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https://github.com/NexVeridian/ark-invest-api-rust-data.git
synced 2025-09-02 01:49:12 +00:00
0.3.4
This commit is contained in:
parent
5b05079edb
commit
6e4c536f68
SSH key fingerprint:
SHA256:bsA1SKZxuEcEVHAy3gY1HUeM5ykRJl0U0kQHQn0hMg8
1 changed files with 259 additions and 205 deletions
464
src/util.rs
464
src/util.rs
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@ -21,7 +21,6 @@ pub enum Ticker {
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ARKW,
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ARKX,
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}
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impl Ticker {
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pub fn value(&self) -> &str {
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match *self {
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@ -36,234 +35,289 @@ impl Ticker {
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}
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}
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pub fn merge_csv_to_parquet(ticker: Ticker) -> Result<(), Box<dyn Error>> {
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let mut dfs = vec![];
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for x in glob(&format!("data/csv/{}/*", ticker))?.filter_map(Result::ok) {
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dfs.push(LazyCsvReader::new(x).finish()?);
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pub enum DF {
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LazyFrame(LazyFrame),
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DataFrame(DataFrame),
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}
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impl From<LazyFrame> for DF {
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fn from(lf: LazyFrame) -> Self {
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DF::LazyFrame(lf)
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}
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let mut df = concat(dfs, false, true)?;
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if read_parquet(ticker).is_ok() {
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let df_old = read_parquet(ticker)?;
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df = concat_df(vec![df_old, df])?;
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write_parquet(ticker, df_sort(df.collect()?)?)?;
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} else {
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write_parquet(ticker, df_format(df)?)?;
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}
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impl From<DataFrame> for DF {
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fn from(df: DataFrame) -> Self {
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DF::DataFrame(df)
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}
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}
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impl DF {
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pub fn collect(self) -> Result<DataFrame, Box<dyn Error>> {
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match self {
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DF::LazyFrame(x) => Ok(x.collect()?),
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DF::DataFrame(x) => Ok(x),
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}
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}
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pub fn lazy(self) -> LazyFrame {
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match self {
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DF::LazyFrame(x) => x,
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DF::DataFrame(x) => x.lazy(),
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}
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}
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Ok(())
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}
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pub enum Source {
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Read,
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Ark,
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ApiIncremental,
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ApiFull,
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}
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struct Ark {
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df: DF,
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ticker: Ticker,
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}
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impl Ark {
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pub fn new(source: Source, ticker: Ticker) -> Result<Self, Box<dyn Error>> {
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let mut ark = Self {
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df: Self::read_parquet(ticker)?.into(),
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ticker,
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};
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pub fn update_parquet(ticker: Ticker, source: Source) -> Result<(), Box<dyn Error>> {
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let mut df = read_parquet(ticker)?;
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let update = match source {
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Source::Read => None,
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Source::Ark => Some(ark.get_csv_ark()?),
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Source::ApiIncremental => {
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let last_day = ark
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.df
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.collect()?
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.column("date")
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.unwrap()
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.max()
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.and_then(NaiveDate::from_num_days_from_ce_opt);
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Some(ark.get_api(last_day)?)
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}
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Source::ApiFull => Some(ark.get_api(None)?),
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};
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let update = match source {
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Source::Ark => get_csv_ark(ticker)?,
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Source::ApiIncremental => {
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let last_day = df
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.clone()
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.collect()?
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.column("date")
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.unwrap()
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.max()
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.and_then(NaiveDate::from_num_days_from_ce_opt);
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get_api(ticker, last_day)?
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if let Some(update) = update {
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ark.df = Self::concat_df(vec![ark.df, update.into()])?.into();
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}
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Source::ApiFull => get_api(ticker, None)?,
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};
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df = concat_df(vec![df, update])?;
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write_parquet(ticker, df_sort(df.collect()?)?)?;
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Ok(())
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}
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fn concat_df(mut dfs: Vec<LazyFrame>) -> Result<LazyFrame, Box<dyn Error>> {
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// with dedupe and format
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for x in &mut dfs {
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*x = df_format(x.to_owned())?.lazy();
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}
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let mut df = concat(dfs, false, true)?;
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df = df.unique_stable(None, UniqueKeepStrategy::First);
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Ok(df)
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}
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pub fn read_parquet(ticker: Ticker) -> Result<LazyFrame, Box<dyn Error>> {
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let df = LazyFrame::scan_parquet(
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format!("data/parquet/{}.parquet", ticker),
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ScanArgsParquet::default(),
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)?;
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Ok(df)
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}
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pub fn write_parquet(ticker: Ticker, mut df: DataFrame) -> Result<(), Box<dyn Error>> {
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ParquetWriter::new(File::create(format!("data/parquet/{}.parquet", ticker))?)
