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Rust library for quantitative finance tools.
:dart: I want to hit a stable and legitimate `v1.0.0` by the end of 2023, so any and all feedback, suggestions, or contributions are strongly welcomed!
Contact: rustquantcontact@gmail.com
**Disclaimer**: This is currently a free-time project and not a professional financial software library. Nothing in this library should be taken as financial advice, and I do not recommend you to use it for trading or making financial decisions.
## :newspaper: Latest features
+ Download time series data from [Yahoo! Finance](https://finance.yahoo.com/).
+ Read (write) from (to) `.csv`, `.json`, and `.parquet` files, using [Polars `DataFrames`](https://pola-rs.github.io/polars-book/).
+ Arithmetic Brownian Motion generator.
+ Gamma, exponential, and chi-squared distributions.
+ Forward start option pricer (Rubinstein 1990 formula).
+ Gap option and cash-or-nothing option pricers (currently adding more binary options).
+ Asian option pricer (closed-form solution for continuous geometric average).
+ Heston Model option pricer (uses the tanh-sinh quadrature numerical integrator).
+ Tanh-sinh (double exponential) quadrature for evaluating integrals.
+ Plus other basic numerical integrators (midpoint, trapezoid, Simpson's 3/8).
+ Characteristic functions and density functions for common distributions:
+ Gaussian, Bernoulli, Binomial, Poisson, Uniform, Chi-Squared, Gamma, and Exponential.
# Table of Contents
1. [Automatic Differentiation](#autodiff)
2. [Option Pricers](#options)
3. [Stochastic Processes and Short Rate Models](#stochastics)
4. [Bonds](#bonds)
5. [Distributions](#distributions)
5. [Mathematics](#maths)
6. [Helper Functions and Macros](#helpers)
7. [How-tos](#howto)
8. [References](#references)
## :link: Automatic Differentiation <a name="autodiff"></a>
Currently only gradients can be computed. Suggestions on how to extend the functionality to Hessian matrices are definitely welcome.
+ [x] Reverse (Adjoint) Mode
+ Implementation via Operator and Function Overloading.
+ Useful when number of outputs is *smaller* than number of inputs.
+ i.e for functions $f:\mathbb{R}^n \rightarrow \mathbb{R}^m$, where $m \ll n$
+ [ ] Forward (Tangent) Mode
+ Implementation via Dual Numbers.
+ Useful when number of outputs is *larger* than number of inputs.
+ i.e. for functions $f:\mathbb{R}^n \rightarrow \mathbb{R}^m$, where $m \gg n$
## :money_with_wings: Option Pricers <a name="options"></a>
+ Closed-form price solutions:
+ [x] Heston Model
+ [x] Barrier
+ [x] European
+ [x] Greeks/Sensitivities
+ [x] Lookback
+ [x] Asian: Continuous Geometric Average
+ [x] Forward Start
+ [ ] Basket
+ [ ] Rainbow
+ [ ] American
+ Lattice models:
+ [x] Binomial Tree (Cox-Ross-Rubinstein)
The stochastic process generators can be used to price path-dependent options via Monte-Carlo.
+ Monte Carlo pricing:
+ [x] Lookback
+ [ ] Asian
+ [ ] Chooser
+ [ ] Barrier
## :chart_with_upwards_trend: Stochastic Processes and Short Rate Models <a name="stochastics"></a>
The following is a list of stochastic processes that can be generated.
