32 posts tagged with "beancount"

Are you drowning in crypto transactions across multiple exchanges, struggling with DeFi complexity, or panicking about tax season? You're not alone. The cryptocurrency landscape has exploded from simple Bitcoin purchases to a sophisticated ecosystem of DeFi protocols, staking rewards, yield farming, and cross-chain activities that challenge traditional accounting methods.

Here's the harsh reality: every crypto transaction is potentially taxable, and the IRS is watching. Whether you're a casual Bitcoin holder or a DeFi power user managing positions across dozens of protocols, maintaining accurate financial records isn't optional—it's essential for compliance and financial clarity.

The problem? Traditional accounting software has limited native support for crypto's complexity. While tools like QuickBooks can handle crypto with additional plugins, and Excel can import blockchain data with scripts, most solutions require significant customization for comprehensive crypto accounting.

The solution? Beancount.io's plain-text accounting system built on the powerful open-source Beancount language. Important note: Beancount is an open-source double-entry accounting language created by Martin Blais, while Beancount.io is a commercial hosting service that provides a user-friendly interface and cloud infrastructure for Beancount. This guide covers both the underlying Beancount principles and how to use them effectively through the Beancount.io platform.

The Cryptocurrency Accounting Nightmare (And Why It's Getting Worse)​

Your Crypto Portfolio is Scattered Everywhere​

Let's be honest about your setup. You probably have:

• 3-5 different exchanges (Coinbase for easy buys, Binance for altcoins, Kraken for that one token...)
• Multiple wallets (MetaMask for DeFi, Ledger for long-term holds, that old wallet you forgot about...)
• DeFi positions across 10+ protocols (Uniswap, Compound, Aave, and whatever new yield farm caught your attention)
• Staking rewards trickling in from various validators
• Random airdrops appearing in your wallet like crypto Christmas presents

Each platform speaks a different language. Coinbase gives you a CSV that looks nothing like Binance's export. Uniswap doesn't even have exports. And don't get me started on tracking your DeFi positions across Layer 2 networks.

Transaction Types That Challenge Traditional Accounting​

Your crypto activities include transaction types that traditional accounting systems weren't originally designed to handle:

• Impermanent loss from liquidity providing (try explaining that to QuickBooks)
• Flash loans that borrow and repay millions in a single transaction
• Yield farming where you earn 5 different tokens for providing liquidity
• Cross-chain bridges that make assets disappear on one network and appear on another
• Staking derivatives like stETH that accrue value differently than the underlying asset
• DAO governance tokens that you received for using protocols

The Tax Compliance Minefield​

Here's what keeps crypto investors awake at night:

• Every single trade is taxable (yes, even that ETH-to-USDC swap)
• Cost basis tracking becomes impossible with hundreds of micro-transactions
• Staking rewards are income the moment you receive them (at fair market value)
• DeFi rewards are income even if you can't sell them yet
• The IRS wants Form 8949 with every single transaction listed
• Penalties are severe for getting it wrong

Traditional accounting software requires significant customization for this complexity. While solutions exist, they often require additional plugins, scripts, or manual processes to handle the full scope of cryptocurrency activities.

Enter Beancount.io: The Crypto Accounting Solution You've Been Waiting For​

What if I told you there's an accounting system designed for exactly this chaos? Beancount.io isn't just another accounting tool—it's a plain-text accounting revolution that handles cryptocurrency complexity like it was born for it.

Why Beancount.io Dominates Crypto Accounting​

🔍 Complete Transparency: Every calculation is visible. No black boxes, no "trust us" algorithms. You see exactly how your cost basis is calculated, how gains are computed, and where every satoshi went.

📊 Unlimited Flexibility: Create any account structure you need. Track DeFi positions, staking derivatives, cross-chain assets, or that weird token you got from a DAO vote. If you can imagine it, you can track it.

🎯 Precise Cost Basis: Lot-based tracking with specific identification. Choose exactly which Bitcoin you're selling for optimal tax outcomes. FIFO, LIFO, or cherry-pick your lots—your choice.

🔗 Future-Proof: Plain-text format means your data is yours forever. No vendor lock-in, no proprietary formats, no "sorry, we're shutting down" emails.

⚡ Scriptable Power: Automate imports, generate custom reports, and integrate with any tool. Because your crypto portfolio is unique, your accounting should be too.

Setting Up Your Crypto Command Center​

Building Your Account Architecture​

Think of this as designing the blueprint for your crypto empire. We're creating a structure that can handle everything from your first Bitcoin purchase to complex multi-protocol DeFi strategies:

Note: The examples use 1970-01-01 as a placeholder opening date. For actual use, replace with your real account opening dates, especially if using automated import tools that filter transactions by date.

; Exchange Accounts
1970-01-01 open Assets:Crypto:Coinbase:USD
1970-01-01 open Assets:Crypto:Coinbase:BTC
1970-01-01 open Assets:Crypto:Coinbase:ETH
1970-01-01 open Assets:Crypto:Binance:USD
1970-01-01 open Assets:Crypto:Binance:BTC
1970-01-01 open Assets:Crypto:Binance:ETH
1970-01-01 open Assets:Crypto:Binance:ADA

; Wallet Accounts
1970-01-01 open Assets:Crypto:Wallet:MetaMask:ETH
1970-01-01 open Assets:Crypto:Wallet:MetaMask:USDC
1970-01-01 open Assets:Crypto:Wallet:MetaMask:UNI
1970-01-01 open Assets:Crypto:Wallet:Ledger:BTC
1970-01-01 open Assets:Crypto:Wallet:Ledger:ETH

; DeFi Protocol Accounts
1970-01-01 open Assets:DeFi:Compound:cUSDC
1970-01-01 open Assets:DeFi:Uniswap:ETH-USDC-LP
1970-01-01 open Assets:Staking:Ethereum:ETH
1970-01-01 open Assets:Crypto:Mining:BTC

; Income Accounts
1970-01-01 open Income:Crypto:Staking:ETH
1970-01-01 open Income:Crypto:Mining:BTC
1970-01-01 open Income:Crypto:Airdrops
1970-01-01 open Income:Crypto:DeFi:Yield
1970-01-01 open Income:CapitalGains:Crypto
1970-01-01 open Income:Crypto:Trading:Margin

; Expense Accounts
1970-01-01 open Expenses:Crypto:Fees:Trading
1970-01-01 open Expenses:Crypto:Fees:Network
1970-01-01 open Expenses:Crypto:Fees:Withdrawal

; Additional accounts for advanced scenarios
1970-01-01 open Assets:Crypto:Binance:BTC-Margin
1970-01-01 open Liabilities:Crypto:Binance:Borrowed
1970-01-01 open Liabilities:Crypto:Binance:BTC
1970-01-01 open Assets:Checking
1970-01-01 open Expenses:Crypto:Mining:Electricity

Commodity Definitions​

Define your cryptocurrencies as commodities with proper metadata:

1970-01-01 commodity BTC
  name: "Bitcoin"
  asset-class: "cryptocurrency"
  price-source: "coinbase"

1970-01-01 commodity ETH
  name: "Ethereum"
  asset-class: "cryptocurrency"
  price-source: "coinbase"

1970-01-01 commodity ADA
  name: "Cardano"
  asset-class: "cryptocurrency"
  price-source: "binance"

1970-01-01 commodity USDC
  name: "USD Coin"
  asset-class: "stablecoin"
  price-source: "coinbase"

1970-01-01 commodity UNI
  name: "Uniswap"
  asset-class: "cryptocurrency"
  price-source: "coinbase"

1970-01-01 commodity UNI-V2-ETH-USDC
  name: "Uniswap V2 ETH-USDC LP Token"
  asset-class: "liquidity-pool"

Mastering Crypto Transactions: From Simple to Sophisticated​

Ready to see Beancount.io in action? Let's walk through real-world scenarios that would break other accounting systems but are handled elegantly here.

1. Your First Bitcoin Purchase (The Gateway Drug)​

Every crypto journey starts somewhere. Here's how to record that life-changing moment when you bought your first Bitcoin:

2024-01-15 * "Buy 0.5 BTC on Coinbase"
  Assets:Crypto:Coinbase:BTC     0.5 BTC {30000.00 USD}
  Assets:Crypto:Coinbase:USD  -15025.00 USD
  Expenses:Crypto:Fees:Trading    25.00 USD

2. Taking Profits (Or Cutting Losses) Like a Pro​

The moment of truth: selling your crypto. This is where most accounting systems fall apart, but Beancount.io shines with precise cost basis tracking:

2024-03-20 * "Sell 0.3 BTC on Coinbase"
  Assets:Crypto:Coinbase:BTC    -0.3 BTC {30000.00 USD}
  Assets:Crypto:Coinbase:USD   10470.00 USD
  Expenses:Crypto:Fees:Trading    30.00 USD
  Income:CapitalGains:Crypto   1440.00 USD  ; 10470 - (0.3 × 30000) - 30 = 1440

3. Staking Rewards: Earning While You Sleep​

Passive income at its finest. But remember, the IRS requires staking rewards to be reported as taxable income when received, based on fair market value at the time of receipt (per IRS Digital Assets guidance, 2024). While there is ongoing litigation (Jarrett v. United States), the current official position requires immediate reporting. Here's how to track them properly:

2024-02-01 * "ETH Staking Rewards"
  Assets:Staking:Ethereum:ETH      0.05 ETH {2500.00 USD}
  Income:Crypto:Staking:ETH      125.00 USD

