README
¶
Finalytics Go Binding
Finalytics is a high-performance Go binding for the Finalytics Rust library, designed for retrieving financial data, security analysis, and portfolio optimization. It provides a fast, modular interface for advanced analytics, and powers dashboards and applications across platforms.
🚀 Installation
To install the Finalytics Go binding, add it to your Go project using:
go get github.com/Nnamdi-sys/finalytics/go/finalytics
After installing the Go module, download the required native binary by running:
curl -O https://raw.githubusercontent.com/Nnamdi-sys/finalytics/refs/heads/main/go/download_binaries.sh
bash download_binaries.sh
🐹 Main Modules
Finalytics Go exposes five core modules for financial analytics:
1. Screener
Efficiently filter and rank securities using advanced metrics and custom filters.
Usage Example:
screener, err := finalytics.NewScreenerBuilder().
QuoteType("EQUITY").
AddFilter(`{"operator":"eq","operands":["exchange","NMS"]}`).
AddFilter(`{"operator":"eq","operands":["sector","Technology"]}`).
AddFilter(`{"operator":"gte","operands":["intradaymarketcap",10000000000]}`).
AddFilter(`{"operator":"gte","operands":["returnonequity.lasttwelvemonths",0.15]}`).
SortField("intradaymarketcap").
SortDescending(true).
Offset(0).
Size(10).
Build()
if err != nil {
panic(err)
}
defer screener.Free()
screener.Display()
symbols, _ := screener.Symbols()
fmt.Println("Symbols:", symbols)
2. Ticker
Analyze a single security in depth: performance, financials, options, news, and more.
Usage Example:
ticker, err := finalytics.NewTickerBuilder().
Symbol("AAPL").
StartDate("2023-01-01").
EndDate("2024-12-31").
Interval("1d").
BenchmarkSymbol("^GSPC").
ConfidenceLevel(0.95).
RiskFreeRate(0.02).
Build()
if err != nil {
panic(err)
}
defer ticker.Free()
for _, reportType := range []string{"performance", "financials", "options", "news"} {
report, err := ticker.Report(reportType)
if err == nil {
report.Show()
}
}
3. Tickers
Work with multiple securities at once—aggregate reports, batch analytics, and portfolio construction.
Usage Example:
tickers, err := finalytics.NewTickersBuilder().
Symbols([]string{"NVDA", "GOOG", "AAPL", "MSFT", "BTC-USD"}).
StartDate("2023-01-01").
EndDate("2024-12-31").
Interval("1d").
BenchmarkSymbol("^GSPC").
ConfidenceLevel(0.95).
RiskFreeRate(0.02).
Build()
if err != nil {
panic(err)
}
defer tickers.Free()
report, err := tickers.Report("performance")
if err == nil {
report.Show()
}
4. Portfolio
Optimize and analyze portfolios using advanced objective functions and constraints. Supports rebalancing strategies, scheduled cash flows (DCA), ad-hoc transactions, and out-of-sample evaluation.
Objective Functions:
max_sharpe, max_sortino, max_return, min_vol, min_var, min_cvar, min_drawdown,
risk_parity, max_diversification, hierarchical_risk_parity
Usage Example: Optimization with Out-of-Sample Evaluation
import "encoding/json"
// Optimize on 2023 - 2024 data (in-sample)
portfolio, err := finalytics.NewPortfolioBuilder().
TickerSymbols([]string{"NVDA", "GOOG", "AAPL", "MSFT", "BTC-USD"}).
BenchmarkSymbol("^GSPC").
StartDate("2023-01-01").
EndDate("2024-12-31").
Interval("1d").
ConfidenceLevel(0.95).
RiskFreeRate(0.02).
ObjectiveFunction("max_sharpe").
Build()
if err != nil {
panic(err)
}
defer portfolio.Free()
report, _ := portfolio.Report("optimization")
report.Show()
// Update to 2025 data for out-of-sample evaluation
portfolio.UpdateDates("2025-01-01", "2026-01-01")
portfolio.PerformanceStats()
report, _ = portfolio.Report("performance")
report.Show()
Usage Example: Explicit Allocation with Rebalancing and DCA
import "encoding/json"
weights, _ := json.Marshal([]float64{25000.0, 25000.0, 25000.0, 25000.0})
rebalance, _ := json.Marshal(map[string]interface{}{
"type": "calendar",
"frequency": "quarterly",
})
cashFlows, _ := json.Marshal([]map[string]interface{}{
{
"amount": 2000.0,
"frequency": "monthly",
"start_date": nil,
"end_date": nil,
"allocation": "pro_rata",
},
})
portfolio, err := finalytics.NewPortfolioBuilder().
TickerSymbols([]string{"AAPL", "MSFT", "NVDA", "BTC-USD"}).
BenchmarkSymbol("^GSPC").
StartDate("2023-01-01").
EndDate("2024-12-31").
Interval("1d").
ConfidenceLevel(0.95).
RiskFreeRate(0.02).
Weights(string(weights)).