+ [x] Brownian Motion
+ [x] Arithmetic Brownian Motion
+ $dX_t = \mu dt + \sigma dW_t$
+ [x] Geometric Brownian Motion
+ $dX_t = \mu X_t dt + \sigma X_t dW_t$
+ Models: Black-Scholes (1973), Rendleman-Bartter (1980)
+ [x] Cox-Ingersoll-Ross (1985)
+ $dX_t = (\theta - \alpha X_t)dt + \sqrt{r_t} \sigma dW_t$
+ [x] Ornstein-Uhlenbeck process
+ $dX_t = \theta(\mu - X_t)dt + \sigma dW_t$
+ Models: Vasicek (1977)
+ [ ] Ho-Lee (1986)
+ $dX_t = \theta_t dt + \sigma dW_t$
+ [ ] Hull-White (1990)
+ $dX_t = (\theta - \alpha X_t)dt + \sigma_t dW_t$
+ [ ] Black-Derman-Toy (1990)
+ $d\ln(X) = \left[ \theta_t + \frac{\sigma_t'}{\sigma_t}\ln(X) \right]dt + \sigma_t dW_t$
+ $d\ln(X) = \theta_t dt + \sigma dW_t$
+ [ ] Merton's model (1973)
+ $X_t = X_0 + at + \sigma W_t^*$
+ $dX_t = adt + \sigma dW_t^*$
## :chart_with_downwards_trend: Bonds <a name="bonds"></a>
+ Prices:
+ [X] The Vasicek Model
+ [x] The Cox, Ingersoll, and Ross Model
+ [ ] The Rendleman and Bartter Model
+ [ ] The Ho–Lee Model
+ [ ] The Hull–White (One-Factor) Model
+ [ ] The Black–Derman–Toy Model
+ [ ] The Black–Karasinski Model
+ [ ] Duration
+ [ ] Convexity
## :bar_chart: Distributions <a name="distributions"></a>
Probability density/mass functions, distribution functions, characteristic functions, etc.
+ [x] Gaussian
+ [x] Bernoulli
+ [x] Binomial
+ [x] Poisson
+ [x] Uniform (discrete & continuous)
+ [x] Chi-Squared
+ [x] Gamma
+ [x] Exponential
## :triangular_ruler: Mathematics <a name="maths"></a>
+ Numerical Integration (needed for Heston model, for example):
+ [x] Tanh-Sinh (double exponential) quadrature
+ [x] Composite Midpoint Rule
+ [x] Composite Trapezoidal Rule
+ [x] Composite Simpson's 3/8 Rule
+ [x] Risk-Reward Measures (Sharpe, Treynor, Sortino, etc)
+ [x] Newton-Raphson
+ [x] Standard Normal Distribution (Distribution/Density functions, and generation of variates)
+ [ ] Interpolation
## :handshake: Helper Functions and Macros <a name="helpers"></a>
A collection of utility functions and macros.
+ [x] Plot a vector.
+ [x] Write vector to file.
+ [x] Cumulative sum of vector.
+ [x] Linearly spaced sequence.
+ [x] `assert_approx_equal!`
## :heavy_check_mark: How-tos <a name="howto"></a>
I would not recommend using RustQuant within any other libraries for some time, as it will most likely go through many breaking changes as I learn more Rust and settle on a decent structure for the library.
:pray: I would greatly appreciate contributions so it can get to the `v1.0.0` mark ASAP.
### Download data from Yahoo! Finance:
You can download data from Yahoo! Finance into a Polars `DataFrame`.
```rust
use RustQuant::data::*;
use time::macros::date;
fn main() {
// New YahooFinanceData instance.
// By default, date range is: 1970-01-01 to present.
let mut yfd = YahooFinanceData::new("AAPL".to_string());
// Can specify custom dates (optional).
yfd.set_start_date(time::macros::datetime!(2019 - 01 - 01 0:00 UTC));
yfd.set_end_date(time::macros::datetime!(2020 - 01 - 01 0:00 UTC));
// Download the historical data.