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.finish(&mut df)?;
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Ok(())
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}
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pub fn df_sort(df: DataFrame) -> Result<DataFrame, Box<dyn Error>> {
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let df = df.sort(["date", "weight"], vec![false, true])?;
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Ok(df)
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}
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pub fn df_format(df: LazyFrame) -> Result<DataFrame, Box<dyn Error>> {
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let mut df = df.collect()?;
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if df.get_column_names().contains(&"market_value_($)") {
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df = df
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.lazy()
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.rename(
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vec!["market_value_($)", "weight_(%)"],
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vec!["market_value", "weight"],
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)
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.collect()?;
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}
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if df.get_column_names().contains(&"market value ($)") {
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df = df
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.lazy()
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.rename(
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vec!["market value ($)", "weight (%)"],
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vec!["market_value", "weight"],
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)
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.collect()?;
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}
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if df.get_column_names().contains(&"CUSIP") {
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df = df
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.lazy()
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.rename(vec!["CUSIP", "weight (%)"], vec!["cusip", "weight"])
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.collect()?;
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Ok(ark)
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}
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// if df.rename("market_value_($)", "market_value").is_ok() {}
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// if df.rename("market value ($)", "market_value").is_ok() {}
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// if df.rename("weight_(%)", "weight").is_ok() {}
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// if df.rename("weight (%)", "weight").is_ok() {}
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// if df.rename("CUSIP", "cusip").is_ok() {}
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if df.get_column_names().contains(&"fund") {
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_ = df.drop_in_place("fund");
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}
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if df.get_column_names().contains(&"weight_rank") {
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_ = df.drop_in_place("weight_rank");
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pub fn write_parquet(mut self) -> Result<Self, Box<dyn Error>> {
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ParquetWriter::new(File::create(format!(
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"data/parquet/{}.parquet",
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self.ticker
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))?)
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.finish(&mut self.df.collect()?)?;
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Ok(self)
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}
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let mut expressions: Vec<Expr> = vec![];
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if !df.fields().contains(&Field::new("date", DataType::Date)) {
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expressions.push(col("date").str().strptime(
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DataType::Date,
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StrptimeOptions {
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// format: Some("%m/%d/%Y".into()),
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format: None,
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strict: false,
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exact: true,
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cache: true,
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},
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));
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}
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if df.fields().contains(&Field::new("weight", DataType::Utf8)) {
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expressions.push(
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col("weight")
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.str()
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.replace(lit("%"), lit(""), true)
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.cast(DataType::Float64),
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);
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}
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if df
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.fields()
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.contains(&Field::new("market_value", DataType::Utf8))
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{
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expressions.push(
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col("market_value")
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.str()
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.replace(lit("$"), lit(""), true)
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.str()
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.replace_all(lit(","), lit(""), true)
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.cast(DataType::Float64)
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.cast(DataType::Int64),
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);
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}
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if df.fields().contains(&Field::new("shares", DataType::Utf8)) {
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expressions.push(
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col("shares")
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.str()
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.replace_all(lit(","), lit(""), true)
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.cast(DataType::Int64),
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);
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}
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df = df
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.lazy()
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.with_columns(expressions)
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.filter(col("date").is_not_null())
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.collect()?;
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if !df.get_column_names().contains(&"share_price")
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&& df.get_column_names().contains(&"market_value")
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{
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df = df
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.lazy()
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.with_column(
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(col("market_value").cast(DataType::Float64)
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/ col("shares").cast(DataType::Float64))
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.alias("share_price")
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.cast(DataType::Float64)
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.round(2),
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)
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pub fn df_sort(&self) -> Result<&Self, Box<dyn Error>> {
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let df = self
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.df
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.collect()?