4. DeFi Yield Farming: Where Things Get Spicy 🌶️​

Welcome to advanced DeFi accounting. DeFi yield farming involves complex multi-token transactions that require sophisticated tracking. Here's how to properly account for a Uniswap liquidity position:

; Initial liquidity provision
2024-01-10 * "Add liquidity to ETH-USDC pool"
  Assets:Crypto:Wallet:MetaMask:ETH    -10 ETH {2500.00 USD}
  Assets:Crypto:Wallet:MetaMask:USDC -25000 USDC {1.00 USD}
  Assets:DeFi:Uniswap:ETH-USDC-LP     100 UNI-V2-ETH-USDC {500.00 USD}

; Claiming rewards
2024-02-10 * "Claim Uniswap LP rewards"
  Assets:Crypto:Wallet:MetaMask:UNI    50 UNI {8.00 USD}
  Income:Crypto:DeFi:Yield           400.00 USD

5. Cross-Exchange Transfers​

Moving Bitcoin from Coinbase to Ledger wallet:

2024-01-20 * "Transfer BTC from Coinbase to Ledger"
  Assets:Crypto:Coinbase:BTC      -1.0 BTC {30000.00 USD}
  Assets:Crypto:Wallet:Ledger:BTC  0.9995 BTC {30000.00 USD}
  Expenses:Crypto:Fees:Withdrawal  0.0005 BTC {30000.00 USD}

Advanced Cryptocurrency Scenarios​

Margin Trading​

Recording leveraged positions:

; Opening a leveraged long position (borrowing BTC)
2024-02-15 * "Open 2x leveraged BTC position"
  Assets:Crypto:Binance:BTC-Margin      1.0 BTC {35000.00 USD}
  Liabilities:Crypto:Binance:BTC       -1.0 BTC {35000.00 USD}
  Expenses:Crypto:Fees:Trading            50.00 USD
  Assets:Crypto:Binance:USD              -50.00 USD

; Closing the position with profit
2024-02-20 * "Close leveraged BTC position"
  Assets:Crypto:Binance:BTC-Margin   -1.0 BTC {35000.00 USD}
  Assets:Crypto:Binance:USD          37950.00 USD
  Liabilities:Crypto:Binance:BTC      1.0 BTC {35000.00 USD}
  Income:Crypto:Trading:Margin       -2900.00 USD
  Expenses:Crypto:Fees:Trading          50.00 USD

Airdrops and Forks​

Recording free token distributions:

2024-03-01 * "Received UNI airdrop"
  Assets:Crypto:Wallet:MetaMask:UNI   400 UNI {8.00 USD}
  Income:Crypto:Airdrops             3200.00 USD

Mining Operations​

For cryptocurrency miners:

2024-01-01 * "Bitcoin mining reward"
  Assets:Crypto:Mining:BTC    0.1 BTC {45000.00 USD}
  Income:Crypto:Mining:BTC   4500.00 USD

; Mining expenses
2024-01-31 * "Mining electricity costs"
  Expenses:Crypto:Mining:Electricity  800.00 USD
  Assets:Checking                    -800.00 USD

Tax Reporting and Compliance​

Cost Basis Tracking​

Beancount.io automatically tracks cost basis using the lot-based system:

; Multiple purchases at different prices
2024-01-01 * "Buy BTC batch 1"
  Assets:Crypto:Coinbase:BTC  1.0 BTC {40000.00 USD}
  Assets:Crypto:Coinbase:USD -40000.00 USD

2024-02-01 * "Buy BTC batch 2"
  Assets:Crypto:Coinbase:BTC  1.0 BTC {45000.00 USD}
  Assets:Crypto:Coinbase:USD -45000.00 USD

; Selling specific lots (FIFO, LIFO, or specific identification)
2024-03-01 * "Sell BTC using FIFO"
  Assets:Crypto:Coinbase:BTC    -0.5 BTC {40000.00 USD}
  Assets:Crypto:Coinbase:USD   22470.00 USD
  Expenses:Crypto:Fees:Trading    30.00 USD
  Income:CapitalGains:Crypto    2440.00 USD  ; 22470 - 20000 - 30 = 2440

Generating Tax Reports​

Use Beancount's query system and plugins to generate tax reports:

• Form 8949: Capital gains and losses (via bean-query or third-party plugins)
• Schedule D: Summary of capital gains (requires additional scripting)
• Form 1040: Income from staking, mining, airdrops (via custom reports)
• Detailed transaction logs: For audit purposes

Note: Tax report generation requires additional plugins, scripts, or integration with specialized tax software. Beancount provides the data foundation, but specific tax forms typically require custom development or third-party tools.

Best Practices for Crypto Accounting​

1. Regular Reconciliation​

• Import exchange data weekly
• Verify wallet balances monthly
• Cross-check with blockchain explorers

2. Proper Documentation​

• Save all transaction confirmations
• Document the purpose of each transaction
• Maintain records of market prices at transaction time

3. Separate Business and Personal​

; Personal crypto investments
Assets:Personal:Crypto:Coinbase:BTC

; Business crypto operations
Assets:Business:Crypto:Treasury:BTC

4. Track All Income Sources​

• Staking rewards (taxable as income)
• Mining rewards (taxable as income)
• Airdrops (taxable at fair market value)
• DeFi yield (taxable as income)

5. Fee Management​

Track all fees separately for proper tax deduction:

Expenses:Crypto:Fees:Trading     ; Exchange trading fees
Expenses:Crypto:Fees:Network     ; Blockchain network fees
Expenses:Crypto:Fees:Withdrawal  ; Withdrawal fees

Integration with Popular Crypto Tools​

Exchange API Integration​

Beancount.io can integrate with major exchanges:

• Coinbase Pro API: Automatic transaction import
• Binance API: Real-time balance updates
• Kraken API: Historical data synchronization

Blockchain Analysis​

Connect with blockchain explorers:

• Etherscan: Ethereum transaction verification
• Blockchain.info: Bitcoin transaction tracking
• BscScan: Binance Smart Chain monitoring

Portfolio Tracking​

Sync with portfolio management tools:

• CoinTracker: Tax report generation
• Koinly: Multi-exchange aggregation
• Blockfolio: Mobile portfolio tracking

Important Disclaimers​

Tax and Legal Notice: This guide provides general information about cryptocurrency accounting using Beancount and does not constitute professional tax, legal, or financial advice. Tax treatment of cryptocurrency transactions varies by jurisdiction and individual circumstances. Complex scenarios like cross-chain bridges, impermanent loss, and flash loans may have unclear tax implications under current regulations. For specific guidance on whether transactions constitute taxable dispositions, refer to IRS FAQ Q-15 through Q-18 (2024) or consult local tax regulations. Always consult with a qualified tax professional or CPA familiar with cryptocurrency taxation in your jurisdiction before making tax-related decisions.

Software Clarification: Examples in this guide use standard Beancount syntax. While Beancount.io provides a user-friendly interface for Beancount, the underlying accounting principles apply to any Beancount implementation.

Conclusion​

Cryptocurrency accounting doesn't have to be overwhelming. With Beancount's powerful plain-text accounting system, accessible through Beancount.io's platform, you can:

• Maintain Complete Transparency: Every transaction is visible and auditable
• Ensure Tax Compliance: Proper cost basis tracking and income reporting
• Scale with Your Portfolio: Handle simple trades to complex DeFi strategies
• Integrate Seamlessly: Connect with exchanges, wallets, and tax tools
• Future-Proof Your Records: Plain-text format ensures long-term accessibility

Whether you're a casual Bitcoin holder or a sophisticated DeFi yield farmer, Beancount provides the foundation and flexibility needed to master cryptocurrency accounting. Start with basic transactions and gradually incorporate more complex scenarios as your crypto journey evolves.

The examples in this guide provide a solid foundation, but remember that cryptocurrency accounting is an evolving field. Stay informed about regulatory changes, consult with tax professionals, and adapt your accounting practices as needed.

Ready to take control of your cryptocurrency finances? Sign up for Beancount.io and experience the power of transparent, scriptable crypto accounting built on the robust Beancount foundation.

Cryptocurrency taxation has evolved from a niche concern to a critical compliance requirement for millions of investors. With the IRS increasing enforcement and requiring detailed reporting, accurate record-keeping isn't just good practice—it's essential for avoiding penalties and optimizing your tax liability.

This comprehensive guide shows you how to achieve complete cryptocurrency tax compliance using Beancount.io's powerful plain-text accounting system, ensuring you meet all IRS requirements while maximizing tax efficiency.

