Quantitative Finance & High-Frequency Trading

package marketdata

import (
	"encoding/binary"
	"fmt"
	"log"
	"os"
	"path/filepath"
	"sync"
	"time"
	
	"github.com/edsrzf/mmap-go"
)

const (
	// Buffer size to allocate initially for each file
	BufferSize = 64 * 1024 * 1024 // 64MB
	
	// Data type constants
	DataTypeTrade     = 1
	DataTypeQuote     = 2
	DataTypeOrderBook = 3
)

// TickData represents a piece of market data
type TickData interface {
	Symbol() string
	Timestamp() time.Time
	Type() int
	Serialize() []byte
}

// TradeData represents a trade
type TradeData struct {
	symbol    string
	timestamp time.Time
	price     float64
	volume    int64
}

func (t *TradeData) Symbol() string       { return t.symbol }
func (t *TradeData) Timestamp() time.Time { return t.timestamp }
func (t *TradeData) Type() int            { return DataTypeTrade }

func (t *TradeData) Serialize() []byte {
	// Implementation to serialize trade data
	// ...
	return []byte{}
}

// OrderBookData represents an order book snapshot
type OrderBookData struct {
	symbol    string
	timestamp time.Time
	bids      [][2]float64 // Price, Size pairs
	asks      [][2]float64 // Price, Size pairs
}

func (b *OrderBookData) Symbol() string       { return b.symbol }
func (b *OrderBookData) Timestamp() time.Time { return b.timestamp }
func (b *OrderBookData) Type() int            { return DataTypeOrderBook }

func (b *OrderBookData) Serialize() []byte {
	// Implementation to serialize order book data
	// ...
	return []byte{}
}

// TickWriter handles writing market data to memory-mapped files
type TickWriter struct {
	dataDir     string
	mmapBuffers map[string]mmap.MMap
	fileHandles map[string]*os.File
	positions   map[string]int64
	lock        sync.Mutex
}

func NewTickWriter(dataDir string) (*TickWriter, error) {
	// Ensure data directory exists
	if err := os.MkdirAll(dataDir, 0755); err != nil {
		return nil, fmt.Errorf("failed to create data directory: %v", err)
	}
	
	return &TickWriter{
		dataDir:     dataDir,
		mmapBuffers: make(map[string]mmap.MMap),
		fileHandles: make(map[string]*os.File),
		positions:   make(map[string]int64),
	}, nil
}

func (w *TickWriter) WriteTick(tick TickData) error {
	w.lock.Lock()
	defer w.lock.Unlock()
	
	// Create file key based on symbol and date
	date := tick.Timestamp().Format("20060102")
	fileKey := fmt.Sprintf("%s_%s", tick.Symbol(), date)
	
	// Get or create memory-mapped buffer
	buf, err := w.getBuffer(fileKey)
	if err != nil {
		return err
	}
	
	// Get current position in file
	pos := w.positions[fileKey]
	
	// Check if we need to resize the memory-mapped region
	if pos+1024 > int64(len(buf)) {
		if err := w.resizeBuffer(fileKey); err != nil {
			return err
		}
		buf = w.mmapBuffers[fileKey]
	}
	
	// Serialize the tick data
	data := tick.Serialize()
	
	// Write length prefix and data
	binary.LittleEndian.PutUint32(buf[pos:], uint32(len(data)))
	copy(buf[pos+4:], data)
	
	// Update position
	w.positions[fileKey] = pos + 4 + int64(len(data))
	
	return nil
}

# Julia backtesting framework for quantitative trading
  using DataFrames, Dates, Statistics, UUIDs
  
  # Abstract strategy interface
abstract type TradingStrategy end

  # Performance metrics type
struct StrategyResult
    positions::DataFrame
    trades::DataFrame
    equity_curve::Vector{Float64}
    metrics::Dict{Symbol, Any}
end

# Simple moving average crossover strategy
mutable struct MACrossStrategy <: TradingStrategy
    fast_period::Int
    slow_period::Int
    position::Int
    last_trade_price::Float64
    
    MACrossStrategy(fast::Int, slow::Int) = new(fast, slow, 0, 0.0)
end

# Event type for backtesting
abstract type MarketEvent end

struct TickEvent <: MarketEvent
    symbol::String
    timestamp::DateTime
    price::Float64
    volume::Int
end

struct BarEvent <: MarketEvent
    symbol::String
    timestamp::DateTime
    open::Float64
    high::Float64
    low::Float64
    close::Float64
    volume::Int
end

# Order and execution events
struct Order
    symbol::String
    timestamp::DateTime
    qty::Int
    order_type::Symbol  # :market or :limit
    limit_price::Float64
    id::String
    
    # Constructor for market orders
    Order(symbol::String, timestamp::DateTime, qty::Int) = 
        new(symbol, timestamp, qty, :market, 0.0, string(uuid4()))
    
    # Constructor for limit orders
    Order(symbol::String, timestamp::DateTime, qty::Int, price::Float64) = 
        new(symbol, timestamp, qty, :limit, price, string(uuid4()))
end

struct Execution
    order_id::String
    timestamp::DateTime
    symbol::String
    qty::Int
    price::Float64
    commission::Float64
end

# Market model for realistic execution simulation
mutable struct MarketImpactModel
    spread_bps::Float64
    impact_factor::Float64
    
    MarketImpactModel(spread_bps::Float64 = 1.0, impact_factor::Float64 = 0.1) = 
        new(spread_bps, impact_factor)
end

function execute_order(model::MarketImpactModel, order::Order, market_price::Float64, avg_vol::Float64)
    # Simulate market impact and slippage
    half_spread = market_price * model.spread_bps / 10000.0
    
    # Direction of order
    direction = sign(order.qty)
    
    # Market impact based on order size relative to average volume
    impact = direction * model.impact_factor * market_price * (abs(order.qty) / avg_vol)^0.5
    
    # Execution price including spread and impact
    execution_price = market_price + direction * half_spread + impact
    
    # Commission (example: fixed bps)
    commission = abs(order.qty * execution_price) * 0.0001
    
    return Execution(order.id, order.timestamp, order.symbol, order.qty, execution_price, commission)
end

# Backtesting engine
mutable struct BacktestEngine
    data::DataFrame
    strategy::TradingStrategy
    initial_capital::Float64
    market_impact::MarketImpactModel
    positions::DataFrame
    trades::DataFrame
    equity_curve::Vector{Float64}
    
    function BacktestEngine(data::DataFrame, strategy::TradingStrategy, capital::Float64 = 100000.0)
        # Initialize positions and trades tables
        positions = DataFrame(
            timestamp = DateTime[], 
            symbol = String[], 
            quantity = Int[], 
            entry_price = Float64[],
            current_price = Float64[],
            pnl = Float64[]
        )
        
        trades = DataFrame(
            timestamp = DateTime[],
            symbol = String[],
            quantity = Int[],
            price = Float64[],
            commission = Float64[],
            pnl = Float64[]
        )
        
        return new(
            data, 
            strategy, 
            capital, 
            MarketImpactModel(), 
            positions, 
            trades, 
            [capital]
        )
    end
  end