// SPDX-License-Identifier: GPL-3.0-or-later
pragma solidity ^0.8.19;

import "forge-std/Test.sol";
import {TestEnvironment} from "../test/helpers/TestBase.sol";
import {IUniswapV3Pool} from "@uniswap/v3-core/contracts/interfaces/IUniswapV3Pool.sol";
import {IUniswapV3Factory} from "@uniswap/v3-core/contracts/interfaces/IUniswapV3Factory.sol";
import {IWETH9} from "../src/interfaces/IWETH9.sol";
import {Kraiken} from "../src/Kraiken.sol";
import {Stake} from "../src/Stake.sol";
import {LiquidityManager} from "../src/LiquidityManager.sol";
import {TickMath} from "@aperture/uni-v3-lib/TickMath.sol";
import {ThreePositionStrategy} from "../src/abstracts/ThreePositionStrategy.sol";
import "../test/mocks/BullMarketOptimizer.sol";
import "../test/mocks/NeutralMarketOptimizer.sol";
import "../test/mocks/BearMarketOptimizer.sol";
import "../test/mocks/WhaleOptimizer.sol";
import "../test/mocks/MockOptimizer.sol";
import "../test/mocks/RandomScenarioOptimizer.sol";
import "./helpers/CSVManager.sol";
import "./helpers/SwapExecutor.sol";

/**
 * @title FuzzingAnalysis
 * @notice Fuzzing analysis to find profitable trading scenarios against LiquidityManager
 * @dev Configurable via environment variables:
 *      - FUZZING_RUNS: Number of fuzzing iterations per market (default 100)
 *      - TRACK_POSITIONS: Track detailed position data (default false)
 */
contract FuzzingAnalysis is Test, CSVManager {
    TestEnvironment testEnv;
    IUniswapV3Factory factory;
    IUniswapV3Pool pool;
    IWETH9 weth;
    Kraiken harberg;
    Stake stake;
    LiquidityManager lm;
    bool token0isWeth;
    
    address account = makeAddr("trader");
    address feeDestination = makeAddr("fees");
    
    // Analysis metrics
    uint256 public scenariosAnalyzed;
    uint256 public profitableScenarios;
    
    // Configuration
    uint256 public fuzzingRuns;
    bool public trackPositions;
    string public optimizerClass;
    uint256 public tradesPerRun;
    
    // Optimizers
    BullMarketOptimizer bullOptimizer;
    NeutralMarketOptimizer neutralOptimizer;
    BearMarketOptimizer bearOptimizer;
    WhaleOptimizer whaleOptimizer;
    MockOptimizer mockOptimizer;
    RandomScenarioOptimizer randomOptimizer;
    
    function run() public {
        _loadConfiguration();
        
        console.log("=== Fuzzing Analysis ===");
        console.log(string.concat("Optimizer: ", optimizerClass));
        console.log(string.concat("Fuzzing runs: ", vm.toString(fuzzingRuns)));
        console.log(string.concat("Trades per run: ", vm.toString(tradesPerRun)));
        console.log(string.concat("Position tracking: ", trackPositions ? "enabled" : "disabled"));
        console.log("");
        
        testEnv = new TestEnvironment(feeDestination);
        
        // Get optimizer based on class name
        address optimizerAddress = _getOptimizerByClass(optimizerClass);
        
        // Initialize CSV for profitable scenarios
        string memory profitableCSV = "Scenario,Seed,Initial Balance,Final Balance,Profit,Profit %\n";
        uint256 profitableCount;
        uint256 marketProfitable = 0;
        
        console.log(string.concat("=== FUZZING with ", optimizerClass, " ==="));
        
        for (uint256 seed = 0; seed < fuzzingRuns; seed++) {
                if (seed % 10 == 0 && seed > 0) {
                    console.log(string.concat("Progress: ", vm.toString(seed), "/", vm.toString(fuzzingRuns)));
                }
                
                // Create fresh environment for each run
                (factory, pool, weth, harberg, stake, lm,, token0isWeth) = 
                    testEnv.setupEnvironmentWithOptimizer(seed % 2 == 0, feeDestination, optimizerAddress);
                
                // Fund account with random amount (10-50 ETH)
                uint256 fundAmount = 10 ether + (uint256(keccak256(abi.encodePacked(seed, "fund"))) % 40 ether);
                vm.deal(account, fundAmount * 2);
                vm.prank(account);
                weth.deposit{value: fundAmount}();
                
                uint256 initialBalance = weth.balanceOf(account);
                
                // Initial recenter
                vm.warp(block.timestamp + 5 hours);
                vm.prank(feeDestination);
                try lm.recenter() {} catch {}
                
                // Run trading scenario
                uint256 finalBalance = _runFuzzedScenario(optimizerClass, seed);
                
                scenariosAnalyzed++;
                