Understanding Cryptocurrency Tax Requirements​

IRS Treatment of Cryptocurrency​

The IRS treats cryptocurrency as property, not currency, which creates specific tax implications:

• Every transaction is potentially taxable: Trading, selling, spending, or exchanging crypto
• Cost basis must be tracked: For every unit of cryptocurrency owned
• Holding period determines tax rate: Short-term vs. long-term capital gains
• Income recognition required: For mining, staking, airdrops, and DeFi rewards
• Detailed records mandatory: Transaction-level documentation for audits

Key Tax Forms for Cryptocurrency​

Form 1040 - Individual Income Tax Return​

• Line 1: Report crypto income (staking, mining, airdrops)
• Schedule 1: Additional income sources
• Digital Asset Question: Must answer "Yes" if you had crypto transactions

Form 8949 - Sales and Other Dispositions of Capital Assets​

• Part I: Short-term capital gains/losses (held ≤ 1 year)
• Part II: Long-term capital gains/losses (held > 1 year)
• Detailed transaction reporting: Date acquired, date sold, proceeds, cost basis

Schedule D - Capital Gains and Losses​

• Summary of Form 8949: Aggregated capital gains/losses
• Net capital gain/loss: Final calculation for tax liability

Setting Up Tax-Compliant Crypto Accounting​

Account Structure for Tax Reporting​

Design your account hierarchy to align with tax requirements:

; Assets - Organized by holding period and source
1970-01-01 open Assets:Crypto:ShortTerm:Coinbase:BTC
1970-01-01 open Assets:Crypto:LongTerm:Coinbase:BTC
1970-01-01 open Assets:Crypto:Trading:Binance:ETH
1970-01-01 open Assets:Crypto:Investment:Ledger:BTC

; Income - Separated by tax treatment
1970-01-01 open Income:Crypto:Staking:Ordinary        ; Taxed as ordinary income
1970-01-01 open Income:Crypto:Mining:Ordinary         ; Taxed as ordinary income
1970-01-01 open Income:Crypto:Airdrops:Ordinary       ; Taxed as ordinary income
1970-01-01 open Income:CapitalGains:ShortTerm         ; Short-term capital gains
1970-01-01 open Income:CapitalGains:LongTerm          ; Long-term capital gains

; Expenses - Tax deductible categories
1970-01-01 open Expenses:Crypto:Fees:Deductible       ; Trading fees
1970-01-01 open Expenses:Crypto:Mining:Equipment      ; Mining equipment
1970-01-01 open Expenses:Crypto:Mining:Electricity    ; Mining electricity
1970-01-01 open Expenses:CapitalLoss:ShortTerm        ; Short-term capital losses
1970-01-01 open Expenses:CapitalLoss:LongTerm         ; Long-term capital losses

Metadata for Tax Compliance​

Use metadata to track tax-relevant information:

2024-01-15 * "Buy BTC for long-term investment" ^investment-btc #long-term
  purchase-date: "2024-01-15"
  intended-holding: "long-term"
  tax-lot-id: "BTC-001"
  Assets:Crypto:LongTerm:Coinbase:BTC  1.0 BTC {45000.00 USD}
  Assets:Crypto:Coinbase:USD         -45000.00 USD
  Expenses:Crypto:Fees:Deductible        50.00 USD
  Assets:Crypto:Coinbase:USD            -50.00 USD

Recording Taxable Cryptocurrency Events​

1. Cryptocurrency Sales​

Short-term Capital Gains (≤ 1 year)​

2024-06-15 * "Sell BTC - Short-term capital gain" ^btc-sale-001
  date-acquired: "2024-01-15"
  holding-period: "151 days"
  form-8949-code: "A"
  Assets:Crypto:ShortTerm:Coinbase:BTC  -0.5 BTC {45000.00 USD}
  Assets:Crypto:Coinbase:USD           24000.00 USD
  Expenses:Crypto:Fees:Deductible         30.00 USD
  Assets:Crypto:Coinbase:USD             -30.00 USD
  Income:CapitalGains:ShortTerm         1470.00 USD  ; 24000 - 22500 - 30

Long-term Capital Gains (> 1 year)​

2025-02-01 * "Sell BTC - Long-term capital gain" ^btc-sale-002
  date-acquired: "2024-01-15"
  holding-period: "382 days"
  form-8949-code: "D"
  Assets:Crypto:LongTerm:Coinbase:BTC   -0.5 BTC {45000.00 USD}
  Assets:Crypto:Coinbase:USD           28000.00 USD
  Expenses:Crypto:Fees:Deductible         35.00 USD
  Assets:Crypto:Coinbase:USD             -35.00 USD
  Income:CapitalGains:LongTerm          5465.00 USD  ; 28000 - 22500 - 35

2. Cryptocurrency-to-Cryptocurrency Trades​

Every crypto-to-crypto trade is taxable:

2024-03-20 * "Trade BTC for ETH - Taxable exchange"
  ; Dispose of BTC (taxable event)
  Assets:Crypto:Trading:Binance:BTC     -1.0 BTC {46000.00 USD}
  Income:CapitalGains:ShortTerm        2000.00 USD  ; 48000 - 46000
  
  ; Acquire ETH (new cost basis)
  Assets:Crypto:Trading:Binance:ETH      20 ETH {2400.00 USD}
  
  ; Trading fees
  Expenses:Crypto:Fees:Deductible        40.00 USD
  Assets:Crypto:Trading:Binance:USD     -40.00 USD

3. Staking Rewards (Ordinary Income)​

2024-01-31 * "ETH Staking Rewards - January"
  reward-type: "staking"
  fair-market-value: "2500.00 USD per ETH"
  taxable-income: "200.00 USD"
  Assets:Staking:Ethereum:ETH           0.08 ETH {2500.00 USD}
  Income:Crypto:Staking:Ordinary       200.00 USD

4. Mining Income​

2024-01-15 * "Bitcoin Mining Reward"
  mining-pool: "Slush Pool"
  block-height: "825000"
  fair-market-value: "45000.00 USD per BTC"
  Assets:Crypto:Mining:BTC              0.01 BTC {45000.00 USD}
  Income:Crypto:Mining:Ordinary        450.00 USD
  
  ; Mining expenses (deductible)
  Expenses:Crypto:Mining:Electricity    120.00 USD
  Assets:Checking                      -120.00 USD

5. Airdrops and Hard Forks​

2024-03-01 * "UNI Token Airdrop"
  airdrop-source: "Uniswap Protocol"
  fair-market-value: "8.00 USD per UNI"
  taxable-amount: "3200.00 USD"
  Assets:Crypto:Wallet:MetaMask:UNI    400 UNI {8.00 USD}
  Income:Crypto:Airdrops:Ordinary     3200.00 USD

6. DeFi Activities​

Yield Farming Rewards​

2024-02-28 * "Compound Protocol Yield"
  protocol: "Compound"
  reward-token: "COMP"
  fair-market-value: "85.00 USD per COMP"
  Assets:Crypto:Wallet:MetaMask:COMP    12 COMP {85.00 USD}
  Income:Crypto:DeFi:Ordinary         1020.00 USD

Liquidity Pool Fees​

2024-02-15 * "Uniswap LP Fees Claimed"
  protocol: "Uniswap V3"
  pool: "ETH-USDC"
  Assets:Crypto:Wallet:MetaMask:ETH     0.2 ETH {2600.00 USD}
  Assets:Crypto:Wallet:MetaMask:USDC    300 USDC
  Income:Crypto:DeFi:Ordinary          820.00 USD

Tax Optimization Strategies​

1. Tax Loss Harvesting​

Systematically realize losses to offset gains:

; Identify losing positions
2024-12-15 * "Tax Loss Harvesting - Sell ADA at loss"
  purchase-date: "2024-06-01"
  purchase-price: "0.60 USD per ADA"
  sale-price: "0.45 USD per ADA"
  Assets:Crypto:Trading:Binance:ADA    -1000 ADA {0.60 USD}
  Assets:Crypto:Trading:Binance:USD     445.00 USD
  Expenses:Crypto:Fees:Deductible         5.00 USD
  Assets:Crypto:Trading:Binance:USD      -5.00 USD
  Expenses:CapitalLoss:ShortTerm       150.00 USD  ; Realized loss

2. Wash Sale Considerations​

While wash sale rules don't officially apply to crypto, avoid potential issues:

; Wait 31 days before repurchasing
2025-01-16 * "Repurchase ADA after wash sale period"
  wash-sale-safe: "31+ days elapsed"
  Assets:Crypto:Trading:Binance:ADA    1000 ADA {0.45 USD}
  Assets:Crypto:Trading:Binance:USD    -450.00 USD
  Expenses:Crypto:Fees:Deductible         5.00 USD
  Assets:Crypto:Trading:Binance:USD      -5.00 USD

3. Specific Identification Method​

Choose which lots to sell for optimal tax outcomes:

; High-cost basis lot for loss harvesting
2024-11-30 * "Sell high-cost ETH lot for tax loss"
  lot-selection: "specific-identification"
  cost-basis: "3500.00 USD per ETH"
  sale-price: "2400.00 USD per ETH"
  Assets:Crypto:Trading:Coinbase:ETH    -5 ETH {3500.00 USD}
  Assets:Crypto:Trading:Coinbase:USD  11950.00 USD
  Expenses:Crypto:Fees:Deductible        50.00 USD
  Assets:Crypto:Trading:Coinbase:USD    -50.00 USD
  Expenses:CapitalLoss:ShortTerm      5500.00 USD  ; Significant loss

4. Long-term vs Short-term Planning​

Strategically time sales for favorable tax treatment:

; Hold for long-term treatment
2024-12-20 * "BTC approaching long-term status"
  purchase-date: "2024-01-01"
  days-held: "354"
  long-term-date: "2025-01-02"
  note: "Hold 11 more days for long-term treatment"

Automated Tax Reporting​

Form 8949 Generation​

Use Beancount.io queries to generate Form 8949 data:

-- Short-term capital gains/losses
SELECT 
  date,
  narration,
  -position as quantity_sold,
  cost(position) as cost_basis,
  value(position) as proceeds,
  value(position) + cost(position) as gain_loss
WHERE account ~ "Income:CapitalGains:ShortTerm" 
   OR account ~ "Expenses:CapitalLoss:ShortTerm"
ORDER BY date;

Schedule D Summary​

-- Capital gains summary
SELECT 
  'Short-term' as type,
  sum(position) as total_gain_loss
WHERE account ~ "Income:CapitalGains:ShortTerm" 
   OR account ~ "Expenses:CapitalLoss:ShortTerm"

