Struct lfest::FullAccountTracker

pub struct FullAccountTracker { /* private fields */ }

Keep track of many possible Account performance statistics This can be quite memory intensive, easily reaching beyond 10GB if using tick-by-tick data due to the storage of many returns

Implementations

impl FullAccountTracker

pub fn new(starting_wb: f64, futures_type: FuturesTypes) -> Self

Create a new AccTracker struct

pub fn absolute_returns(&self, source: &ReturnsSource) -> &Vec<f64>

Vector of absolute returns the account has generated, including unrealized pnl # Parameters source: the sampling interval of pnl snapshots

pub fn ln_returns(&self, source: &ReturnsSource) -> &Vec<f64>

Vector of natural logarithmic returns the account has generated, including unrealized pnl # Parameters source: the sampling interval of pnl snapshots

pub fn profit_loss_ratio(&self) -> f64

Ratio of cumulative trade profit over cumulative trade loss

pub fn cumulative_fees(&self) -> f64

Cumulative fees paid to the exchange

pub fn buy_and_hold_return(&self) -> f64

Would be return of buy and hold strategy

pub fn sell_and_hold_return(&self) -> f64

Would be return of sell and hold strategy

pub fn sharpe(
    &self,
    returns_source: ReturnsSource,
    risk_free_is_buy_and_hold: bool
) -> f64

Return the sharpe ratio using the selected returns as source

Parameters:

returns_source: the sampling interval of pnl snapshots risk_free_is_buy_and_hold: if true, it will use the market returns as the risk-free comparison else risk-free rate is zero

pub fn sortino(
    &self,
    returns_source: ReturnsSource,
    risk_free_is_buy_and_hold: bool
) -> f64

Return the Sortino ratio based on daily returns data

Parameters:

returns_source: the sampling interval of pnl snapshots risk_free_is_buy_and_hold: if true, it will use the market returns as the risk-free comparison else risk-free rate is zero

pub fn historical_value_at_risk(
    &self,
    returns_source: ReturnsSource,
    percentile: f64
) -> f64

Calculate the value at risk using the percentile method on daily returns multiplied by starting wallet balance The time horizon N is assumed to be 1 The literature says if you want a larger N, just multiply by N.sqrt(), which assumes standard normal distribution # Arguments returns_source: the sampling interval of pnl snapshots percentile: value between [0.0, 1.0], smaller value will return more worst case results

pub fn historical_value_at_risk_from_n_hourly_returns(
    &self,
    n: usize,
    percentile: f64
) -> f64

Calculate the historical value at risk from n consequtive hourly return values, This should have better statistical properties compared to using daily returns due to having more samples. Set n to 24 for daily value at risk, but with 24x more samples from which to take the percentile, giving a more accurate VaR

Parameters:

n: number of hourly returns to use percentile: value between [0.0, 1.0], smaller value will return more worst case results

pub fn cornish_fisher_value_at_risk(
    &self,
    returns_source: ReturnsSource,
    percentile: f64
) -> f64

Calculate the cornish fisher value at risk based on daily returns of the account # Arguments returns_source: the sampling interval of pnl snapshots percentile: in range [0.0, 1.0], usually something like 0.01 or 0.05

pub fn cornish_fisher_value_at_risk_from_n_hourly_returns(
    &self,
    n: usize,
    percentile: f64
) -> f64

Calculate the corni fisher value at risk from n consequtive hourly return values This should have better statistical properties compared to using daily returns due to having more samples. Set n to 24 for daily value at risk, but with 24x more samples from which to take the percentile, giving a more accurate VaR

Parameters:

n: number of hourly returns to use percentile: value between [0.0, 1.0], smaller value will return more worst case results

pub fn num_trading_days(&self) -> u64

Return the number of trading days

pub fn d_ratio(&self, returns_source: ReturnsSource) -> f64

Also called discriminant-ratio, which focuses on the added value of the algorithm It uses the Cornish-Fish Value at Risk (CF-VaR) It better captures the risk of the asset as it is not limited by the assumption of a gaussian distribution It it time-insensitive from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3927058

Parameters

returns_source: the sampling interval of pnl snapshots

pub fn annualized_roi(&self) -> f64

Annualized return on investment as a factor, e.g.: 100% -> 2x

pub fn max_drawdown_wallet_balance(&self) -> f64

Maximum drawdown of the wallet balance

pub fn max_drawdown_total(&self) -> f64

Maximum drawdown of the wallet balance including unrealized profit and loss

pub fn num_trades(&self) -> i64

Return the number of trades the account made

pub fn trade_percentage(&self) -> f64

Return the ratio of executed trades vs total trading opportunities Higher values means a more active trading agent

pub fn buy_ratio(&self) -> f64

Return the ratio of buy trades vs total number of trades

pub fn turnover(&self) -> f64

Return the cumulative turnover denoted in margin currency

pub fn total_rpnl(&self) -> f64

Return the total realized profit and loss of the account

pub fn upnl(&self) -> f64

Return the current unrealized profit and loss

pub fn win_ratio(&self) -> f64

Return the ratio of winning trades vs all trades

pub fn limit_order_fill_ratio(&self) -> f64

Return the ratio of filled limit orders vs number of submitted limit orders

pub fn limit_order_cancellation_ratio(&self) -> f64

Return the ratio of limit order cancellations vs number of submitted limit orders

Trait Implementations

impl AccountTracker for FullAccountTracker

fn update(&mut self, timestamp: u64, price: f64, upnl: f64)

Update with each tick, using data provided in update_state method of Exchange # Arguments timestamp: timestamp of latest tick price: price of latest tick upnl: unrealized profit and loss of account in current tick

fn log_rpnl(&mut self, rpnl: f64)

Log a realized profit and loss event

fn log_fee(&mut self, fee: f64)

Log a fee event

fn log_limit_order_submission(&mut self)

Log a limit order submission event

fn log_limit_order_cancellation(&mut self)

Log a limit order cancellation event

fn log_limit_order_fill(&mut self)

Log a limit order fill event

fn log_trade(&mut self, side: Side, price: f64, size: f64)

Log a trade event where some order got filled and the position changed

impl Clone for FullAccountTracker

fn clone(&self) -> FullAccountTracker

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for FullAccountTracker

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Display for FullAccountTracker

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.