                // Check profitability
                if (finalBalance > initialBalance) {
                    profitableScenarios++;
                    marketProfitable++;
                    
                    uint256 profit = finalBalance - initialBalance;
                    uint256 profitPercentage = (profit * 100) / initialBalance;
                    
                    console.log(string.concat("PROFITABLE! Seed: ", vm.toString(seed), " Profit: ", vm.toString(profitPercentage), "%"));
                    
                    // Add to CSV
                    profitableCSV = string.concat(
                        profitableCSV,
                        optimizerClass, ",",
                        vm.toString(seed), ",",
                        vm.toString(initialBalance), ",",
                        vm.toString(finalBalance), ",",
                        vm.toString(profit), ",",
                        vm.toString(profitPercentage), "\n"
                    );
                    profitableCount++;
                }
            }
            
            console.log(string.concat("\nResults for ", optimizerClass, ":"));
            console.log(string.concat("Profitable: ", vm.toString(marketProfitable), "/", vm.toString(fuzzingRuns)));
            console.log("");
        
        console.log("=== ANALYSIS COMPLETE ===");
        console.log(string.concat("Total scenarios analyzed: ", vm.toString(scenariosAnalyzed)));
        console.log(string.concat("Total profitable scenarios: ", vm.toString(profitableScenarios)));
        
        // Write profitable scenarios CSV if any found
        if (profitableCount > 0) {
            console.log("Writing profitable scenarios CSV...");
            string memory filename = string.concat("profitable_scenarios_", vm.toString(block.timestamp), ".csv");
            vm.writeFile(filename, profitableCSV);
            console.log(string.concat("\nProfitable scenarios written to: ", filename));
        } else {
            console.log("\nNo profitable scenarios found.");
        }
        
        console.log("Script execution complete.");
    }
    
    function _loadConfiguration() internal {
        fuzzingRuns = vm.envOr("FUZZING_RUNS", uint256(100));
        trackPositions = vm.envOr("TRACK_POSITIONS", false);
        optimizerClass = vm.envOr("OPTIMIZER_CLASS", string("BullMarketOptimizer"));
        tradesPerRun = vm.envOr("TRADES_PER_RUN", uint256(20));
    }
    
    function _runFuzzedScenario(string memory scenarioName, uint256 seed) internal returns (uint256) {
        // Initialize position tracking CSV if enabled
        if (trackPositions) {
            initializePositionsCSV();
            _recordPositionData("Initial");
        }
        
        // Use seed for randomness
        uint256 rand = uint256(keccak256(abi.encodePacked(seed, scenarioName, block.timestamp)));
        
        // Use configured number of trades (with some randomness)
        uint256 numTrades = tradesPerRun + (rand % 11) - 5; // +/- 5 trades
        if (numTrades < 5) numTrades = 5; // Minimum 5 trades
        
        // Initial buy if no HARB
        if (harberg.balanceOf(account) == 0 && weth.balanceOf(account) > 0) {
            uint256 initialBuy = weth.balanceOf(account) / 10;
            _executeBuy(initialBuy);
        }
        
        // Execute random trades
        for (uint256 i = 0; i < numTrades; i++) {
            rand = uint256(keccak256(abi.encodePacked(rand, i)));
            uint256 action = rand % 100;
            
            if (action < 40) { // 40% chance buy
                uint256 wethBal = weth.balanceOf(account);
                if (wethBal > 0) {
                    uint256 buyPercent = 1 + (rand % 1000); // 0.1% to 100%
                    uint256 buyAmount = (wethBal * buyPercent) / 1000;
                    if (buyAmount > 0) _executeBuy(buyAmount);
                }
            } else if (action < 80) { // 40% chance sell
                uint256 harbBal = harberg.balanceOf(account);
                if (harbBal > 0) {
                    uint256 sellPercent = 1 + (rand % 1000); // 0.1% to 100%
                    uint256 sellAmount = (harbBal * sellPercent) / 1000;
                    if (sellAmount > 0) _executeSell(sellAmount);
                }
            } else if (action < 95) { // 15% chance recenter
                uint256 waitTime = 1 minutes + (rand % 10 hours);
                vm.warp(block.timestamp + waitTime);
                vm.prank(feeDestination);
                try lm.recenter() {
                    if (trackPositions) {
                        _recordPositionData(string.concat("Recenter_", vm.toString(i)));
                    }
                } catch {}
            } else { // 5% chance wait
                vm.warp(block.timestamp + 1 minutes + (rand % 2 hours));
            }
            
            // Skip trades at extreme ticks
            (, int24 currentTick, , , , , ) = pool.slot0();
            if (currentTick < -887000 || currentTick > 887000) continue;
        }
        
        // Sell remaining HARB
        uint256 finalHarb = harberg.balanceOf(account);
        if (finalHarb > 0) _executeSell(finalHarb);
        
        // Final recenters
        for (uint256 j = 0; j < 1 + (rand % 3); j++) {
            vm.warp(block.timestamp + 5 hours);
            vm.prank(feeDestination);
            try lm.recenter() {} catch {}
        }
        
        // Write position tracking CSV if enabled
        if (trackPositions) {
            string memory positionFilename = string.concat(
                "positions_", scenarioName, "_", vm.toString(seed), ".csv"
            );
            writeCSVToFile(positionFilename);
        }
        
        return weth.balanceOf(account);
    }
    
    function _executeBuy(uint256 amount) internal {
        if (amount == 0 || weth.balanceOf(account) < amount) return;
        
        SwapExecutor executor = new SwapExecutor(pool, weth, harberg, token0isWeth);
        vm.prank(account);
        weth.transfer(address(executor), amount);
        
        try executor.executeBuy(amount, account) {} catch {}
    }
    
    function _executeSell(uint256 amount) internal {
        if (amount == 0 || harberg.balanceOf(account) < amount) return;
        
        SwapExecutor executor = new SwapExecutor(pool, weth, harberg, token0isWeth);
        vm.prank(account);
        harberg.transfer(address(executor), amount);
        
        try executor.executeSell(amount, account) {} catch {}
    }
    
    function _getOrCreateOptimizer(uint256 index) internal returns (address) {
        if (index == 0) {
            if (address(bullOptimizer) == address(0)) bullOptimizer = new BullMarketOptimizer();
            return address(bullOptimizer);
        } else if (index == 1) {
            if (address(neutralOptimizer) == address(0)) neutralOptimizer = new NeutralMarketOptimizer();
            return address(neutralOptimizer);
        } else {
            if (address(bearOptimizer) == address(0)) bearOptimizer = new BearMarketOptimizer();
            return address(bearOptimizer);
        }
    }
    
    function _getOptimizerByClass(string memory className) internal returns (address) {
        bytes32 classHash = keccak256(abi.encodePacked(className));
        
        if (classHash == keccak256(abi.encodePacked("BullMarketOptimizer"))) {
            if (address(bullOptimizer) == address(0)) bullOptimizer = new BullMarketOptimizer();
            return address(bullOptimizer);
        } else if (classHash == keccak256(abi.encodePacked("NeutralMarketOptimizer"))) {
            if (address(neutralOptimizer) == address(0)) neutralOptimizer = new NeutralMarketOptimizer();
            return address(neutralOptimizer);
        } else if (classHash == keccak256(abi.encodePacked("BearMarketOptimizer"))) {
            if (address(bearOptimizer) == address(0)) bearOptimizer = new BearMarketOptimizer();
            return address(bearOptimizer);
        } else if (classHash == keccak256(abi.encodePacked("WhaleOptimizer"))) {
            if (address(whaleOptimizer) == address(0)) whaleOptimizer = new WhaleOptimizer();
            return address(whaleOptimizer);
        } else if (classHash == keccak256(abi.encodePacked("MockOptimizer"))) {
            if (address(mockOptimizer) == address(0)) {
                mockOptimizer = new MockOptimizer();
                mockOptimizer.initialize(address(harberg), address(stake));
            }
            return address(mockOptimizer);
        } else if (classHash == keccak256(abi.encodePacked("RandomScenarioOptimizer"))) {
            if (address(randomOptimizer) == address(0)) randomOptimizer = new RandomScenarioOptimizer();
            return address(randomOptimizer);
        } else {
            revert(string.concat("Unknown optimizer class: ", className, ". Available: BullMarketOptimizer, NeutralMarketOptimizer, BearMarketOptimizer, WhaleOptimizer, MockOptimizer, RandomScenarioOptimizer"));
        }
    }
    
    function _recordPositionData(string memory label) internal {
        (,int24 currentTick,,,,,) = pool.slot0();
        
        // Get each position
        (uint128 floorLiq, int24 floorLower, int24 floorUpper) = lm.positions(ThreePositionStrategy.Stage.FLOOR);
        (uint128 anchorLiq, int24 anchorLower, int24 anchorUpper) = lm.positions(ThreePositionStrategy.Stage.ANCHOR);
        (uint128 discoveryLiq, int24 discoveryLower, int24 discoveryUpper) = lm.positions(ThreePositionStrategy.Stage.DISCOVERY);
        
        // Create position data row
        string memory row = string.concat(
            label, ",",
            vm.toString(currentTick), ",",
            vm.toString(floorLiq), ",",
            vm.toString(floorLower), ",",
            vm.toString(floorUpper), ",",
            vm.toString(anchorLiq), ",",
            vm.toString(anchorLower), ",",
            vm.toString(anchorUpper), ",",
            vm.toString(discoveryLiq), ",",
            vm.toString(discoveryLower), ",",
            vm.toString(discoveryUpper), ",",
            vm.toString(weth.balanceOf(address(lm))), ",",
            vm.toString(harberg.balanceOf(address(lm)))
        );
        appendCSVRow(row);
    }
}