UNION ALL

SELECT 
  'Long-term' as type,
  sum(position) as total_gain_loss
WHERE account ~ "Income:CapitalGains:LongTerm" 
   OR account ~ "Expenses:CapitalLoss:LongTerm";

Ordinary Income Reporting​

-- Crypto ordinary income
SELECT 
  account,
  sum(position) as total_income
WHERE account ~ "Income:Crypto.*:Ordinary"
GROUP BY account
ORDER BY total_income DESC;

Record Keeping and Documentation​

Essential Documentation​

Maintain comprehensive records for each transaction:

2024-01-15 * "BTC Purchase - Complete Documentation" ^btc-001
  exchange: "Coinbase Pro"
  transaction-id: "abc123def456"
  confirmation-hash: "0x789..."
  ip-address: "192.168.1.100"
  timestamp: "2024-01-15T14:30:00Z"
  market-price-source: "CoinGecko"
  Assets:Crypto:LongTerm:Coinbase:BTC  1.0 BTC {45000.00 USD}
  Assets:Crypto:Coinbase:USD         -45000.00 USD

Audit Trail Maintenance​

Create detailed audit trails:

; Link related transactions
2024-01-15 * "BTC Purchase" ^btc-purchase-001
2024-01-20 * "BTC Transfer to Wallet" ^btc-purchase-001
2024-06-15 * "BTC Sale" ^btc-purchase-001

Price Verification​

Document price sources for fair market value:

2024-01-15 price BTC 45000.00 USD
  source: "CoinGecko"
  timestamp: "2024-01-15T14:30:00Z"
  verification: "Multiple exchange average"

Common Tax Compliance Mistakes​

1. Missing Transactions​

• Problem: Not reporting all taxable events
• Solution: Comprehensive transaction tracking in Beancount.io

2. Incorrect Cost Basis​

• Problem: Using wrong cost basis calculation method
• Solution: Lot-based tracking with specific identification

3. Missing Income Reporting​

• Problem: Not reporting staking, mining, or airdrop income
• Solution: Separate income accounts for all sources

4. Inadequate Documentation​

• Problem: Insufficient records for audit defense
• Solution: Detailed metadata and transaction linking

5. Timing Issues​

• Problem: Incorrect holding period calculations
• Solution: Automated date tracking and alerts

Integration with Tax Software​

Export to Popular Tax Tools​

Generate reports compatible with major tax software:

# Export to TurboTax format
def export_turbotax_format():
    return {
        'form_8949_short_term': query_short_term_gains(),
        'form_8949_long_term': query_long_term_gains(),
        'schedule_1_income': query_ordinary_income(),
        'deductible_expenses': query_deductible_expenses()
    }

Professional Tax Preparation​

Provide CPAs with comprehensive reports:

-- Complete tax package for CPA
SELECT 
  'Capital Gains Summary' as report_type,
  account,
  sum(position) as amount
WHERE account ~ "Income:CapitalGains" 
   OR account ~ "Expenses:CapitalLoss"
GROUP BY account

UNION ALL

SELECT 
  'Ordinary Income Summary' as report_type,
  account,
  sum(position) as amount
WHERE account ~ "Income:Crypto.*:Ordinary"
GROUP BY account;

Year-End Tax Planning​

December Tax Strategies​

Implement year-end tax optimization:

; December tax planning checklist
2024-12-01 * "Year-end tax planning review"
  unrealized-gains: "Calculate unrealized positions"
  loss-harvesting: "Identify loss harvesting opportunities"
  income-timing: "Consider deferring income to next year"
  expense-timing: "Accelerate deductible expenses"

Quarterly Estimated Taxes​

Track quarterly tax obligations:

2024-01-15 * "Q1 Estimated Tax Payment"
  Expenses:Taxes:Estimated:Federal     5000.00 USD
  Expenses:Taxes:Estimated:State       1200.00 USD
  Assets:Checking                     -6200.00 USD

Conclusion​

Cryptocurrency tax compliance doesn't have to be overwhelming. With Beancount.io's comprehensive plain-text accounting system, you can:

• Ensure Complete Compliance: Meet all IRS reporting requirements
• Optimize Tax Liability: Implement sophisticated tax strategies
• Maintain Audit-Ready Records: Comprehensive documentation and audit trails
• Automate Reporting: Generate tax forms and reports automatically
• Scale with Complexity: Handle simple trades to complex DeFi strategies

Key benefits for crypto tax compliance:

• Transparent Calculations: See exactly how tax numbers are computed
• Flexible Reporting: Generate any report format needed
• Professional Integration: Work seamlessly with CPAs and tax software
• Future-Proof Records: Plain-text format ensures long-term accessibility

Start implementing proper crypto tax compliance today. The investment in accurate record-keeping will save you time, money, and stress during tax season while ensuring you're fully compliant with IRS requirements.

Ready to master cryptocurrency tax compliance? Get started with Beancount.io and take control of your crypto tax obligations.

Decentralized Finance (DeFi) has revolutionized how we interact with financial services, offering unprecedented opportunities for yield generation, liquidity provision, and decentralized trading. However, with these opportunities comes the challenge of accurately tracking complex transactions for tax compliance and portfolio management.

Traditional accounting methods struggle with DeFi's unique characteristics: automated market makers, liquidity mining, impermanent loss, and multi-token rewards. This comprehensive guide shows you how to master DeFi accounting using Beancount.io's powerful plain-text accounting system.

Understanding DeFi Accounting Challenges​

The Complexity of DeFi Transactions​

DeFi protocols create accounting challenges that don't exist in traditional finance:

• Multi-token transactions: Single operations involving multiple cryptocurrencies
• Automated compounding: Rewards automatically reinvested
• Impermanent loss: Value changes due to price divergence in liquidity pools
• Gas fee optimization: Complex fee structures across different networks
• Protocol governance: Voting rights and governance token distributions
• Cross-protocol interactions: Transactions spanning multiple DeFi platforms

Tax Implications of DeFi Activities​

The IRS treats DeFi activities as taxable events:

• Liquidity provision: May trigger taxable events when depositing assets
• Yield farming rewards: Taxable as ordinary income at fair market value
• Impermanent loss: Potential tax implications when withdrawing from pools
• Governance tokens: Airdrops and rewards taxable as income
• Staking rewards: Taxable as income when received

Setting Up DeFi Accounts in Beancount.io​

Comprehensive Account Structure​

Create a detailed account hierarchy that captures all DeFi activities:

; Wallet Accounts
1970-01-01 open Assets:Crypto:Wallet:MetaMask:ETH
1970-01-01 open Assets:Crypto:Wallet:MetaMask:USDC
1970-01-01 open Assets:Crypto:Wallet:MetaMask:DAI
1970-01-01 open Assets:Crypto:Wallet:MetaMask:WBTC

; Uniswap V3 Liquidity Pools
1970-01-01 open Assets:DeFi:Uniswap:ETH-USDC-LP
1970-01-01 open Assets:DeFi:Uniswap:DAI-USDC-LP
1970-01-01 open Assets:DeFi:Uniswap:WBTC-ETH-LP

; Compound Protocol
1970-01-01 open Assets:DeFi:Compound:cUSDC
1970-01-01 open Assets:DeFi:Compound:cETH
1970-01-01 open Assets:DeFi:Compound:cDAI

; Aave Protocol
1970-01-01 open Assets:DeFi:Aave:aUSDC
1970-01-01 open Assets:DeFi:Aave:aETH
1970-01-01 open Assets:DeFi:Aave:DebtETH

; Staking Protocols
1970-01-01 open Assets:Staking:Ethereum:ETH
1970-01-01 open Assets:Staking:Lido:stETH
1970-01-01 open Assets:Staking:RocketPool:rETH

; Income Accounts
1970-01-01 open Income:DeFi:Yield:Uniswap
1970-01-01 open Income:DeFi:Yield:Compound
1970-01-01 open Income:DeFi:Yield:Aave
1970-01-01 open Income:DeFi:Staking:Ethereum
1970-01-01 open Income:DeFi:Governance:Tokens
1970-01-01 open Income:DeFi:Airdrops

; Expense Accounts
1970-01-01 open Expenses:DeFi:Gas:Ethereum
1970-01-01 open Expenses:DeFi:Gas:Polygon
1970-01-01 open Expenses:DeFi:Gas:Arbitrum
1970-01-01 open Expenses:DeFi:ImpermanentLoss

DeFi-Specific Commodities​

Define tokens and LP tokens with proper metadata:

1970-01-01 commodity UNI-V3-ETH-USDC
  name: "Uniswap V3 ETH-USDC LP Token"
  asset-class: "liquidity-pool"
  protocol: "uniswap-v3"

1970-01-01 commodity cUSDC
  name: "Compound USDC"
  asset-class: "lending-token"
  protocol: "compound"

1970-01-01 commodity stETH
  name: "Lido Staked Ethereum"
  asset-class: "staking-derivative"
  protocol: "lido"

Tracking Common DeFi Activities​

1. Uniswap Liquidity Provision​

Adding Liquidity to a Pool​

2024-01-15 * "Add liquidity to ETH-USDC Uniswap V3 pool"
  Assets:Crypto:Wallet:MetaMask:ETH     -5.0 ETH {2500.00 USD}
  Assets:Crypto:Wallet:MetaMask:USDC  -12500 USDC
  Assets:DeFi:Uniswap:ETH-USDC-LP        100 UNI-V3-ETH-USDC {250.00 USD}
  Expenses:DeFi:Gas:Ethereum            0.01 ETH {2500.00 USD}
  Assets:Crypto:Wallet:MetaMask:ETH    -0.01 ETH {2500.00 USD}

