Implementation of the Binomial Model for Option Pricing

Cargo.toml

@@ -21,32 +21,33 @@ exclude = [
     "Cargo.lock"
 ]
 
-[toolchain]
-channel = "nightly"
-
 [dependencies]
-tokio = { version = "1.39.2", features = ["rt", "rt-multi-thread", "macros", "time", "signal"] }
-futures-util = "0.3.30"
-dotenv = "0.15.0"
-thiserror = "1.0.63"
-serde_json = "1.0.121"
-serde = { version = "1.0.204", features = ["derive"] }
-redis = { version = "0.26.0", features = ["cluster", "cluster-async", "aio", "async-std-comp"] }
-mongodb-aggregations = "0.1.0"
-mongodb = {  version = "=2.8.2" }
-futures = "0.3.30"
+#tokio = { version = "1.39.2", features = ["rt", "rt-multi-thread", "macros", "time", "signal"] }
+#futures-util = "0.3.30"
+#dotenv = "0.15.0"
+#thiserror = "1.0.63"
+#serde_json = "1.0.121"
+#serde = { version = "1.0.204", features = ["derive"] }
+#redis = { version = "0.26.0", features = ["cluster", "cluster-async", "aio", "async-std-comp"] }
+#mongodb-aggregations = "0.1.0"
+#mongodb = {  version = "=2.8.2" }
+#futures = "0.3.30"
 chrono = "0.4.38"
-regex = "1.10.5"
-rand = "0.8.5"
-sha2 = "0.10.8"
-reqwest = { version = "0.12.5", features = ["json"] }
-anyhow = "1.0.86"
-tracing = "0.1.40"
-tracing-subscriber = "0.3.18"
-async-trait = "0.1.80"
-sqlx = { version = "0.8.0", features = ["runtime-tokio", "macros", "mysql"] }
-chrono-tz = "0.9.0"
+#regex = "1.10.5"
+#rand = "0.8.5"
+#sha2 = "0.10.8"
+#reqwest = { version = "0.12.5", features = ["json"] }
+#anyhow = "1.0.86"
+#tracing = "0.1.40"
+#tracing-subscriber = "0.3.18"
+#async-trait = "0.1.80"
+#sqlx = { version = "0.8.0", features = ["runtime-tokio", "macros", "mysql"] }
+#chrono-tz = "0.9.0"
 approx = "0.5.1"
+plotters = "0.3"
+statrs = "0.17.1"
+rayon = "1.10.0"
+rand = "0.8.5"
 
 
 [dev-dependencies]
@@ -59,7 +60,7 @@ lazy_static = "1.5.0"
 
 [[example]]
 name = "example"
-path = "examples/example.rs"
+path = "examples/volatility.rs"
 
 [[test]]
 name = "tests"

README.md

@@ -1,5 +1,7 @@
 
-![OptionStratLib](doc/images/logo.png)
+<div style="text-align: center;">
+  <img src="doc/images/logo.png" alt="OptionStratLib" style="width: 100%; height: 200px;">
+</div>
 
 OptionStratLib is a comprehensive Rust library for options trading and strategy development across multiple asset classes. This versatile toolkit enables traders, quants, and developers to:
 
@@ -19,4 +21,37 @@ Risk analysis tools
 Backtesting capabilities
 Performance visualization
 
-Empower your options trading with OptionStratLib – the all-in-one solution for option strategy development and analysis.
+Empower your options trading with OptionStratLib – the all-in-one solution for option strategy development and analysis.
+
+
+## Recent Updates
+
+### Implementation of the Binomial Model for Option Pricing
+
+We have successfully implemented a robust binomial model for option pricing. This implementation includes:
+
+1. **Flexible Option Types**: Our model now supports various option types including European, American, and exotic options like Asian, Barrier, and more.
+
+2. **Comprehensive Pricing Parameters**: We've introduced a `BinomialPricingParams` struct that encapsulates all necessary parameters for option pricing, including asset price, volatility, interest rate, strike price, time to expiry, number of steps, option type, option style (Call/Put), and trade side (Long/Short).
+
+3. **Efficient Pricing Algorithm**: The `price_binomial` function implements an efficient binomial tree algorithm for option pricing. It handles special cases such as zero time to expiry and zero volatility.
+
+4. **Support for Both Call and Put Options**: Our implementation allows pricing of both call and put options through the `OptionStyle` enum.
+
+5. **Long and Short Positions**: The model accounts for both long and short positions in options through the `Side` enum.
+
+6. **Payoff Trait**: We've introduced a `Payoff` trait that allows for easy extension to new option types in the future.
+
+7. **Comprehensive Test Suite**: We've developed a suite of unit tests to ensure the accuracy of our pricing model under various scenarios, including edge cases.
+
+8. **Code Optimization**: We've addressed Clippy warnings and optimized our code for better performance and readability.
+
+9. **Detailed Documentation**: We've added comprehensive documentation to our main pricing function, explaining its usage, parameters, and providing examples.
+
+### Future Work
+
+- Implement additional exotic option types
+- Enhance the model to handle dividends
+- Develop a user-friendly interface for easy option pricing
+- Implement additional pricing models (e.g., Black-Scholes, Monte Carlo) for comparison
+