yfd.get_price_history();
println!("Apple's quotes: {:?}", yfd.price_history)
}
```
```bash
Apple's quotes: Some(shape: (252, 7)
┌────────────┬───────────┬───────────┬───────────┬───────────┬────────────┬───────────┐
│ date ┆ open ┆ high ┆ low ┆ close ┆ volume ┆ adjusted │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ date ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞════════════╪═══════════╪═══════════╪═══════════╪═══════════╪════════════╪═══════════╡
│ 2019-01-02 ┆ 38.7225 ┆ 39.712502 ┆ 38.557499 ┆ 39.48 ┆ 1.481588e8 ┆ 37.994499 │
│ 2019-01-03 ┆ 35.994999 ┆ 36.43 ┆ 35.5 ┆ 35.547501 ┆ 3.652488e8 ┆ 34.209969 │
│ 2019-01-04 ┆ 36.1325 ┆ 37.137501 ┆ 35.950001 ┆ 37.064999 ┆ 2.344284e8 ┆ 35.670372 │
│ 2019-01-07 ┆ 37.174999 ┆ 37.2075 ┆ 36.474998 ┆ 36.982498 ┆ 2.191112e8 ┆ 35.590965 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2019-12-26 ┆ 71.205002 ┆ 72.495003 ┆ 71.175003 ┆ 72.477501 ┆ 9.31212e7 ┆ 70.798401 │
│ 2019-12-27 ┆ 72.779999 ┆ 73.4925 ┆ 72.029999 ┆ 72.449997 ┆ 1.46266e8 ┆ 70.771545 │
│ 2019-12-30 ┆ 72.364998 ┆ 73.172501 ┆ 71.305 ┆ 72.879997 ┆ 1.441144e8 ┆ 71.191582 │
│ 2019-12-31 ┆ 72.482498 ┆ 73.419998 ┆ 72.379997 ┆ 73.412498 ┆ 1.008056e8 ┆ 71.711739 │
└────────────┴───────────┴───────────┴───────────┴───────────┴────────────┴───────────┘)
```
### Read/write data:
```rust
use RustQuant::data::*;
fn main() {
// New `Data` instance.
let mut data = Data::new(
format: DataFormat::CSV, // Can also be JSON or PARQUET.
path: String::from("./file/path/read.csv")
)
// Read from the given file.
data.read().unwrap();
// New path to write the data to.
data.path = String::from("./file/path/write.csv")
data.write().unwrap();
println!("{:?}", data.data)
}
```
### Compute gradients:
```rust
use RustQuant::autodiff::*;
fn main() {
// Create a new Tape.
let t = Tape::new();
// Assign variables.
let x = t.var(0.5);
let y = t.var(4.2);
// Define a function.
let z = x * y + x.sin();
// Accumulate the gradient.
let grad = z.accumulate();
println!("Function = {}", z);
println!("Gradient = {:?}", grad.wrt([x, y]));
}
```
### Compute integrals:
```rust
use RustQuant::math::*;
fn main() {
// Define a function to integrate: e^(sin(x))
fn f(x: f64) -> f64 {
(x.sin()).exp()
}
// Integrate from 0 to 5.
let integral = integrate(f, 0.0, 5.0);
// ~ 7.18911925
println!("Integral = {}", integral);
}
```
### Price options:
```rust
use RustQuant::options::*;
fn main() {
let VanillaOption = EuropeanOption {
initial_price: 100.0,
strike_price: 110.0,
risk_free_rate: 0.05,
volatility: 0.2,
dividend_rate: 0.02,
time_to_maturity: 0.5,
};
let prices = VanillaOption.price();
println!("Call price = {}", prices.0);
println!("Put price = {}", prices.1);
}
```
### Generate stochastic processes:
```rust
use RustQuant::stochastics::*;
fn main() {
// Create new GBM with mu and sigma.
let gbm = GeometricBrownianMotion::new(0.05, 0.9);
// Generate path using Euler-Maruyama scheme.
