String>,
|
||||||
normalize_weights: bool,
|
normalize_weights: bool,
|
||||||
start: Option<String>,
|
start: Option<String>,
|
||||||
end: Option<String>,
|
end: Option<String>,
|
||||||
@ -429,10 +534,28 @@ pub fn linear_composite(
|
|||||||
) -> Result<DataFrame, Box<dyn std::error::Error>> {
|
) -> Result<DataFrame, Box<dyn std::error::Error>> {
|
||||||
// Check if the DataFrame is a Quantamental DataFrame
|
// Check if the DataFrame is a Quantamental DataFrame
|
||||||
check_quantamental_dataframe(df)?;
|
check_quantamental_dataframe(df)?;
|
||||||
|
|
||||||
|
if agg_xcats_for_cid(cids.clone(), xcats.clone()) {
|
||||||
|
if weight_xcat.is_some() {
|
||||||
|
return Err(
|
||||||
|
format!(
|
||||||
|
"Using xcats as weights is not supported when aggregating cids for a single xcat. {:?} {:?}",
|
||||||
|
cids, xcats
|
||||||
|
)
|
||||||
|
.into(),
|
||||||
|
);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
let mut rxcats = xcats.clone();
|
||||||
|
if weight_xcat.is_some() {
|
||||||
|
rxcats.extend(vec![weight_xcat.clone().unwrap()]);
|
||||||
|
}
|
||||||
|
|
||||||
let rdf = reduce_dataframe(
|
let rdf = reduce_dataframe(
|
||||||
df.clone(),
|
df.clone(),
|
||||||
Some(cids.clone()),
|
Some(cids.clone()),
|
||||||
Some(xcats.clone()),
|
Some(rxcats.clone()),
|
||||||
Some(vec!["value".to_string()]),
|
Some(vec!["value".to_string()]),
|
||||||
start.clone(),
|
start.clone(),
|
||||||
end.clone(),
|
end.clone(),
|
||||||
@ -443,10 +566,11 @@ pub fn linear_composite(
|
|||||||
let new_xcat = new_xcat.unwrap_or_else(|| "COMPOSITE".to_string());
|
let new_xcat = new_xcat.unwrap_or_else(|| "COMPOSITE".to_string());
|
||||||
let new_cid = new_cid.unwrap_or_else(|| "GLB".to_string());
|
let new_cid = new_cid.unwrap_or_else(|| "GLB".to_string());
|
||||||
|
|
||||||
let dfw = pivot_dataframe_by_ticker(rdf.clone(), Some("value".to_string())).unwrap();
|
let dfw = pivot_dataframe_by_ticker(rdf, Some("value".to_string())).unwrap();
|
||||||
|
|
||||||
let mul_targets = get_mul_targets(cids.clone(), xcats.clone())?;
|
let mul_targets = get_mul_targets(cids.clone(), xcats.clone())?;
|
||||||
let weights_map = form_weights_and_signs_map(cids.clone(), xcats.clone(), weights, signs)?;
|
let weights_map =
|
||||||
|
form_weights_and_signs_map(cids.clone(), xcats.clone(), weights, weight_xcat, signs)?;
|
||||||
|
|
||||||
for (ticker, targets) in mul_targets.iter() {
|
for (ticker, targets) in mul_targets.iter() {
|
||||||
println!("ticker: {}, targets: {:?}", ticker, targets);
|
println!("ticker: {}, targets: {:?}", ticker, targets);
|
||||||
|
27
src/utils/qdf/blacklist.rs
Normal file
27
src/utils/qdf/blacklist.rs
Normal file
@ -0,0 +1,27 @@
|
|||||||
|
use crate::utils::dateutils::{get_bdates_series_default_opt, get_min_max_real_dates};
|
||||||
|
use crate::utils::qdf::core::*;
|
||||||
|
use chrono::{Duration, NaiveDate};
|
||||||
|
use polars::prelude::*;
|
||||||
|
use std::collections::HashMap;
|
||||||
|
use std::error::Error;
|
||||||
|
|
||||||
|
use super::pivots::pivot_dataframe_by_ticker;
|
||||||
|
|
||||||
|
/// The required columns for a Quantamental DataFrame.
|
||||||
|
const QDF_INDEX_COLUMNS: [&str; 3] = ["real_date", "cid", "xcat"];
|
||||||
|
|
||||||
|
pub fn create_blacklist_from_qdf(
|
||||||
|
df: &DataFrame,
|
||||||
|
metric: Option<String>,
|
||||||
|
start: Option<String>,
|
||||||
|
end: Option<String>,
|
||||||
|
) -> Result<HashMap<String, Vec<String>>, Box<dyn Error>> {
|
||||||
|
// Verify that the DataFrame follows the Quantamental structure.
|
||||||
|
check_quantamental_dataframe(df)?;
|
||||||
|
let mut blacklist: HashMap<String, Vec<String>> = HashMap::new();
|
||||||
|
|
||||||
|
// Use the provided metric or default to "value".
|
||||||
|
let metric = metric.unwrap_or_else(|| "value".into());
|
||||||
|
|
||||||
|
Ok(blacklist)
|
||||||
|
}
|
@ -1,11 +1,12 @@
|
|||||||
|
pub mod blacklist;
|
||||||
pub mod core;
|
pub mod core;
|
||||||
pub mod update_df;
|
|
||||||
pub mod load;
|
pub mod load;
|
||||||
pub mod reduce_df;
|
|
||||||
pub mod pivots;
|
pub mod pivots;
|
||||||
|
pub mod reduce_df;
|
||||||
|
pub mod update_df;
|
||||||
|
|
||||||
// Re-export submodules for easier access
|
// Re-export submodules for easier access
|
||||||
pub use core::*;
|
pub use core::*;
|
||||||
pub use update_df::*;
|
|
||||||
pub use load::*;
|
pub use load::*;
|
||||||
pub use reduce_df::*;
|
pub use reduce_df::*;
|
||||||
|
pub use update_df::*;
|
Loading…
x
Reference in New Issue
Block a user