diff --git a/src/panel/linear_composite.rs b/src/panel/linear_composite.rs
index e69de29..39a34e1 100644
--- a/src/panel/linear_composite.rs
+++ b/src/panel/linear_composite.rs
@@ -0,0 +1,272 @@
+use crate::utils::misc::*;
+use crate::utils::qdf::check_quantamental_dataframe;
+use crate::utils::qdf::pivots::*;
+use crate::utils::qdf::reduce_df::*;
+use chrono::NaiveDate;
+use ndarray::Data;
+use ndarray::{s, Array, Array1, Zip};
+use polars::prelude::*;
+use polars::series::Series; // Series struct
+use std::collections::HashMap;
+
+fn perform_single_group_agg(
+    dfw: &DataFrame,
+    new_dfw: &DataFrame,
+    agg_on: &String,
+    agg_targs: &Vec<String>,
+    agg_weights_map: &HashMap<String, Vec<f64>>,
+    normalize_weights: bool,
+    complete: bool,
+) -> Result<DataFrame, PolarsError> {
+    // Replace this with the actual implementation
+    // get all agg_targs as columns
+
+    let mut weights_dfw = DataFrame::new(vec![])?; // Placeholder for weights DataFrame
+    for (agg_targ, weight_signs) in agg_weights_map.iter() {
+        let wgt = weight_signs[0] * weight_signs[1];
+        let wgt_series = Series::new(agg_targ.into(), vec![wgt]);
+        weights_dfw.with_column(wgt_series)?;
+    }
+
+    let mut data_dfw = DataFrame::new(vec![])?; // Placeholder for target DataFrame
+    for agg_targ in agg_targs {
+        if !dfw.get_column_names().contains(&&PlSmallStr::from_string(agg_targ.to_string())) {
+            continue;
+        }
+        let agg_targ_series = dfw.column(agg_targ)?.clone();
+        data_dfw.with_column(agg_targ_series)?;
+    }
+
+
+    // nan_mask = [iter over data_dfw.columns() applying is_nan()] OR [iter over data_dfw.rows() applying is_nan()]
+    let mut nan_mask = DataFrame::new(vec![])?; // Placeholder for NaN mask DataFrame
+    for col in data_dfw.get_column_names() {
+        let col_series = data_dfw.column(col)?;
+        let nan_mask_series = col_series.is_nan()?
+            .cast(&DataType::Boolean)?
+            .into_series();
+        nan_mask.with_column(nan_mask_series)?;
+    }
+    
+
+
+    Ok(new_dfw.clone())
+}
+
+fn perform_multiplication(
+    dfw: &DataFrame,
+    mult_targets: &HashMap<String, Vec<String>>,
+    weights_map: &HashMap<String, HashMap<String, Vec<f64>>>,
+    complete: bool,
+    normalize_weights: bool,
+) -> Result<DataFrame, PolarsError> {
+    let real_date = dfw.column("real_date")?.clone();
+    let mut new_dfw = DataFrame::new(vec![real_date])?;
+
+    for (agg_on, agg_targs) in mult_targets.iter() {
+        // perform_single_group_agg
+        perform_single_group_agg(
+            dfw,
+            &new_dfw,
+            agg_on,
+            agg_targs,
+            &weights_map[agg_on],
+            normalize_weights,
+            complete,
+        )?;
+    }
+
+    // Placeholder logic to return a valid DataFrame for now
+    // Replace this with the actual implementation
+    Ok(new_dfw)
+}
+
+fn get_agg_on_agg_targs(cids: Vec<String>, xcats: Vec<String>) -> (Vec<String>, Vec<String>) {
+    let _agg_xcats_for_cid = agg_xcats_for_cid(cids.clone(), xcats.clone());
+    let (agg_on, agg_targ) = if _agg_xcats_for_cid {
+        (cids.clone(), xcats.clone())
+    } else {
+        (xcats.clone(), cids.clone())
+    };
+    // assert that if agg_xcats_for_cid is true, agg_on = cids
+    match _agg_xcats_for_cid {
+        true => {
+            assert_eq!(agg_on, cids);
+            assert_eq!(agg_targ, xcats);
+        }
+        false => {
+            assert_eq!(agg_on, xcats);
+            assert_eq!(agg_targ, cids);
+        }
+    }
+    (agg_on, agg_targ)
+}
+
+/// Get the mapping of aggregation targets for the implied mode of aggregation.
+/// # Returns
+/// * `HashMap<String, Vec<String>>` - A mapping of cid/xcat to the list of tickers to be aggregated.
