Finalytics is a Rust library designed for retrieving financial data and performing security analysis and portfolio optimization.
Add the following to your Cargo.toml file:
[dependencies]
finalytics = {version = "0.4.0", features = ["kaleido"]}Or run the following command:
cargo install finalytics --features kaleidoView the library documentation on docs.rs or visit the homepage
use std::error::Error;
use finalytics::prelude::*;
#[tokio::main]
async fn main() -> Result<(), Box<dyn Error>> {
// Instantiate the Ticker Object
let ticker = TickerBuilder::new().ticker("AAPL")?
.start_date("2023-01-01")
.end_date("2023-02-01")
.interval(Interval::OneDay)
.benchmark_symbol("^GSPC")
.confidence_level(0.95)
.risk_free_rate(0.02)
.build()?;
// Fetch Ticker Data
let quote = ticker.get_quote().await?;
let stats = ticker.get_ticker_stats().await?;
let chart = ticker.get_chart().await?;
let options = ticker.get_options().await?;
println!("{:?}", quote);
println!("{:?}", stats);
println!("{:?}", chart);
println!("{:?}", options);
// Fundamental Analysis
let income_statement = ticker.income_statement().await?;
let balance_sheet = ticker.balance_sheet().await?;
let cash_flow = ticker.cashflow_statement().await?;
let financial_ratios = ticker.financial_ratios().await?;
let performance_stats = ticker.performance_stats().await?;
let volatility_surface = ticker.volatility_surface().await?;
println!("{:?}", income_statement);
println!("{:?}", balance_sheet);
println!("{:?}", cash_flow);
println!("{:?}", financial_ratios);
println!("{:?}", performance_stats);
println!("{:?}", volatility_surface);
// Technical Analysis
let sma = ticker.sma(50).await?;
let ema = ticker.ema(3).await?;
let macd = ticker.macd(12, 26, 9).await?;
let rsi = ticker.rsi(14).await?;
let fs = ticker.fs(14).await?;
let ss = ticker.ss(7, 3).await?;
let ppo = ticker.ppo(12, 26, 9).await?;
let roc = ticker.roc(1).await?;
let mfi = ticker.mfi(14).await?;
let bb = ticker.bb(20, 2.0).await?;
let sd = ticker.sd(20).await?;
let mad = ticker.mad(20).await?;
let atr = ticker.atr(14).await?;
let max = ticker.max(20).await?;
let min = ticker.min(20).await?;
let obv = ticker.obv().await?;
println!("SMA:{:?}\nEMA:{:?}\nMACD:{:?}\nRSI:{:?}\nFS:{:?}\nSS:{:?}\nPPO:{:?}\nROC:{:?}\nMFI:{:?}\
\nBB:{:?}\nSD:{:?}\nMAD:{:?}\nATR:{:?}\nMAX:{:?}\nMIN:{:?}\nOBV:{:?}\n",
sma, ema, macd, rsi, fs, ss, ppo, roc, mfi, bb, sd, mad, atr, max, min, obv);
// News Sentiment Analysis
let news_sentiment = ticker.get_news(true).await?;
println!("{:?}", news_sentiment);
// Display Ticker Charts
let candlestick_chart = ticker.candlestick_chart().await?;
let performance_chart = ticker.performance_chart().await?;
let volatility_charts = ticker.volatility_charts().await?;
let summary_stats_table = ticker.summary_stats_table().await?;
let performance_stats_table = ticker.performance_stats_table().await?;
let financials_tables = ticker.financials_tables().await?;
candlestick_chart.show();
performance_chart.show();
summary_stats_table.show();
performance_stats_table.show();
volatility_charts["Volatility Surface"].show();
volatility_charts["Volatility Smile"].show();
volatility_charts["Volatility Term Structure"].show();
financials_tables["Income Statement"].show();
financials_tables["Balance Sheet"].show();
financials_tables["Cashflow Statement"].show();
financials_tables["Financial Ratios"].show();
Ok(())
}use std::error::Error;
use finalytics::prelude::*;
#[tokio::main]
async fn main() -> Result<(), Box<dyn Error>> {
// Construct the Portfolio Object
let ticker_symbols = Vec::from(["NVDA", "BRK-A", "AAPL", "ZN=F"]);
let portfolio = PortfolioBuilder::new().ticker_symbols(ticker_symbols)
.benchmark_symbol("^GSPC")
.start_date("2017-01-01")
.end_date("2023-01-01")
.interval(Interval::OneDay)
.confidence_level(0.95)
.risk_free_rate(0.02)
.max_iterations(1000)
.objective_function(ObjectiveFunction::MaxSharpe)
.build().await?;
// Display Portfolio Optimization Results
println!("{:?}", portfolio.performance_stats);
// Display Portfolio Analytics Charts
portfolio.optimization_chart()?.show();
portfolio.performance_chart()?.show();
portfolio.asset_returns_chart()?.show();
portfolio.performance_stats_table()?.show();
Ok(())
}This sample application allows you to perform security analysis based on the Finalytics Library.
This sample application enables you to perform portfolio optimization based on the Finalytics Library.
The Finalytics Telegram Bot allows you to perform security analysis, portfolio optimization and news sentiment analysis directly from Telegram.
pip install finalyticsView Library documentation on readthedocs here
from finalytics import Ticker
ticker = Ticker(symbol="AAPL")
print(ticker.get_current_price())
print(ticker.get_summary_stats())
print(ticker.get_price_history(start="2023-01-01", end="2023-10-31", interval="1d"))
print(ticker.get_options_chain())
print(ticker.get_news(compute_sentiment=True))
print(ticker.get_income_statement())
print(ticker.get_balance_sheet())
print(ticker.get_cashflow_statement())
print(ticker.get_financial_ratios())
print(ticker.compute_performance_stats(start="2023-01-01", end="2023-10-31", interval="1d", benchmark="^GSPC",
confidence_level=0.95, risk_free_rate=0.02))
ticker.display_performance_chart(start="2023-01-01", end="2023-10-31", interval="1d", benchmark="^GSPC",
confidence_level=0.95, risk_free_rate=0.02, display_format="notebook")
ticker.display_candlestick_chart(start="2023-01-01", end="2023-10-31", interval="1d", display_format="html")
ticker.display_options_chart(risk_free_rate=0.02, chart_type="surface", display_format="png")from finalytics import Portfolio
portfolio = Portfolio(ticker_symbols=["AAPL", "GOOG", "MSFT", "BTC-USD"],
benchmark_symbol="^GSPC", start_date="2020-01-01", end_date="2022-01-01", interval="1d",
confidence_level=0.95, risk_free_rate=0.02, max_iterations=1000,
objective_function="max_sharpe")
print(portfolio.get_optimization_results())
portfolio.display_portfolio_charts("performance", "html")