RebalanceStrategy(string(rebalance)).
ScheduledCashFlows(string(cashFlows)).
Build()
if err != nil {
panic(err)
}
defer portfolio.Free()
report, _ := portfolio.Report("performance")
report.Show()
Usage Example: Optimization with Weight & Categorical Constraints
import "encoding/json"
// Per-asset bounds: [lower, upper] in the same order as ticker_symbols
assetConstraints, _ := json.Marshal([][]float64{
{0.05, 0.40}, // AAPL
{0.05, 0.40}, // MSFT
{0.05, 0.40}, // NVDA
{0.05, 0.30}, // JPM
{0.05, 0.20}, // XOM
{0.05, 0.25}, // BTC-USD
})
categoricalConstraints, _ := json.Marshal([]map[string]interface{}{
{
"name": "Sector",
"category_per_symbol": []string{"Tech", "Tech", "Tech", "Finance", "Energy", "Crypto"},
"weight_per_category": [][]interface{}{
{"Tech", 0.30, 0.60},
{"Finance", 0.05, 0.30},
{"Energy", 0.05, 0.20},
{"Crypto", 0.05, 0.25},
},
},
{
"name": "Asset Class",
"category_per_symbol": []string{"Equity", "Equity", "Equity", "Equity", "Equity", "Crypto"},
"weight_per_category": [][]interface{}{
{"Equity", 0.70, 0.95},
{"Crypto", 0.05, 0.30},
},
},
})
portfolio, err := finalytics.NewPortfolioBuilder().
TickerSymbols([]string{"AAPL", "MSFT", "NVDA", "JPM", "XOM", "BTC-USD"}).
BenchmarkSymbol("^GSPC").
StartDate("2023-01-01").
EndDate("2024-12-31").
Interval("1d").
ConfidenceLevel(0.95).
RiskFreeRate(0.02).
ObjectiveFunction("max_sharpe").
AssetConstraints(string(assetConstraints)).
CategoricalConstraints(string(categoricalConstraints)).
Build()
if err != nil {
panic(err)
}
defer portfolio.Free()
report, _ := portfolio.Report("optimization")
report.Show()
5. Custom Data
Load your own price data from CSV files as DataFrames and use it with any Finalytics module.
CSV files must have columns: timestamp (unix epoch), open, high, low, close, volume, adjclose.
Usage Example:
import (
"os"
"github.com/go-gota/gota/dataframe"
)
// Load data from CSV files
files := map[string]string{
"aapl": "examples/datasets/aapl.csv",
"msft": "examples/datasets/msft.csv",
"nvda": "examples/datasets/nvda.csv",
"goog": "examples/datasets/goog.csv",
"btcusd": "examples/datasets/btcusd.csv",
"gspc": "examples/datasets/gspc.csv",
}
dataFrames := make(map[string]dataframe.DataFrame)
for name, path := range files {
file, _ := os.Open(path)
defer file.Close()
dataFrames[name] = dataframe.ReadCSV(file)
}
gspc := dataFrames["gspc"]
// Single Ticker from custom data
aaplDF := dataFrames["aapl"]
ticker, err := finalytics.NewTickerBuilder().
Symbol("AAPL").
BenchmarkSymbol("^GSPC").
ConfidenceLevel(0.95).
RiskFreeRate(0.02).
TickerData(&aaplDF).
BenchmarkData(&gspc).
Build()
if err != nil {
panic(err)
}
defer ticker.Free()
report, _ := ticker.Report("performance")
report.Show()
// Multiple Tickers from custom data
tickersData := []dataframe.DataFrame{
dataFrames["nvda"], dataFrames["goog"], dataFrames["aapl"],
dataFrames["msft"], dataFrames["btcusd"],
}
tickers, err := finalytics.NewTickersBuilder().
Symbols([]string{"NVDA", "GOOG", "AAPL", "MSFT", "BTC-USD"}).
BenchmarkSymbol("^GSPC").
ConfidenceLevel(0.95).
RiskFreeRate(0.02).
TickersData(tickersData).
BenchmarkData(&gspc).
Build()
if err != nil {
panic(err)
}
defer tickers.Free()
report, _ = tickers.Report("performance")
report.Show()
// Portfolio optimization from custom data
portfolio, err := finalytics.NewPortfolioBuilder().
TickerSymbols([]string{"NVDA", "GOOG", "AAPL", "MSFT", "BTC-USD"}).
BenchmarkSymbol("^GSPC").
ConfidenceLevel(0.95).
RiskFreeRate(0.02).
ObjectiveFunction("max_sharpe").
TickersData(tickersData).
BenchmarkData(&gspc).
Build()
if err != nil {
panic(err)
}
defer portfolio.Free()
report, _ = portfolio.Report("optimization")
report.Show()
📚 More Documentation
- See the Go API documentation for full details.
🗂️ Multi-language Bindings
Finalytics is also available in:
Finalytics — Modular, high-performance financial analytics for Go.
Documentation
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There is no documentation for this package.
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