// Parameters: x_0, t_0, t_n, n, sims, parallel.
let output = (&gbm).euler_maruyama(10.0, 0.0, 0.5, 10, 1, false);
println!("GBM = {:?}", output.trajectories);
}
```
## :book: References: <a name="references"></a>
+ John C. Hull - *Options, Futures, and Other Derivatives*
+ Damiano Brigo & Fabio Mercurio - *Interest Rate Models - Theory and Practice (With Smile, Inflation and Credit)*
+ Paul Glasserman - *Monte Carlo Methods in Financial Engineering*
+ Andreas Griewank & Andrea Walther - *Evaluating Derivatives - Principles and Techniques of Algorithmic Differentiation*
+ Steven E. Shreve - *Stochastic Calculus for Finance II: Continuous-Time Models*
+ Espen Gaarder Haug - *Option Pricing Formulas*
+ Antoine Savine - *Modern Computational Finance: AAD and Parallel Simulations*
Raw data
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"description": "\n\n\n<p align=\"center\">\n <a href=\"#license\" alt=\"license\">\n <img alt=\"License\" src=\"https://img.shields.io/github/license/avhz/RustQuant\">\n <a href=\"#build\" alt=\"build\">\n <img alt=\"GitHub Workflow Status\" src=\"https://img.shields.io/github/actions/workflow/status/avhz/RustQuant/rust.yml\">\n <a href=\"#downloads\" alt=\"downloads\">\n <img alt=\"Crates.io\" src=\"https://img.shields.io/crates/d/RustQuant\">\n <a href=\"#stars\" alt=\"stars\">\n <img alt=\"GitHub Repo stars\" src=\"https://img.shields.io/github/stars/avhz/RustQuant\">\n <a href=\"#version\" alt=\"version\">\n <img alt=\"Crates.io\" src=\"https://img.shields.io/crates/v/RustQuant\">\n <a href=\"#codecov\" alt=\"codecov\">\n <img alt=\"Codecov\" src=\"https://img.shields.io/codecov/c/gh/avhz/RustQuant\">\n <a href=\"#deps\" alt=\"deps\">\n <img alt=\"Dependencies\" src=\"https://deps.rs/repo/github/avhz/RustQuant/status.svg\">\n</p>\n\nRust library for quantitative finance tools. \n\n:dart: I want to hit a stable and legitimate `v1.0.0` by the end of 2023, so any and all feedback, suggestions, or contributions are strongly welcomed! \n\nContact: rustquantcontact@gmail.com \n\n**Disclaimer**: This is currently a free-time project and not a professional financial software library. Nothing in this library should be taken as financial advice, and I do not recommend you to use it for trading or making financial decisions. \n\n## :newspaper: Latest features\n\n+ Download time series data from [Yahoo! Finance](https://finance.yahoo.com/).\n+ Read (write) from (to) `.csv`, `.json`, and `.parquet` files, using [Polars `DataFrames`](https://pola-rs.github.io/polars-book/).\n+ Arithmetic Brownian Motion generator. \n+ Gamma, exponential, and chi-squared distributions.\n+ Forward start option pricer (Rubinstein 1990 formula).\n+ Gap option and cash-or-nothing option pricers (currently adding more binary options).\n+ Asian option pricer (closed-form solution for continuous geometric average).\n+ Heston Model option pricer (uses the tanh-sinh quadrature numerical integrator).\n+ Tanh-sinh (double exponential) quadrature for evaluating integrals.\n + Plus other basic numerical integrators (midpoint, trapezoid, Simpson's 3/8).