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.sort(["date", "weight"], vec![false, true])?;
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self.df = df.into();
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Ok(self)
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}
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if df.get_column_names().contains(&"share_price") {
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df = df.select([
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"date",
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"ticker",
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"cusip",
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"company",
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"market_value",
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"shares",
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"share_price",
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"weight",
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])?;
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} else {
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df = df.select(["date", "ticker", "cusip", "company", "weight"])?;
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fn read_parquet(ticker: Ticker) -> Result<LazyFrame, Box<dyn Error>> {
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let df = LazyFrame::scan_parquet(
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format!("data/parquet/{}.parquet", ticker),
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ScanArgsParquet::default(),
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)?;
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Ok(df)
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}
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Ok(df)
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}
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fn concat_df(mut dfs: Vec<DF>) -> Result<DF, Box<dyn Error>> {
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// with dedupe and format
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// for x in &mut dfs {
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// *x = Self::df_format(x.to_owned())?.lazy();
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// }
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let mut df = concat(dfs, false, true)?;
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df = df.unique_stable(None, UniqueKeepStrategy::First);
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Ok(df.into())
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}
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pub fn get_api(ticker: Ticker, last_day: Option<NaiveDate>) -> Result<LazyFrame, Box<dyn Error>> {
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let url = match (ticker, last_day) {
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(Ticker::ARKVC, Some(last_day)) => format!(
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"https://api.nexveridian.com/arkvc_holdings?end={}",
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last_day
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),
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(ticker, Some(last_day)) => format!(
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"https://api.nexveridian.com/ark_holdings?ticker={}&end={}",
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ticker, last_day
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),
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(Ticker::ARKVC, None) => "https://api.nexveridian.com/arkvc_holdings".to_owned(),
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(ticker, None) => {
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format!("https://api.nexveridian.com/ark_holdings?ticker={}", ticker)
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fn df_format(df: LazyFrame) -> Result<DataFrame, Box<dyn Error>> {
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let mut df = df.collect()?;
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if df.get_column_names().contains(&"market_value_($)") {
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df = df
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.lazy()
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.rename(
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vec!["market_value_($)", "weight_(%)"],
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vec!["market_value", "weight"],
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)
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.collect()?;
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}
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if df.get_column_names().contains(&"market value ($)") {
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df = df
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.lazy()
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.rename(
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vec!["market value ($)", "weight (%)"],
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vec!["market_value", "weight"],
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)
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.collect()?;
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}
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if df.get_column_names().contains(&"CUSIP") {
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df = df
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.lazy()
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.rename(vec!["CUSIP", "weight (%)"], vec!["cusip", "weight"])
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.collect()?;
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}
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};
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Reader::Json.get_data_url(url)
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}
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pub fn get_csv_ark(ticker: Ticker) -> Result<LazyFrame, Box<dyn Error>> {
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let url = match ticker {
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Ticker::ARKVC => "https://ark-ventures.com/wp-content/uploads/funds-etf-csv/ARK_VENTURE_FUND_HOLDINGS.csv".to_owned(),
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_ => format!("https://ark-funds.com/wp-content/uploads/funds-etf-csv/ARK_{}_ETF_{}_HOLDINGS.csv", ticker.value(), ticker),
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};
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Reader::Csv.get_data_url(url)
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// if df.rename("market_value_($)", "market_value").is_ok() {}
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// if df.rename("market value ($)", "market_value").is_ok() {}
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// if df.rename("weight_(%)", "weight").is_ok() {}
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// if df.rename("weight (%)", "weight").is_ok() {}
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// if df.rename("CUSIP", "cusip").is_ok() {}
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if df.get_column_names().contains(&"fund") {
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_ = df.drop_in_place("fund");
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}
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if df.get_column_names().contains(&"weight_rank") {
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_ = df.drop_in_place("weight_rank");
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}
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let mut expressions: Vec<Expr> = vec![];
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if !df.fields().contains(&Field::new("date", DataType::Date)) {
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expressions.push(col("date").str().strptime(
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DataType::Date,
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StrptimeOptions {
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// format: Some("%m/%d/%Y".into()),
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format: None,
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strict: false,
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exact: true,
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cache: true,
