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Refactor get_bdates_from_col
to enhance business day identification and bucket dates by period
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@ -43,53 +43,65 @@ pub fn get_min_max_real_dates(
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)))
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}
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}
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/// Get the business dates from a date column in a DataFrame.
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/// Identify business days, bucket them by period, and pick the first available date from each period.
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pub fn get_bdates_from_col(date_col: &Series, freq: &str) -> Result<Series, Box<dyn Error>> {
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let dates = date_col
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.date()?
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.into_iter()
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.filter_map(|opt| opt.map(|d| NaiveDate::from_num_days_from_ce_opt(d as i32)))
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.filter(|d| {
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if let Some(date) = d {
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let wd = date.weekday();
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wd != Weekday::Sat && wd != Weekday::Sun
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} else {
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false
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// Ensure the column is of Date type
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if date_col.dtype() != &DataType::Date {
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return Err("The column is not of Date type".into());
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}
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})
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.filter_map(|opt| opt) // Filter out None and unwrap Some
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.collect::<Vec<_>>();
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let aligned_dates: Vec<NaiveDate> = dates
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.iter()
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.map(|date| match freq {
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"D" => *date,
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"W" => {
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let weekday = date.weekday().num_days_from_monday(); // Get the weekday directly
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*date - chrono::Duration::days(weekday as i64)
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}
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"M" => NaiveDate::from_ymd_opt(date.year(), date.month(), 1)
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.unwrap_or_else(|| NaiveDate::from_ymd_opt(1970, 1, 1).unwrap()),
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"Q" => {
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let quarter = (date.month0() / 3) + 1;
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let month = match quarter {
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1 => 1,
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2 => 4,
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3 => 7,
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4 => 10,
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_ => unreachable!(),
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};
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NaiveDate::from_ymd_opt(date.year(), month, 1)
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.unwrap_or_else(|| NaiveDate::from_ymd_opt(1970, 1, 1).unwrap())
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}
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"A" => NaiveDate::from_ymd_opt(date.year(), 1, 1)
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.unwrap_or_else(|| NaiveDate::from_ymd_opt(1970, 1, 1).unwrap()),
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_ => *date, // fallback
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// Step 1: Identify business days (exclude weekends)
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let date_as_days = date_col.cast(&DataType::Int32)?;
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let business_days: Vec<NaiveDate> = date_as_days
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.i32()?
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.into_iter()
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.filter_map(|opt_days| {
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opt_days.map(|days| {
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NaiveDate::from_ymd_opt(1970, 1, 1).unwrap() + chrono::Duration::days(days as i64)
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})
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})
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.filter(|date| {
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// Exclude weekends (Saturday and Sunday)
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let weekday = date.weekday();
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weekday != Weekday::Sat && weekday != Weekday::Sun
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})
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.collect();
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Ok(DateChunked::from_naive_date(date_col.name().clone(), aligned_dates).into_series())
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// Step 2: Bucket dates by period
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let mut buckets: HashMap<String, Vec<NaiveDate>> = HashMap::new();
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for date in &business_days {
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let bucket_key = match freq {
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"D" => date.format("%Y-%m-%d").to_string(),
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"W" => format!("{}-W{:02}", date.year(), date.iso_week().week()),
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"M" => date.format("%Y-%m").to_string(),
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"Q" => format!("{}-Q{}", date.year(), (date.month() - 1) / 3 + 1),
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"A" => date.year().to_string(),
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_ => return Err("Invalid frequency specified".into()),
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};
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buckets.entry(bucket_key).or_default().push(*date);
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}
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// Step 3: Pick the first available date from each bucket
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let mut selected_dates: Vec<NaiveDate> = Vec::new();
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for (_, mut dates) in buckets {
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dates.sort(); // Ensure dates are sorted within the bucket
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if let Some(first_date) = dates.first() {
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selected_dates.push(*first_date);
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}
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}
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// Step 4: Convert selected dates back to a Series of Date type
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let bdates_series = Series::new(
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"bdates".into(),
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selected_dates
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.into_iter()
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.map(|date| date.format("%Y-%m-%d").to_string()) // Format as strings
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.collect::<Vec<String>>(),
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)
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.cast(&DataType::Date)?; // Cast to Date type
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Ok(bdates_series)
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}
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/// Get the `cid` from a ticker string.
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