Claiming LP Fees​

2024-02-15 * "Claim Uniswap LP fees"
  Assets:Crypto:Wallet:MetaMask:ETH     0.2 ETH {2600.00 USD}
  Assets:Crypto:Wallet:MetaMask:USDC    300 USDC
  Income:DeFi:Yield:Uniswap            820.00 USD
  Expenses:DeFi:Gas:Ethereum           0.005 ETH {2600.00 USD}
  Assets:Crypto:Wallet:MetaMask:ETH   -0.005 ETH {2600.00 USD}

Removing Liquidity with Impermanent Loss​

2024-03-15 * "Remove liquidity from ETH-USDC pool"
  Assets:DeFi:Uniswap:ETH-USDC-LP       -100 UNI-V3-ETH-USDC {250.00 USD}
  Assets:Crypto:Wallet:MetaMask:ETH      4.8 ETH {2800.00 USD}
  Assets:Crypto:Wallet:MetaMask:USDC   12800 USDC
  Expenses:DeFi:ImpermanentLoss         240.00 USD  ; IL calculation
  Expenses:DeFi:Gas:Ethereum           0.008 ETH {2800.00 USD}
  Assets:Crypto:Wallet:MetaMask:ETH   -0.008 ETH {2800.00 USD}

2. Compound Protocol Lending​

Supplying Assets to Compound​

2024-01-20 * "Supply USDC to Compound"
  Assets:Crypto:Wallet:MetaMask:USDC  -10000 USDC
  Assets:DeFi:Compound:cUSDC            500 cUSDC {20.00 USD}
  Expenses:DeFi:Gas:Ethereum           0.008 ETH {2500.00 USD}
  Assets:Crypto:Wallet:MetaMask:ETH   -0.008 ETH {2500.00 USD}

Earning Compound Interest​

2024-02-20 * "Compound interest accrual"
  Assets:DeFi:Compound:cUSDC             5.2 cUSDC {20.50 USD}
  Income:DeFi:Yield:Compound           106.60 USD

Claiming COMP Rewards​

2024-02-20 * "Claim COMP governance tokens"
  Assets:Crypto:Wallet:MetaMask:COMP     12 COMP {85.00 USD}
  Income:DeFi:Governance:Tokens        1020.00 USD
  Expenses:DeFi:Gas:Ethereum           0.006 ETH {2600.00 USD}
  Assets:Crypto:Wallet:MetaMask:ETH   -0.006 ETH {2600.00 USD}

3. Aave Protocol Operations​

Depositing and Borrowing​

; Deposit ETH as collateral
2024-01-25 * "Deposit ETH to Aave"
  Assets:Crypto:Wallet:MetaMask:ETH    -10 ETH {2500.00 USD}
  Assets:DeFi:Aave:aETH                 10 aETH {2500.00 USD}
  Expenses:DeFi:Gas:Ethereum          0.01 ETH {2500.00 USD}
  Assets:Crypto:Wallet:MetaMask:ETH  -0.01 ETH {2500.00 USD}

; Borrow USDC against ETH collateral
2024-01-25 * "Borrow USDC from Aave"
  Assets:Crypto:Wallet:MetaMask:USDC  15000 USDC
  Liabilities:DeFi:Aave:DebtUSDC     -15000 USDC
  Expenses:DeFi:Gas:Ethereum          0.008 ETH {2500.00 USD}
  Assets:Crypto:Wallet:MetaMask:ETH  -0.008 ETH {2500.00 USD}

Repaying Loans with Interest​

2024-03-25 * "Repay USDC loan to Aave"
  Liabilities:DeFi:Aave:DebtUSDC      15000 USDC
  Expenses:DeFi:Interest:Aave           450 USDC  ; Interest accrued
  Assets:Crypto:Wallet:MetaMask:USDC -15450 USDC
  Expenses:DeFi:Gas:Ethereum          0.008 ETH {2700.00 USD}
  Assets:Crypto:Wallet:MetaMask:ETH  -0.008 ETH {2700.00 USD}

4. Ethereum Staking​

Direct Ethereum Staking​

2024-01-10 * "Stake ETH on Ethereum 2.0"
  Assets:Crypto:Wallet:MetaMask:ETH    -32 ETH {2500.00 USD}
  Assets:Staking:Ethereum:ETH           32 ETH {2500.00 USD}
  Expenses:DeFi:Gas:Ethereum          0.01 ETH {2500.00 USD}
  Assets:Crypto:Wallet:MetaMask:ETH  -0.01 ETH {2500.00 USD}

Staking Rewards​

2024-02-10 * "ETH staking rewards"
  Assets:Staking:Ethereum:ETH          0.15 ETH {2600.00 USD}
  Income:DeFi:Staking:Ethereum        390.00 USD

Liquid Staking with Lido​

2024-01-12 * "Stake ETH with Lido"
  Assets:Crypto:Wallet:MetaMask:ETH    -10 ETH {2500.00 USD}
  Assets:Staking:Lido:stETH             10 stETH {2500.00 USD}
  Expenses:DeFi:Gas:Ethereum          0.008 ETH {2500.00 USD}
  Assets:Crypto:Wallet:MetaMask:ETH  -0.008 ETH {2500.00 USD}

Advanced DeFi Scenarios​

Yield Farming Strategies​

Multi-Protocol Yield Farming​

; Step 1: Deposit USDC to Compound
2024-01-30 * "Deposit USDC to Compound for yield farming"
  Assets:Crypto:Wallet:MetaMask:USDC  -20000 USDC
  Assets:DeFi:Compound:cUSDC            1000 cUSDC {20.00 USD}

; Step 2: Borrow DAI against cUSDC
2024-01-30 * "Borrow DAI from Compound"
  Assets:Crypto:Wallet:MetaMask:DAI    15000 DAI
  Liabilities:DeFi:Compound:DebtDAI   -15000 DAI

; Step 3: Provide DAI-USDC liquidity to Uniswap
2024-01-30 * "Add DAI-USDC liquidity for farming"
  Assets:Crypto:Wallet:MetaMask:DAI   -15000 DAI
  Assets:Crypto:Wallet:MetaMask:USDC   -5000 USDC  ; Additional USDC
  Assets:DeFi:Uniswap:DAI-USDC-LP        200 UNI-V3-DAI-USDC {100.00 USD}

Cross-Chain DeFi Operations​

Bridging Assets​

2024-02-05 * "Bridge ETH to Polygon"
  Assets:Crypto:Wallet:MetaMask:ETH    -5 ETH {2600.00 USD}
  Assets:Crypto:Wallet:Polygon:ETH      5 ETH {2600.00 USD}
  Expenses:DeFi:Bridge:Fees           0.01 ETH {2600.00 USD}
  Assets:Crypto:Wallet:MetaMask:ETH  -0.01 ETH {2600.00 USD}

Flash Loans and Arbitrage​

2024-02-10 * "Flash loan arbitrage opportunity"
  ; Flash loan
  Assets:Crypto:Temp:FlashLoan       100000 USDC
  Liabilities:DeFi:Aave:FlashLoan   -100000 USDC
  
  ; Arbitrage trade
  Assets:Crypto:Temp:FlashLoan      -100000 USDC
  Assets:Crypto:Temp:Arbitrage       101500 USDC
  
  ; Repay flash loan
  Liabilities:DeFi:Aave:FlashLoan    100000 USDC
  Assets:Crypto:Temp:Arbitrage      -100090 USDC  ; Including fees
  
  ; Profit
  Assets:Crypto:Wallet:MetaMask:USDC   1410 USDC
  Income:DeFi:Arbitrage:Profit        1410.00 USD

DeFi Tax Reporting Considerations​

Income Recognition​

All DeFi rewards are taxable as ordinary income:

; Track all income sources separately
Income:DeFi:Yield:Uniswap           ; LP fees
Income:DeFi:Yield:Compound          ; Interest earned
Income:DeFi:Staking:Ethereum        ; Staking rewards
Income:DeFi:Governance:Tokens       ; Governance token airdrops
Income:DeFi:Airdrops               ; Protocol airdrops

Expense Deductions​

Track deductible expenses:

Expenses:DeFi:Gas:Ethereum          ; Gas fees
Expenses:DeFi:Gas:Polygon           ; Layer 2 fees
Expenses:DeFi:Interest:Aave         ; Borrowing costs
Expenses:DeFi:ImpermanentLoss       ; IL from LP positions

Capital Gains Tracking​

Use lot-based accounting for accurate capital gains:

; Different cost basis for same token
Assets:DeFi:Compound:cUSDC   100 cUSDC {20.00 USD}  ; Batch 1
Assets:DeFi:Compound:cUSDC   100 cUSDC {21.00 USD}  ; Batch 2

Best Practices for DeFi Accounting​

1. Real-Time Transaction Recording​

• Record transactions immediately after execution
• Use transaction hashes for verification
• Monitor mempool for pending transactions

2. Gas Fee Optimization Tracking​

; Track gas optimization strategies
Expenses:DeFi:Gas:Ethereum:Standard    ; Standard gas price
Expenses:DeFi:Gas:Ethereum:Fast        ; Fast gas price
Expenses:DeFi:Gas:Ethereum:Instant     ; Instant gas price

3. Protocol Risk Management​

; Track protocol-specific risks
Expenses:DeFi:Risk:SmartContract      ; Smart contract failures
Expenses:DeFi:Risk:Liquidation        ; Liquidation events
Expenses:DeFi:Risk:Slippage          ; Slippage costs

4. Automated Reconciliation​

• Set up automated price feeds
• Use protocol APIs for balance verification
• Implement automated transaction imports

5. Documentation Standards​

• Maintain detailed transaction notes
• Document strategy rationale
• Keep protocol interaction records

Integration with DeFi Tools​

Portfolio Tracking​

• DeBank: DeFi portfolio overview
• Zapper: Multi-protocol dashboard
• Zerion: DeFi wallet and tracker

Tax Reporting​

• Koinly: DeFi tax calculations
• CoinTracker: Multi-protocol support
• TokenTax: Specialized DeFi reporting

Analytics Platforms​

• DeFi Pulse: Protocol analytics
• DeFiLlama: TVL and yield tracking
• APY.vision: Impermanent loss tracking

Conclusion​

DeFi accounting complexity shouldn't prevent you from participating in the decentralized finance revolution. With Beancount.io's powerful plain-text accounting system, you can:

• Track Complex Transactions: Handle multi-protocol interactions seamlessly
• Ensure Tax Compliance: Proper income recognition and expense tracking
• Monitor Portfolio Performance: Real-time insights into DeFi positions
• Manage Risk: Track impermanent loss and protocol risks
• Scale Operations: From simple staking to complex yield farming strategies

The key to successful DeFi accounting is consistency, accuracy, and proper categorization. Start with basic protocols and gradually expand to more complex strategies as you become comfortable with the accounting patterns.

Ready to master DeFi accounting? Start your Beancount.io journey and take control of your decentralized finance portfolio today.

Beancount version 3, released in mid-2024, marks a significant architectural evolution for the popular plain-text accounting tool. While it maintains backward compatibility for user ledger files, the underlying structure and accompanying tools have undergone substantial changes. Here’s a breakdown of what’s new in Beancount v3.

A More Modular and Streamlined Architecture​

The most significant change in Beancount v3 is the move to a more modular ecosystem. Several key functionalities that were previously bundled with the core have been spun off into separate, independent projects. This makes the core of Beancount leaner and allows for more focused development on individual components.

The key components that are now separate packages include:

• beanquery: The powerful SQL-like query tool for your ledger files is now in its own package.
• beangulp: This is the new home for the data importing framework, replacing the former beancount.ingest module.
• beanprice: A dedicated tool for fetching prices of commodities and stocks.

This separation means that users will need to install these packages in addition to beancount itself to retain the full functionality they were used to in version 2.

Changes to Command-Line Tools and Workflows​

Reflecting the new modular architecture, there are some notable changes to the command-line tools:

• bean-report is gone: This tool has been removed. Users are now encouraged to use bean-query (from the beanquery package) for their reporting needs.
• New Importer Workflow: The bean-extract and bean-identify commands have been removed from the core. The new approach with beangulp is script-based. Users will now create their own Python scripts to handle the importing of data from external sources like bank statements.

Syntax and Feature Enhancements​

While the core accounting principles remain the same, Beancount v3 introduces some welcome flexibility to its syntax:

• More Flexible Currency Codes: The previous restrictions on the length and characters for currency names have been relaxed. Single-character currency symbols are now supported.
• Expanded Transaction Flags: Users can now use any capital letter from A to Z as a flag for transactions, allowing for more granular categorization.

Importantly, these changes are backward-compatible, so your existing Beancount v2 ledger files will work without any modifications.

The C++ Rewrite and Performance​

One of the long-term goals for Beancount has been a rewrite of its performance-critical components in C++. While this work is ongoing, the initial release of Beancount v3 does not include the C++-based core. This means that for now, the performance of v3 is comparable to v2. The C++ code remains in a separate development branch for future integration.

Migrating from v2 to v3​

For most users, the migration from Beancount v2 to v3 is relatively straightforward:

1. Ledger Files: No changes are needed for your .beancount files.
2. Installation: You will need to install the new, separate packages like beanquery and beangulp using pip.
3. Importer Scripts: If you have custom importers, you will need to update them to use the new beangulp API. This mainly involves changing the base class your importers inherit from and adjusting some method signatures.
4. Fava: The popular web interface for Beancount, Fava, has been updated to be compatible with v3. Ensure you have the latest version of Fava for a seamless experience.

In essence, Beancount v3 is a foundational release that streamlines the project's architecture, making it more modular and easier to maintain and extend in the long run. While it requires some adjustments to user workflows, especially around data importing, it sets the stage for the future development of this powerful accounting tool.

Small business owners spend an average of 11 hours per month manually categorizing expenses - nearly three full workweeks annually devoted to data entry. A 2023 QuickBooks survey reveals that 68% of business owners rank expense tracking as their most frustrating bookkeeping task, yet only 15% have embraced automation solutions.

Plain text accounting, powered by tools like Beancount, offers a fresh approach to financial management. By combining transparent, programmable architecture with modern AI capabilities, businesses can achieve highly accurate expense categorization while maintaining full control over their data.

This guide will walk you through building an expense automation system tailored to your business's unique patterns. You'll learn why traditional software falls short, how to harness Beancount's plain text foundation, and practical steps for implementing adaptive machine learning models.

The Hidden Costs of Manual Expense Management​

Manual expense categorization drains more than just time—it undermines business potential. Consider the opportunity cost: those hours spent matching receipts to categories could instead fuel business growth, strengthen client relationships, or refine your offerings.

A recent Accounting Today survey found small business owners dedicate 10 hours weekly to bookkeeping tasks. Beyond the time sink, manual processes introduce risks. Take the case of a digital marketing agency that discovered their manual categorization had inflated travel expenses by 20%, distorting their financial planning and decision-making.

Poor financial management remains a leading cause of small business failure, according to the Small Business Administration. Misclassified expenses can mask profitability issues, overlook cost-saving opportunities, and create tax season headaches.

Beancount's Architecture: Where Simplicity Meets Power​

Beancount's plain-text foundation transforms financial data into code, making every transaction trackable and AI-ready. Unlike traditional software trapped in proprietary databases, Beancount's approach enables version control through tools like Git, creating an audit trail for every change.

This open architecture allows seamless integration with programming languages and AI tools. A digital marketing agency reported saving 12 monthly hours through custom scripts that automatically categorize transactions based on their specific business rules.

The plain text format ensures data remains accessible and portable—no vendor lock-in means businesses can adapt as technology evolves. This flexibility, combined with robust automation capabilities, creates a foundation for sophisticated financial management without sacrificing simplicity.

Creating Your Automation Pipeline​

Building an expense automation system with Beancount starts with organizing your financial data. Let's walk through a practical implementation using real examples.

1. Setting Up Your Beancount Structure​

First, establish your account structure and categories:

2025-01-01 open Assets:Business:Checking
2025-01-01 open Expenses:Office:Supplies
2025-01-01 open Expenses:Software:Subscriptions
2025-01-01 open Expenses:Marketing:Advertising
2025-01-01 open Liabilities:CreditCard

2. Creating Automation Rules​

Here's a Python script that demonstrates automatic categorization:

import pandas as pd
from datetime import datetime

def categorize_transaction(description, amount):
    rules = {
        'ADOBE': 'Expenses:Software:Subscriptions',
        'OFFICE DEPOT': 'Expenses:Office:Supplies',
        'FACEBOOK ADS': 'Expenses:Marketing:Advertising'
    }
    
    for vendor, category in rules.items():
        if vendor.lower() in description.lower():
            return category
    return 'Expenses:Uncategorized'

def generate_beancount_entry(row):
    date = row['date'].strftime('%Y-%m-%d')
    desc = row['description']
    amount = abs(float(row['amount']))
    category = categorize_transaction(desc, amount)
    
    return f'''
{date} * "{desc}"
    {category}                               {amount:.2f} USD
    Liabilities:CreditCard                  -{amount:.2f} USD
'''

3. Processing Transactions​

Here's how the automated entries look in your Beancount file:

2025-05-01 * "ADOBE CREATIVE CLOUD"
    Expenses:Software:Subscriptions            52.99 USD
    Liabilities:CreditCard                   -52.99 USD

2025-05-02 * "OFFICE DEPOT #1234 - PRINTER PAPER"
    Expenses:Office:Supplies                  45.67 USD
    Liabilities:CreditCard                   -45.67 USD

2025-05-03 * "FACEBOOK ADS #FB12345"
    Expenses:Marketing:Advertising           250.00 USD
    Liabilities:CreditCard                  -250.00 USD

Testing proves crucial—start with a subset of transactions to verify categorization accuracy. Regular execution through task schedulers can save 10+ hours monthly, freeing you to focus on strategic priorities.

Achieving High Accuracy Through Advanced Techniques​

Let's explore how to combine machine learning with pattern matching for precise categorization.

Pattern Matching with Regular Expressions​

import re

patterns = {
    r'(?i)aws.*cloud': 'Expenses:Cloud:AWS',
    r'(?i)(zoom|slack|notion).*subscription': 'Expenses:Software:Subscriptions',
    r'(?i)(uber|lyft|taxi)': 'Expenses:Travel:Transport',
    r'(?i)(marriott|hilton|airbnb)': 'Expenses:Travel:Accommodation'
}

def regex_categorize(description):
    for pattern, category in patterns.items():
        if re.search(pattern, description):
            return category
    return None

Machine Learning Integration​

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
import re
from typing import List, Tuple

class ExpenseClassifier:
    def __init__(self):
        self.vectorizer = TfidfVectorizer()
        self.classifier = MultinomialNB()
    
    def parse_beancount_entries(self, beancount_text: str) -> List[Tuple[str, str]]:
        """Parse Beancount entries into (description, category) pairs."""
        entries = []
        for line in beancount_text.split('\n'):
            # Look for transaction descriptions
            if '* "' in line:
                desc = re.search('"(.+)"', line)
                if desc:
                    description = desc.group(1)
                    # Get the next line which should contain the expense category
                    next_line = next(filter(None, beancount_text.split('\n')[beancount_text.split('\n').index(line)+1:]))
                    if 'Expenses:' in next_line:
                        category = next_line.split()[0].strip()
                        entries.append((description, category))
        return entries
        
    def train(self, beancount_text: str):
        """Train the classifier using Beancount entries."""
        entries = self.parse_beancount_entries(beancount_text)
        if not entries:
            raise ValueError("No valid entries found in training data")
            
        descriptions, categories = zip(*entries)
        X = self.vectorizer.fit_transform(descriptions)
        self.classifier.fit(X, categories)
        
    def predict(self, description: str) -> str:
        """Predict category for a new transaction description."""
        X = self.vectorizer.transform([description])
        return self.classifier.predict(X)[0]

# Example usage with training data:
classifier = ExpenseClassifier()

training_data = """
2025-04-01 * "AWS Cloud Services Monthly Bill" 
    Expenses:Cloud:AWS                         150.00 USD
    Liabilities:CreditCard                   -150.00 USD

2025-04-02 * "Zoom Monthly Subscription"
    Expenses:Software:Subscriptions            14.99 USD
    Liabilities:CreditCard                    -14.99 USD
    
2025-04-03 * "AWS EC2 Instances"
    Expenses:Cloud:AWS                         250.00 USD
    Liabilities:CreditCard                    -250.00 USD

2025-04-04 * "Slack Annual Plan"
    Expenses:Software:Subscriptions           120.00 USD
    Liabilities:CreditCard                   -120.00 USD
"""

# Train the classifier
classifier.train(training_data)

# Test predictions
test_descriptions = [
    "AWS Lambda Services",
    "Zoom Webinar Add-on",
    "Microsoft Teams Subscription"
]

for desc in test_descriptions:
    predicted_category = classifier.predict(desc)
    print(f"Description: {desc}")
    print(f"Predicted Category: {predicted_category}\n")

This implementation includes:

• Proper parsing of Beancount entries
• Training data with multiple examples per category
• Type hints for better code clarity
• Error handling for invalid training data
• Example predictions with similar but unseen transactions