+fn get_mul_targets(
+    cids: Vec<String>,
+    xcats: Vec<String>,
+    dfw: &DataFrame,
+) -> Result<HashMap<String, Vec<String>>, Box<dyn std::error::Error>> {
+    let _agg_xcats_for_cid = agg_xcats_for_cid(cids.clone(), xcats.clone());
+    let mut mul_targets = HashMap::new();
+
+    let found_tickers = dfw
+        .get_column_names()
+        .iter()
+        .map(|name| name.to_string())
+        .collect::<Vec<String>>();
+
+    let (agg_on, agg_targ) = get_agg_on_agg_targs(cids.clone(), xcats.clone());
+
+    for agg_o in agg_on {
+        let mut targets = Vec::new();
+        for agg_t in &agg_targ {
+            let ticker = match _agg_xcats_for_cid {
+                true => format!("{}_{}", agg_t, agg_o),
+                false => format!("{}_{}", agg_o, agg_t),
+            };
+            if found_tickers.contains(&ticker) {
+                targets.push(ticker);
+            }
+        }
+        if !targets.is_empty() {
+            mul_targets.insert(agg_o.clone(), targets);
+        }
+    }
+    Ok(mul_targets)
+}
+
+fn form_weights_and_signs_map(
+    cids: Vec<String>,
+    xcats: Vec<String>,
+    weights: Option<Vec<f64>>,
+    signs: Option<Vec<f64>>,
+) -> Result<HashMap<String, HashMap<String, Vec<f64>>>, Box<dyn std::error::Error>> {
+    let _agg_xcats_for_cid = agg_xcats_for_cid(cids.clone(), xcats.clone());
+
+    let (agg_on, agg_targ) = get_agg_on_agg_targs(cids.clone(), xcats.clone());
+
+    // if weights are None, create a vector of 1s of the same length as agg_targ
+    let weights = weights.unwrap_or(vec![1.0; agg_targ.len()]);
+    let signs = signs.unwrap_or(vec![1.0; agg_targ.len()]);
+
+    // check that the lengths of weights and signs match the length of agg_targ
+    check_weights_signs_lengths(
+        weights.clone(),
+        signs.clone(),
+        _agg_xcats_for_cid,
+        agg_targ.len(),
+    )?;
+
+    let mut weights_map = HashMap::new();
+
+    for agg_o in agg_on {
+        let mut agg_t_map = HashMap::new();
+        for (i, agg_t) in agg_targ.iter().enumerate() {
+            let ticker = match _agg_xcats_for_cid {
+                true => format!("{}_{}", agg_t, agg_o),
+                false => format!("{}_{}", agg_o, agg_t),
+            };
+            let weight_signs = vec![weights[i], signs[i]];
+            agg_t_map.insert(ticker, weight_signs);
+        }
+        weights_map.insert(agg_o.clone(), agg_t_map);
+    }
+    Ok(weights_map)
+}
+
+fn check_weights_signs_lengths<T>(
+    weights_vec: Vec<T>,
+    signs_vec: Vec<T>,
+    _agg_xcats_for_cid: bool,
+    agg_targ_len: usize,
+) -> Result<(), Box<dyn std::error::Error>> {
+    // for vx, vname in ...
+    let agg_targ = match _agg_xcats_for_cid {
+        true => "xcats",
+        false => "cids",
+    };
+    for (vx, vname) in vec![(weights_vec.len(), "weights"), (signs_vec.len(), "signs")] {
+        if vx != agg_targ_len {
+            return Err(format!(
+                "The length of {} ({}) does not match the length of {} ({})",
+                vname, vx, agg_targ, agg_targ_len
+            )
+            .into());
+        }
+    }
+    Ok(())
+}
+
+/// Flags if the xcats are aggregated for a given cid.
+/// If true, the xcats are aggregated for each cid, creating a new xcat.
+/// If false, the cids are aggregated for each xcat, creating a new cid.
+fn agg_xcats_for_cid(cids: Vec<String>, xcats: Vec<String>) -> bool {
+    // if there is more than 1 xcat, return xcats.len() > 1
+    xcats.len() > 1
+}
+
+/// Weighted linear combinations of cross sections or categories
+/// # Arguments
+/// * `df` - QDF DataFrame
+/// * `xcats` - List of category names or a single category name
+/// * `cids` - List of cross section names or None
+/// * `weights` - List of weights or a string indicating a weight `xcat`
+/// * `normalize_weights` - Normalize weights to sum to 1 before applying
+/// * `signs` - List of signs for each category (+1 or -1)
+/// * `start` - Start date for the analysis
+/// * `end` - End date for the analysis
+/// * `blacklist` - Dictionary of blacklisted categories
+/// * `complete_xcats` - If True, complete xcats with missing values
+/// * `complete_cids` - If True, complete cids with missing values
+/// * `new_xcat` - Name of the new xcat
+/// * `new_cid` - Name of the new cid
+///
+/// # Returns
+/// * `DataFrame` - DataFrame with the linear composite
+pub fn linear_composite(
+    df: &DataFrame,
+    xcats: Vec<String>,
+    cids: Vec<String>,
+    weights: Option<Vec<f64>>,
+    signs: Option<Vec<f64>>,
+    weight_xcats: Option<Vec<String>>,
+    normalize_weights: bool,
+    start: Option<String>,
+    end: Option<String>,
+    blacklist: Option<HashMap<String, Vec<String>>>,
+    complete_xcats: bool,
+    complete_cids: bool,
+    new_xcat: Option<String>,
+    new_cid: Option<String>,
+) -> Result<DataFrame, Box<dyn std::error::Error>> {
+    // Check if the DataFrame is a Quantamental DataFrame
+    check_quantamental_dataframe(df)?;
+    let rdf = reduce_dataframe(
+        df.clone(),
+        Some(cids.clone()),
+        Some(xcats.clone()),
+        Some(vec!["value".to_string()]),
+        start.clone(),
+        end.clone(),
+        false,
+    )
+    .unwrap();
+
+    let mut dfw = pivot_dataframe_by_ticker(rdf, Some("value".to_string())).unwrap();
+
+    let mul_targets = get_mul_targets(cids.clone(), xcats.clone(), &dfw)?;
+    let weights_map = form_weights_and_signs_map(cids.clone(), xcats.clone(), weights, signs)?;
+
+    for (ticker, targets) in mul_targets.iter() {
+        println!("ticker: {}, targets: {:?}", ticker, targets);
+    }
+    for (agg_on, agg_t_map) in weights_map.iter() {
+        println!("agg_on: {}, agg_t_map: {:?}", agg_on, agg_t_map);
+    }
+
+    Ok(dfw)
+}