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},
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));
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}
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|
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if df.fields().contains(&Field::new("weight", DataType::Utf8)) {
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expressions.push(
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col("weight")
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.str()
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.replace(lit("%"), lit(""), true)
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.cast(DataType::Float64),
|
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);
|
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}
|
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|
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if df
|
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.fields()
|
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.contains(&Field::new("market_value", DataType::Utf8))
|
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{
|
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expressions.push(
|
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col("market_value")
|
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.str()
|
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.replace(lit("$"), lit(""), true)
|
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.str()
|
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.replace_all(lit(","), lit(""), true)
|
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.cast(DataType::Float64)
|
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.cast(DataType::Int64),
|
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);
|
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}
|
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|
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if df.fields().contains(&Field::new("shares", DataType::Utf8)) {
|
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expressions.push(
|
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col("shares")
|
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.str()
|
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.replace_all(lit(","), lit(""), true)
|
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.cast(DataType::Int64),
|
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);
|
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}
|
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|
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df = df
|
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.lazy()
|
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.with_columns(expressions)
|
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.filter(col("date").is_not_null())
|
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.collect()?;
|
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|
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if !df.get_column_names().contains(&"share_price")
|
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&& df.get_column_names().contains(&"market_value")
|
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{
|
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df = df
|
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.lazy()
|
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.with_column(
|
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(col("market_value").cast(DataType::Float64)
|
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/ col("shares").cast(DataType::Float64))
|
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.alias("share_price")
|
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.cast(DataType::Float64)
|
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.round(2),
|
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)
|
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.collect()?
|
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}
|
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|
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if df.get_column_names().contains(&"share_price") {
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df = df.select([
|
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"date",
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"ticker",
|
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"cusip",
|
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"company",
|
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"market_value",
|
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"shares",
|
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"share_price",
|
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"weight",
|
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])?;
|
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} else if !df
|
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.get_column_names()
|
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.eq(&["date", "ticker", "cusip", "company", "weight"])
|
||||
{
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df = df.select(["date", "ticker", "cusip", "company", "weight"])?;
|
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}
|
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|
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Ok(df)
|
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}
|
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|
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pub fn get_api(self, last_day: Option<NaiveDate>) -> Result<LazyFrame, Box<dyn Error>> {
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let tic = self.ticker;
|
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let url = match (tic, last_day) {
|
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(self::Ticker::ARKVC, Some(last_day)) => format!(
|
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"https://api.nexveridian.com/arkvc_holdings?end={}",
|
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last_day
|
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),
|
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(tic, Some(last_day)) => format!(
|
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"https://api.nexveridian.com/ark_holdings?ticker={}&end={}",
|
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tic.value(),
|
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last_day
|
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),
|
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(self::Ticker::ARKVC, None) => "https://api.nexveridian.com/arkvc_holdings".to_owned(),
|
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(tic, None) => {
|
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format!(
|
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"https://api.nexveridian.com/ark_holdings?ticker={}",
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tic.value()
|
||||
)
|
||||
}
|
||||
};
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Reader::Json.get_data_url(url)
|
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}
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|
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pub fn get_csv_ark(self) -> Result<LazyFrame, Box<dyn Error>> {
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let url = match self.ticker {
|
||||
self::Ticker::ARKVC => "https://ark-ventures.com/wp-content/uploads/funds-etf-csv/ARK_VENTURE_FUND_HOLDINGS.csv".to_owned(),
|
||||
_ => format!("https://ark-funds.com/wp-content/uploads/funds-etf-csv/ARK_{}_ETF_{}_HOLDINGS.csv", self.ticker.value(), self.ticker),
|
||||
};
|
||||
Reader::Csv.get_data_url(url)
|
||||
}
|
||||
|
||||
pub fn merge_old_csv_to_parquet(ticker: Ticker) -> Result<(), Box<dyn Error>> {
|
||||
let mut dfs = vec![];
|
||||
for x in glob(&format!("data/csv/{}/*", ticker))?.filter_map(Result::ok) {
|
||||
dfs.push(LazyCsvReader::new(x).finish()?);
|
||||
}
|
||||
let mut df = concat(dfs, false, true)?;
|
||||
|
||||
if Self::read_parquet(ticker).is_ok() {
|
||||
let df_old = Self::read_parquet(ticker)?;
|
||||
df = Self::concat_df(vec![df_old, df])?;
|
||||
write_parquet(ticker, df_sort(df.collect()?)?)?;
|
||||
} else {
|
||||
write_parquet(ticker, df_format(df)?)?;
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
pub enum Reader {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue