Detailed Instructions

First, define the core data structures that will represent the state of your strategy and the blockchain environment. This includes a StrategyState object to track the user's position and a PoolState object to simulate the liquidity pool. The StrategyState should hold the user's token balances (e.g., tokenA, tokenB, lpTokens), total value locked (TVL), and accrued fees. The PoolState must model the pool's reserves, the current liquidity provider (LP) token supply, and the swap fee percentage (commonly 0.3% for Uniswap V2).

• Sub-step 1: Create a PoolState class with attributes: reserveA, reserveB, totalSupply, and fee.
• Sub-step 2: Create a StrategyState class with attributes: balanceA, balanceB, lpBalance, and totalValue.
• Sub-step 3: Initialize a sample pool with reserveA = 1000000, reserveB = 500000, totalSupply = 1000000, and fee = 0.003.

Tip: Use TypeScript interfaces or Python dataclasses for clarity and type safety. These structures are the backbone of all simulation logic.

Detailed Instructions

The backtester's heart is the Automated Market Maker (AMM) mathematics. You must implement the constant product formula x * y = k to calculate swap outputs and LP token minting. Key functions include getOutputAmount for swaps, getLPTokenMintAmount for adding liquidity, and getWithdrawalAmounts for removing liquidity. These functions must account for the protocol fee (if any) and the slippage incurred on large trades. For accuracy, use precise decimal math libraries like decimal.js or Python's Decimal.

• Sub-step 1: Write getOutputAmount(inputReserve, outputReserve, inputAmount, fee=0.003) that returns the output token amount after fees.
• Sub-step 2: Write calculateLPTokens(reserveA, reserveB, amountA, amountB) that returns LP tokens minted based on share of pool.
• Sub-step 3: Write calculateWithdrawal(lpTokens, totalSupply, reserveA, reserveB) to return the amounts of tokenA and tokenB received.

Tip: Thoroughly test these functions with edge cases (e.g., zero inputs, massive swaps) to ensure they mirror real DEX behavior like Uniswap V2.

Detailed Instructions

To simulate realistically, you need historical price data for the token pair. Use a reliable API like CoinGecko, CoinMarketCap, or a blockchain indexer like The Graph. You'll fetch OHLCV (Open, High, Low, Close, Volume) data at a specific interval (e.g., hourly). Store this data in a local CSV or database for efficient access. The simulation will iterate through each time step, updating the simulated pool's reserves based on price movements and volume to reflect impermanent loss and trading fee accrual.

• Sub-step 1: Choose a data source. For a public test, use the free CoinGecko API endpoint: https://api.coingecko.com/api/v3/coins/ethereum/market_chart?vs_currency=usd&days=365.
• Sub-step 2: Write a script to fetch and parse data for your target pair (e.g., ETH/USDC). Save it as price_data.csv.
• Sub-step 3: Create a DataLoader class in your backtester that reads this file and yields {timestamp, price} for each step.

Tip: Cache the data locally to avoid rate limits. Consider using a moving average or volatility derived from this data to generate more realistic simulated swap volumes.

Detailed Instructions

Create the main simulation engine that orchestrates the backtest. This loop will iterate through each historical data point. For each time step, it must: update the pool state (simulating price changes via virtual swaps), apply your yield farming strategy logic (e.g., compound fees, rebalance), and record metrics. The strategy logic is where you define rules, such as "harvest and compound rewards every 24 hours" or "add liquidity when price is within a 5% range." Track key performance indicators (KPIs) like Total Value Locked (TVL), Annual Percentage Yield (APY), and impermanent loss.

• Sub-step 1: Define the main function runBacktest(initialCapital, priceData, strategyFunction).
• Sub-step 2: Inside the loop, call a helper simulateMarketActivity(poolState, priceChange, volume) to adjust reserves.
• Sub-step 3: Call the user-provided strategyFunction(currentState, poolState) to execute deposits, withdrawals, or harvests.
• Sub-step 4: Append the updated StrategyState to a results array for later analysis.

Tip: Start with a simple buy-and-hold strategy as a baseline to compare your farming strategy against. Use console.log or a logging library to output progress every 1000 steps.