examples/option_graph.rs

@@ -0,0 +1,37 @@
+/******************************************************************************
+   Author: Joaquín Béjar García
+   Email: jb@taunais.com
+   Date: 20/8/24
+******************************************************************************/
+use optionstratlib::model::option::Options;
+use optionstratlib::model::types::{ExpirationDate, OptionStyle, OptionType, Side};
+use optionstratlib::visualization::utils::Graph;
+use std::error::Error;
+
+fn create_sample_option() -> Options {
+    Options::new(
+        OptionType::European,
+        Side::Long,
+        "AAPL".to_string(),
+        100.0,
+        ExpirationDate::Days(30.0),
+        0.2,
+        1,
+        105.0,
+        0.05,
+        OptionStyle::Call,
+        0.0,
+        None,
+    )
+}
+fn main() -> Result<(), Box<dyn Error>> {
+    let option = create_sample_option();
+
+    // Define a range of prices for the graph
+    let price_range: Vec<f64> = (50..150).map(|x| x as f64).collect();
+
+    // Generate the intrinsic value graph
+    option.graph(&price_range, "Draws/Options/intrinsic_value_chart.png")?;
+
+    Ok(())
+}

examples/position_graph.rs

@@ -0,0 +1,37 @@
+/******************************************************************************
+   Author: Joaquín Béjar García
+   Email: jb@taunais.com
+   Date: 20/8/24
+******************************************************************************/
+use chrono::Utc;
+use optionstratlib::model::option::Options;
+use optionstratlib::model::position::Position;
+use optionstratlib::model::types::{ExpirationDate, OptionStyle, OptionType, Side};
+use optionstratlib::visualization::utils::Graph;
+use std::error::Error;
+
+fn create_sample_option() -> Options {
+    Options::new(
+        OptionType::European,
+        Side::Long,
+        "AAPL".to_string(),
+        100.0,
+        ExpirationDate::Days(30.0),
+        0.2,
+        10,
+        105.0,
+        0.05,
+        OptionStyle::Call,
+        0.0,
+        None,
+    )
+}
+fn main() -> Result<(), Box<dyn Error>> {
+    let position = Position::new(create_sample_option(), 5.71, Utc::now(), 1.0, 1.0);
+
+    let price_range: Vec<f64> = (50..150).map(|x| x as f64).collect();
+
+    position.graph(&price_range, "Draws/Position/pnl_at_expiration_chart.png")?;
+
+    Ok(())
+}

examples/risk-free-rate.rs

@@ -0,0 +1,66 @@
+use optionstratlib::greeks::utils::big_n;
+
+fn black_scholes_call(s: f64, k: f64, t: f64, r: f64, sigma: f64) -> f64 {
+    let d1 = (s.ln() / k + (r + sigma * sigma / 2.0) * t) / (sigma * t.sqrt());
+    let d2 = d1 - sigma * t.sqrt();
+
+    s * big_n(d1) - k * (-r * t).exp() * big_n(d2)
+}
+
+fn implied_risk_free_rate(
+    s: f64,
+    k: f64,
+    t: f64,
+    sigma: f64,
+    market_price: f64,
+    epsilon: f64,
+    max_iterations: usize,
+) -> Option<f64> {
+    let mut r_low = -1.0;
+    let mut r_high = 1.0;
+
+    for _ in 0..max_iterations {
+        let r_mid = (r_low + r_high) / 2.0;
+        let price = black_scholes_call(s, k, t, r_mid, sigma);
+
+        if (price - market_price).abs() < epsilon {
+            return Some(r_mid);
+        }
+
+        if price > market_price {
+            r_high = r_mid;
+        } else {
+            r_low = r_mid;
+        }
+    }
+
+    None
+}
+
+fn main() {
+    let s = 2476.6; // Current gold price
+    let k = 2470.0; // Strike price
+    let t = 3.0 / 365.0; // Time to expiration in years
+    let sigma = 0.216875; // Volatility
+    let market_price = 22.5; // Market price of the option
+    let epsilon = 0.0001; // Desired precision
+    let max_iterations = 1000;
+
+    match implied_risk_free_rate(s, k, t, sigma, market_price, epsilon, max_iterations) {
+        Some(r) => println!("The implied risk-free rate is: {:.4}%", r * 100.0),
+        None => println!("Could not converge to a solution"),
+    }
+
+    // Test with the other set of parameters
+    let k2 = 2390.0;
+    let sigma2 = 0.199375;
+    let market_price2 = 87.8;
+
+    match implied_risk_free_rate(s, k2, t, sigma2, market_price2, epsilon, max_iterations) {
+        Some(r) => println!(
+            "The implied risk-free rate for the second set is: {:.4}%",
+            r * 100.0
+        ),
+        None => println!("Could not converge to a solution for the second set"),
+    }
+}