\n+ Characteristic functions and density functions for common distributions:\n + Gaussian, Bernoulli, Binomial, Poisson, Uniform, Chi-Squared, Gamma, and Exponential.\n\n# Table of Contents\n\n1. [Automatic Differentiation](#autodiff)\n2. [Option Pricers](#options)\n3. [Stochastic Processes and Short Rate Models](#stochastics)\n4. [Bonds](#bonds)\n5. [Distributions](#distributions)\n5. [Mathematics](#maths)\n6. [Helper Functions and Macros](#helpers)\n7. [How-tos](#howto)\n8. [References](#references)\n\n\n## :link: Automatic Differentiation <a name=\"autodiff\"></a>\n\nCurrently only gradients can be computed. Suggestions on how to extend the functionality to Hessian matrices are definitely welcome. \n\n+ [x] Reverse (Adjoint) Mode\n + Implementation via Operator and Function Overloading.\n + Useful when number of outputs is *smaller* than number of inputs. \n + i.e for functions $f:\\mathbb{R}^n \\rightarrow \\mathbb{R}^m$, where $m \\ll n$\n+ [ ] Forward (Tangent) Mode\n + Implementation via Dual Numbers.\n + Useful when number of outputs is *larger* than number of inputs. \n + i.e. for functions $f:\\mathbb{R}^n \\rightarrow \\mathbb{R}^m$, where $m \\gg n$\n\n## :money_with_wings: Option Pricers <a name=\"options\"></a>\n\n+ Closed-form price solutions:\n + [x] Heston Model\n + [x] Barrier\n + [x] European\n + [x] Greeks/Sensitivities\n + [x] Lookback\n + [x] Asian: Continuous Geometric Average \n + [x] Forward Start \n + [ ] Basket\n + [ ] Rainbow\n + [ ] American\n\n+ Lattice models:\n + [x] Binomial Tree (Cox-Ross-Rubinstein)\n\nThe stochastic process generators can be used to price path-dependent options via Monte-Carlo.\n\n+ Monte Carlo pricing:\n + [x] Lookback\n + [ ] Asian\n + [ ] Chooser\n + [ ] Barrier\n\n## :chart_with_upwards_trend: Stochastic Processes and Short Rate Models <a name=\"stochastics\"></a>\n\nThe following is a list of stochastic processes that can be generated.\n\n+ [x] Brownian Motion\n+ [x] Arithmetic Brownian Motion\n + $dX_t = \\mu dt + \\sigma dW_t$\n+ [x] Geometric Brownian Motion\n + $dX_t = \\mu X_t dt + \\sigma X_t dW_t$\n + Models: Black-Scholes (1973), Rendleman-Bartter (1980)\n+ [x] Cox-Ingersoll-Ross (1985)\n + $dX_t = (\\theta - \\alpha X_t)dt + \\sqrt{r_t} \\sigma dW_t$\n+ [x] Ornstein-Uhlenbeck process\n + $dX_t = \\theta(\\mu - X_t)dt + \\sigma dW_t$\n + Models: Vasicek (1977)\n+ [ ] Ho-Lee (1986)\n + $dX_t = \\theta_t dt + \\sigma dW_t$\n+ [ ] Hull-White (1990)\n + $dX_t = (\\theta - \\alpha X_t)dt + \\sigma_t dW_t$\n+ [ ] Black-Derman-Toy (1990)\n + $d\\ln(X) = \\left[ \\theta_t + \\frac{\\sigma_t'}{\\sigma_t}\\ln(X) \\right]dt + \\sigma_t dW_t$\n + $d\\ln(X) = \\theta_t dt + \\sigma dW_t$\n+ [ ] Merton's model (1973)\n + $X_t = X_0 + at + \\sigma W_t^*$\n + $dX_t = adt + \\sigma dW_t^*$\n\n## :chart_with_downwards_trend: Bonds <a name=\"bonds\"></a>\n\n+ Prices:\n + [X] The Vasicek Model\n + [x] The Cox, Ingersoll, and Ross Model\n + [ ] The Rendleman and Bartter Model\n + [ ] The Ho\u2013Lee Model\n + [ ] The Hull\u2013White (One-Factor) Model\n + [ ] The Black\u2013Derman\u2013Toy Model\n + [ ] The Black\u2013Karasinski Model\n+ [ ] Duration\n+ [ ] Convexity\n\n## :bar_chart: Distributions <a name=\"distributions\"></a>\n\nProbability density/mass functions, distribution functions, characteristic functions, etc. \n\n+ [x] Gaussian\n+ [x] Bernoulli\n+ [x] Binomial\n+ [x] Poisson\n+ [x] Uniform (discrete & continuous)\n+ [x] Chi-Squared\n+ [x] Gamma\n+ [x] Exponential\n\n## :triangular_ruler: Mathematics <a name=\"maths\"></a>\n\n+ Numerical Integration (needed for Heston model, for example):\n + [x] Tanh-Sinh (double exponential) quadrature \n + [x] Composite Midpoint Rule\n + [x] Composite Trapezoidal Rule\n + [x] Composite Simpson's 3/8 Rule\n+ [x] Risk-Reward Measures (Sharpe, Treynor, Sortino, etc)\n+ [x] Newton-Raphson\n+ [x] Standard Normal Distribution (Distribution/Density functions, and generation of variates)\n+ [ ] Interpolation\n\n## :handshake: Helper Functions and Macros <a name=\"helpers\"></a>\n\nA collection of utility functions and macros. \n\n+ [x] Plot a vector.\n+ [x] Write vector to file.\n+ [x] Cumulative sum of vector.\n+ [x] Linearly spaced sequence.\n+ [x] `assert_approx_equal!`\n\n## :heavy_check_mark: How-tos <a name=\"howto\"></a>\n\nI would not recommend using RustQuant within any other libraries for some time, as it will most likely go through many breaking changes as I learn more Rust and settle on a decent structure for the library. \n\n:pray: I would greatly appreciate contributions so it can get to the `v1.0.0` mark ASAP.\n\n### Download data from Yahoo! Finance:\n\nYou can download data from Yahoo! Finance into a Polars `DataFrame`.\n\n```rust\nuse RustQuant::data::*;\nuse time::macros::date;\n\nfn main() {\n // New YahooFinanceData instance. \n // By default, date range is: 1970-01-01 to present. \n let mut yfd = YahooFinanceData::new(\"AAPL\".to_string());\n\n // Can specify custom dates (optional). \n yfd.set_start_date(time::macros::datetime!(2019 - 01 - 01 0:00 UTC));\n yfd.set_end_date(time::macros::datetime!(2020 - 01 - 01 0:00 UTC));\n\n // Download the historical data. \n yfd.get_price_history();\n\n println!(\"Apple's quotes: {:?}\", yfd.price_history)\n}\n```\n\n```bash\nApple's quotes: Some(shape: (252, 7)\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 date \u2506 open \u2506 high \u2506 low \u2506 close \u2506 volume \u2506 adjusted \u2502\n\u2502 --- \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2506 --- \u2502\n\u2502 date \u2506 f64 \u2506 f64 \u2506 f64 \u2506 f64 \u2506 f64 \u2506 f64 \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 2019-01-02 \u2506 38.7225 \u2506 39.712502 \u2506 38.557499 \u2506 39.48 \u2506 1.481588e8 \u2506 37.994499 \u2502\n\u2502 2019-01-03 \u2506 35.994999 \u2506 36.43 \u2506 35.5 \u2506 35.547501 \u2506 3.652488e8 \u2506 34.209969 \u2502\n\u2502 2019-01-04 \u2506 36.1325 \u2506 37.137501 \u2506 35.950001 \u2506 37.064999 \u2506 2.344284e8 \u2506 35.670372 \u2502\n\u2502 2019-01-07 \u2506 37.174999 \u2506 37.2075 \u2506 36.474998 \u2506 36.982498 \u2506 2.191112e8 \u2506 35.590965 \u2502\n\u2502 \u2026 \u2506 \u2026 \u2506 \u2026 \u2506 \u2026 \u2506 \u2026 \u2506 \u2026 \u2506 \u2026 \u2502\n\u2502 2019-12-26 \u2506 71.205002 \u2506 72.495003 \u2506 71.175003 \u2506 72.477501 \u2506 