Detailed Instructions

Begin by defining the core strategy parameters that will drive the simulation. This includes the initial capital (e.g., INITIAL_CAPITAL_USD = 10000), the specific DeFi protocols and pools to interact with (e.g., Uniswap V3 WETH/USDC pool on Ethereum mainnet at address 0x8ad599c3A0ff1De082011EFDDc58f1908eb6e6D8), and the time period for the backtest (e.g., from block 15000000 to 17000000). Create a structured data model to hold the portfolio state, tracking assets, their quantities, and accrued fees or rewards over time.

• Sub-step 1: Declare constants for the strategy's fixed inputs, such as swap fee percentages and reward token addresses.
• Sub-step 2: Design a Portfolio class with attributes like holdings (a dictionary mapping token addresses to balances) and total_value_usd.
• Sub-step 3: Set up a data fetching mechanism to pull historical price feeds, typically from an archive node or a service like The Graph, for the required tokens.

Tip: Use dataclasses or pydantic models for your data structures to ensure type safety and clean serialization.

Detailed Instructions

The engine's heart is a loop that iterates through historical price and event data chronologically. At each timestep (e.g., daily or per block), the logic must evaluate the current market state and decide on actions. The primary decision is often a rebalancing trigger, such as moving liquidity when the price moves outside a predetermined range or claiming rewards when they reach a threshold. Implement functions like should_rebalance(current_price, range_low, range_high) that return a boolean.

• Sub-step 1: Load your historical DataFrame, ensuring it's sorted by timestamp. Iterate over each row.
• Sub-step 2: At each step, update the portfolio's value based on the new token prices.
• Sub-step 3: Call your strategy's decision function. If it returns true, execute the simulated trade or pool interaction, updating the Portfolio object's holdings.

pythonCopy
for index, row in historical_data.iterrows():
    current_price = row['eth_price']
    portfolio.update_value(current_price)
    if strategy.check_rebalance_condition(current_price):
        portfolio.execute_swap('WETH', 'USDC', amount=100)

Detailed Instructions

Accurately simulate the financial mechanics of yield farming. For a liquidity provision strategy, this involves calculating impermanent loss and fee income. When your logic decides to add liquidity, you must deduct the paired tokens from the portfolio and start tracking the pool share. Use formulas to estimate fees earned based on historical volume data (e.g., a 0.3% fee on a simulated $1M daily volume yields $3). Simultaneously, track any governance token rewards (e.g., UNI or COMP) emitted by the protocol, using known emission rates per block.

• Sub-step 1: Implement a add_liquidity(token_a, token_b, amount_a, amount_b) method that reduces holdings and creates a LiquidityPosition object.
• Sub-step 2: At each step, calculate fees for active positions using: fees = volume * fee_percentage * (my_liquidity_share / total_liquidity).
• Sub-step 3: Calculate impermanent loss by comparing the value of held LP tokens against the value of the initial token amounts if simply held (HODL).

Tip: Store all transactions and state changes in a list for later analysis and to enable a step-by-step replay of the simulation.

Detailed Instructions

After the simulation loop completes, analyze the final portfolio state to compute key performance indicators (KPIs). The most critical metric is the Total Return, calculated as (Final Portfolio Value - Initial Capital) / Initial Capital. Compare this to a simple buy-and-hold benchmark. Also calculate the Sharpe Ratio to assess risk-adjusted returns, using the daily portfolio returns to find the standard deviation. Generate a comprehensive report that includes a equity curve (portfolio value over time) and a breakdown of returns from fees, rewards, and capital gains/losses.

• Sub-step 1: Compute final value and total return. For example: final_value = portfolio.total_value_usd; total_return_pct = ((final_value - 10000) / 10000) * 100.
• Sub-step 2: Generate a pandas Series of daily returns and calculate annualized volatility and Sharpe Ratio (assuming a 0% risk-free rate for simplicity).
• Sub-step 3: Use matplotlib to plot the equity curve versus the benchmark. Export all transaction logs and the final report to a CSV file for further inspection.

pythonCopy
import pandas as pd
returns_series = portfolio_value_history.pct_change().dropna()
sharpe_ratio = (returns_series.mean() / returns_series.std()) * (np.sqrt(365))
print(f'Sharpe Ratio: {sharpe_ratio:.2f}')