9.31212e7 \u2506 70.798401 \u2502\n\u2502 2019-12-27 \u2506 72.779999 \u2506 73.4925 \u2506 72.029999 \u2506 72.449997 \u2506 1.46266e8 \u2506 70.771545 \u2502\n\u2502 2019-12-30 \u2506 72.364998 \u2506 73.172501 \u2506 71.305 \u2506 72.879997 \u2506 1.441144e8 \u2506 71.191582 \u2502\n\u2502 2019-12-31 \u2506 72.482498 \u2506 73.419998 \u2506 72.379997 \u2506 73.412498 \u2506 1.008056e8 \u2506 71.711739 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518)\n```\n\n### Read/write data:\n\n```rust\nuse RustQuant::data::*;\n\nfn main() {\n // New `Data` instance.\n let mut data = Data::new(\n format: DataFormat::CSV, // Can also be JSON or PARQUET.\n path: String::from(\"./file/path/read.csv\")\n )\n\n // Read from the given file. \n data.read().unwrap();\n\n // New path to write the data to. \n data.path = String::from(\"./file/path/write.csv\")\n data.write().unwrap();\n\n println!(\"{:?}\", data.data)\n}\n```\n\n### Compute gradients:\n\n```rust\nuse RustQuant::autodiff::*;\n\nfn main() {\n // Create a new Tape.\n let t = Tape::new();\n\n // Assign variables.\n let x = t.var(0.5);\n let y = t.var(4.2);\n\n // Define a function.\n let z = x * y + x.sin();\n\n // Accumulate the gradient.\n let grad = z.accumulate();\n\n println!(\"Function = {}\", z);\n println!(\"Gradient = {:?}\", grad.wrt([x, y]));\n}\n```\n\n### Compute integrals:\n\n```rust\nuse RustQuant::math::*;\n\nfn main() {\n // Define a function to integrate: e^(sin(x))\n fn f(x: f64) -> f64 {\n (x.sin()).exp()\n }\n\n // Integrate from 0 to 5.\n let integral = integrate(f, 0.0, 5.0);\n\n // ~ 7.18911925\n println!(\"Integral = {}\", integral); \n}\n```\n\n### Price options:\n\n```rust\nuse RustQuant::options::*;\n\nfn main() {\n let VanillaOption = EuropeanOption {\n initial_price: 100.0,\n strike_price: 110.0,\n risk_free_rate: 0.05,\n volatility: 0.2,\n dividend_rate: 0.02,\n time_to_maturity: 0.5,\n };\n\n let prices = VanillaOption.price();\n\n println!(\"Call price = {}\", prices.0);\n println!(\"Put price = {}\", prices.1);\n}\n```\n\n### Generate stochastic processes:\n\n```rust\nuse RustQuant::stochastics::*;\n\nfn main() {\n // Create new GBM with mu and sigma.\n let gbm = GeometricBrownianMotion::new(0.05, 0.9);\n\n // Generate path using Euler-Maruyama scheme.\n // Parameters: x_0, t_0, t_n, n, sims, parallel.\n let output = (&gbm).euler_maruyama(10.0, 0.0, 0.5, 10, 1, false);\n\n println!(\"GBM = {:?}\", output.trajectories);\n}\n```\n\n## :book: References: <a name=\"references\"></a>\n\n+ John C. Hull - *Options, Futures, and Other Derivatives*\n+ Damiano Brigo & Fabio Mercurio - *Interest Rate Models - Theory and Practice (With Smile, Inflation and Credit)*\n+ Paul Glasserman - *Monte Carlo Methods in Financial Engineering*\n+ Andreas Griewank & Andrea Walther - *Evaluating Derivatives - Principles and Techniques of Algorithmic Differentiation*\n+ Steven E. Shreve - *Stochastic Calculus for Finance II: Continuous-Time Models*\n+ Espen Gaarder Haug - *Option Pricing Formulas*\n+ Antoine Savine - *Modern Computational Finance: AAD and Parallel Simulations*\n\n",
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