### Combining Both Approaches

```beancount
2025-05-15 * "AWS Cloud Platform - Monthly Usage"
    Expenses:Cloud:AWS                        234.56 USD
    Liabilities:CreditCard                   -234.56 USD

2025-05-15 * "Uber Trip - Client Meeting"
    Expenses:Travel:Transport                  45.00 USD
    Liabilities:CreditCard                    -45.00 USD

2025-05-16 * "Marriott Hotel - Conference Stay"
    Expenses:Travel:Accommodation             299.99 USD
    Liabilities:CreditCard                   -299.99 USD

This hybrid approach achieves remarkable accuracy by:

1. Using regex for predictable patterns (subscriptions, vendors)
2. Applying ML for complex or new transactions
3. Maintaining a feedback loop for continuous improvement

A tech startup implemented these techniques to automate their expense tracking, reducing manual processing time by 12 hours monthly while maintaining 99% accuracy.

Tracking Impact and Optimization​

Measure your automation success through concrete metrics: time saved, error reduction, and team satisfaction. Track how automation affects broader financial indicators like cash flow accuracy and forecasting reliability.

Random transaction sampling helps verify categorization accuracy. When discrepancies arise, refine your rules or update training data. Analytics tools integrated with Beancount can reveal spending patterns and optimization opportunities previously hidden in manual processes.

Engage with the Beancount community to discover emerging best practices and optimization techniques. Regular refinement ensures your system continues delivering value as your business evolves.

Moving Forward​

Automated plain-text accounting represents a fundamental shift in financial management. Beancount's approach combines human oversight with AI precision, delivering accuracy while maintaining transparency and control.

The benefits extend beyond time savings—think clearer financial insights, reduced errors, and more informed decision-making. Whether you're technically inclined or focused on business growth, this framework offers a path to more efficient financial operations.

Start small, measure carefully, and build on success. Your journey toward automated financial management begins with a single transaction.

Modern finance teams typically dedicate 65% of their time to manual reconciliation and data validation, according to McKinsey's 2023 research. At Beancount.io, we're witnessing teams slash their weekly review time from 5 hours to just 1 hour through AI-assisted workflows, while maintaining rigorous accuracy standards.

Plain text accounting already offers transparency and version control. By integrating advanced AI capabilities, we're eliminating tedious transaction matching, discrepancy hunting, and manual categorization that traditionally burden reconciliation processes.

Let's explore how organizations achieve substantial time savings through AI-powered reconciliation, examining technical foundations, real implementation stories, and practical guidance for transitioning to automated workflows.

The Hidden Cost of Manual Reconciliation​

Manual reconciliation resembles solving a puzzle with scattered pieces. Each transaction demands attention, discrepancies require investigation, and the process consumes valuable time. The Institute of Financial Operations and Leadership reports that 60% of accounting professionals spend over half their week on manual reconciliation.

This creates a cascade of challenges beyond just lost time. Teams face mental fatigue from repetitive tasks, increasing error risks under pressure. Even minor mistakes can propagate through financial reports. Additionally, outdated processes hamper collaboration as teams struggle to maintain consistent records across departments.

Consider a mid-sized tech firm whose monthly close dragged on for weeks due to manual reconciliation. Their finance team was perpetually verifying transactions across platforms, leaving minimal bandwidth for strategic work. Only after adopting automation did they reduce reconciliation time by 70%, finally focusing on growth initiatives.

How AI + Plain Text Transform Bank Statement Matching​

AI algorithms analyze transaction patterns within plain text accounting systems, automatically proposing matches between bank statements and accounting records. Natural language processing enables AI to interpret unstructured bank statement data - for example, recognizing "AMZN Mktp US" as an Amazon Marketplace purchase.

Here's a real-world example of how AI assists with bank statement matching in Beancount:

# Original bank statement entry:
# "AMZN Mktp US*IF8QX0QS3" -29.99 USD

# AI-suggested Beancount transaction:
2025-05-20 * "Amazon" "Office supplies - keyboard wrist rest"
  Expenses:Office:Supplies            29.99 USD
  Assets:Bank:Checking              -29.99 USD
  
# Original bank statement entry:
# "UBER *TRIP HELP.UBER.COM" -24.50 USD

# AI-suggested Beancount transaction:
2025-05-21 * "Uber" "Client meeting transportation"
  Expenses:Transportation:Taxi       24.50 USD
  Assets:Bank:Checking             -24.50 USD

The AI system:

1. Recognizes common merchant patterns (e.g., "AMZN Mktp US*" → "Amazon")
2. Suggests appropriate account categories based on transaction history
3. Extracts meaningful descriptions from transaction data
4. Maintains proper double-entry format
5. Tags business-related expenses automatically

For more complex scenarios, like split payments or recurring transactions, the AI excels at pattern recognition:

# Original bank statement entries:
# "POPEYES #1234" -80.00 USD
# "ALICE SMITH" +20.00 USD
# "BOB JONES" +20.00 USD
# "CHARLIE BROWN" +20.00 USD

# AI-suggested Beancount transaction with split payments:
2025-05-22 * "Popeyes" "Team lunch - split with Alice, Bob, and Charlie"
  Expenses:Food                      20.00 USD
  Assets:Receivables:Alice          20.00 USD
  Assets:Receivables:Bob            20.00 USD
  Assets:Receivables:Charlie        20.00 USD
  Liabilities:CreditCard           -80.00 USD

# AI automatically reconciles repayments:
2025-05-23 * "Alice Smith" "Team lunch repayment"
  Assets:Bank:Checking              20.00 USD
  Assets:Receivables:Alice         -20.00 USD

2025-05-23 * "Bob Jones" "Team lunch repayment"
  Assets:Bank:Checking              20.00 USD
  Assets:Receivables:Bob           -20.00 USD

2025-05-23 * "Charlie Brown" "Team lunch repayment"
  Assets:Bank:Checking              20.00 USD
  Assets:Receivables:Charlie       -20.00 USD

FinTech Insights reports that 70% of finance professionals experienced significant error reduction using AI-driven tools. The plain text format enhances this efficiency by enabling easy version control and auditing while remaining highly compatible with AI processing.

Real-World Results from Beancount.io Teams​

A mid-sized accounting firm previously spent five hours reconciling each client account manually. After implementing AI-powered plain text accounting, they completed the same work in one hour. Their financial controller noted, "The system catches discrepancies we might have missed while freeing us to focus on analysis."

A fast-growing tech startup faced mounting transaction volumes that threatened to overwhelm their finance team. By adopting AI reconciliation, they not only managed the increased workload but reduced processing time by 75%. This allowed them to redirect resources toward strategic planning.

Recent research in the Journal of Financial Technologies found that companies using AI-driven accounting solutions reported 30% fewer errors, thanks to robust automated detection and correction features.

Implementation Guide for Automated Reconciliation​

Start by selecting AI tools that integrate smoothly with Beancount.io, such as OpenAI's GPT models or Google's BERT. Prepare your data by standardizing transaction formats and categories - organizations report 78% better AI performance after proper data standardization.

Develop automation scripts leveraging Beancount's flexibility to identify discrepancies and cross-reference data. Train AI models specifically for anomaly detection to catch subtle patterns human reviewers might miss, like recurring late payments that could indicate systemic issues.

Establish regular performance reviews and feedback loops with your team. This iterative approach helps the AI system learn from experience while building trust in the automated process.

Beyond Time Savings: Enhanced Accuracy and Audit-Readiness​

AI reconciliation minimizes human error through automated cross-verification. Deloitte's research shows companies using AI for financial processes achieve 70% fewer accounting discrepancies. The system maintains detailed audit trails, making it easier for auditors to verify transactions.

A technology company struggling with frequent reconciliation errors saw audit costs decrease by 30% after implementing AI tools. The system's continuous learning capabilities meant accuracy improved over time as it processed more transactions.

Conclusion​

AI-powered reconciliation fundamentally transforms financial operations, offering both efficiency gains and enhanced accuracy. Organizations using Beancount.io demonstrate that automated workflows reduce reconciliation time while strengthening data integrity.

As financial complexity increases, manual reconciliation becomes increasingly unsustainable. Organizations that embrace AI-powered plain text accounting gain advantages in speed, accuracy, and strategic capability.

Consider starting with a single account in Beancount.io to experience how modern tooling can enhance your financial workflows.

Financial fraud costs businesses an average of 5% of their annual revenue, with global losses exceeding $4.7 trillion in 2021. While traditional accounting systems struggle to keep pace with sophisticated financial crimes, plain-text accounting combined with artificial intelligence offers a robust solution for protecting financial integrity.

As organizations move from conventional spreadsheets to plain-text accounting systems like Beancount.io, they're discovering AI's ability to identify subtle patterns and anomalies that even experienced auditors might overlook. Let's explore how this technological integration enhances financial security, examine real-world applications, and provide practical guidance for implementation.

Why Traditional Accounting Falls Short​

Traditional accounting systems, particularly spreadsheets, harbor inherent vulnerabilities. Nearly 30% of financial fraud cases stem from spreadsheet manipulation, according to the Association of Certified Fraud Examiners. These tools lack robust audit trails and can be modified without leaving traces, making fraud detection challenging even for vigilant teams.

The isolation of traditional systems from other business tools creates blind spots. Real-time analysis becomes cumbersome, leading to delayed fraud detection and potentially significant losses. Plain-text accounting, enhanced by AI monitoring, addresses these weaknesses by providing transparent, traceable records where every transaction can be readily audited.

Understanding AI's Role in Financial Security​

Modern AI algorithms excel at detecting financial anomalies through various techniques:

• Anomaly detection using isolation forests and clustering methods
• Supervised learning from historical fraud cases
• Natural language processing to analyze transaction descriptions
• Continuous learning and adaptation to evolving patterns

A mid-sized tech company recently discovered this firsthand when AI flagged micro-transactions spread across multiple accounts—an embezzlement scheme that had eluded traditional audits. Organizations using AI for fraud detection report 25% lower fraud losses compared to those using conventional methods alone.

Real-World Success Stories​

Consider a retail chain struggling with inventory losses. Traditional audits suggested clerical errors, but AI analysis revealed coordinated fraud by employees manipulating records. The system identified subtle patterns in transaction timing and amounts that pointed to systematic theft.

Another example involves a financial services firm where AI detected irregular payment processing patterns. The system flagged transactions that appeared normal individually but formed suspicious patterns when analyzed collectively. This led to the discovery of a sophisticated money laundering operation that had evaded detection for months.

Implementing AI Detection in Beancount​

To integrate AI fraud detection into your Beancount workflow:

1. Identify specific vulnerability points in your financial processes
2. Select AI tools designed for plain-text environments
3. Train algorithms on your historical transaction data
4. Establish automated cross-referencing with external databases
5. Create clear protocols for investigating AI-flagged anomalies

Organizations report a 30% reduction in fraud investigation time after implementing AI systems. The key lies in creating a seamless workflow where AI augments rather than replaces human oversight.

Human Expertise Meets Machine Intelligence​

The most effective approach combines AI's processing power with human judgment. While AI excels at pattern recognition and continuous monitoring, human experts provide crucial context and interpretation. A recent Deloitte survey found that companies using this hybrid approach achieved a 42% reduction in financial discrepancies.

Financial professionals play vital roles in:

• Refining AI algorithms
• Investigating flagged transactions
• Distinguishing between legitimate and suspicious patterns
• Developing preventive strategies based on AI insights

Building Stronger Financial Security​

Plain-text accounting with AI fraud detection offers several advantages:

• Transparent, auditable records
• Real-time anomaly detection
• Adaptive learning from new patterns
• Reduced human error
• Comprehensive audit trails

By combining human expertise with AI capabilities, organizations create a robust defense against financial fraud while maintaining transparency and efficiency in their accounting practices.

The integration of AI into plain-text accounting represents a significant advance in financial security. As fraud techniques become more sophisticated, this combination of transparency and intelligent monitoring provides the tools needed to protect financial integrity effectively.

Consider exploring these capabilities within your own organization. The investment in AI-enhanced plain-text accounting could be the difference between detecting fraud early and discovering it too late.

In an era where financial fraud costs businesses and individuals over $5 trillion annually, intelligent transaction validation has become essential. While traditional accounting relies on rigid rules, AI-powered confidence scoring is transforming how we validate financial data, offering both opportunities and challenges.

Plain-text accounting systems like Beancount, when enhanced with machine learning, become sophisticated fraud detection tools. These systems can now identify suspicious patterns and predict potential errors, though they must balance automation with human oversight to maintain accuracy and accountability.

Understanding Account Confidence Scores: The New Frontier in Financial Validation​

Account confidence scores represent a shift from simple balance sheet accuracy to nuanced risk assessment. Think of it as having a tireless digital auditor examining every transaction, weighing multiple factors to determine reliability. This approach goes beyond matching debits and credits, considering transaction patterns, historical data, and contextual information.

While AI excels at processing vast amounts of data quickly, it's not infallible. The technology works best when complementing human expertise rather than replacing it. Some organizations have found that over-reliance on automated scoring can lead to blind spots, particularly with novel transaction types or emerging fraud patterns.

Implementing LLM-Powered Risk Assessment in Beancount: A Technical Deep Dive​

Consider Sarah, a financial controller managing thousands of monthly transactions. Rather than relying solely on traditional checks, she uses LLM-powered assessment to spot patterns human reviewers might miss. The system flags unusual activities while learning from each review, though Sarah ensures human judgment remains central to final decisions.

The implementation involves preprocessing transaction data, training models on diverse financial datasets, and continuous refinement. However, organizations must weigh the benefits against potential challenges like data privacy concerns and the need for ongoing model maintenance.

Pattern Recognition and Anomaly Detection: Training AI to Flag Suspicious Transactions​

AI's pattern recognition capabilities have transformed transaction monitoring, but success depends on quality training data and careful system design. A regional credit union recently implemented AI detection and found that while it caught several fraudulent transactions, it also initially flagged legitimate but unusual business expenses.

The key lies in striking the right balance between sensitivity and specificity. Too many false positives can overwhelm staff, while overly lenient systems might miss crucial red flags. Organizations must regularly fine-tune their detection parameters based on real-world feedback.

Practical Implementation: Using LLMs with Beancount​

Beancount.io integrates LLMs with plain text accounting through a plugin system. Here's how it works:

; 1. First, enable the AI confidence scoring plugin in your Beancount file
2025-01-01 custom "ai.confidence_scoring" "enable"
  threshold: "0.70"  ; Transactions below this score require review
  model: "gpt-4"     ; LLM model to use
  mode: "realtime"    ; Score transactions as they're added

; 2. Define custom risk rules (optional)
2025-01-01 custom "ai.confidence_rules"
  high_value: "5000 USD"  ; Threshold for high-value transactions
  weekend_trading: "false" ; Flag weekend transactions
  new_vendor_period: "90"  ; Days to consider a vendor "new"

; 3. The LLM analyzes each transaction in context
2025-05-15 * "NewCo Services" "Consulting fee"
  Expenses:Consulting                 6000.00 USD
  Assets:Bank:Checking              -6000.00 USD

; 4. The LLM adds metadata based on analysis
2025-05-15 * "NewCo Services" "Consulting fee"
  Expenses:Consulting                 6000.00 USD
  Assets:Bank:Checking              -6000.00 USD
  confidence: "0.45"  ; Added by LLM
  risk_factors: "high-value, new-vendor"
  llm_notes: "First transaction with this vendor, amount exceeds typical consulting fees"
  review_required: "true"

The LLM performs several key functions:

1. Context Analysis: Reviews transaction history to establish patterns
2. Natural Language Processing: Understands vendor names and payment descriptions
3. Pattern Matching: Identifies similar past transactions
4. Risk Assessment: Evaluates multiple risk factors
5. Explanation Generation: Provides human-readable rationale

You can customize the system through directives in your Beancount file:

; Example: Configure custom confidence thresholds by account
2025-01-01 custom "ai.confidence_thresholds"
  Assets:Crypto: "0.85"           ; Higher threshold for crypto
  Expenses:Travel: "0.75"        ; Watch travel expenses closely
  Assets:Bank:Checking: "0.60"    ; Standard threshold for regular banking

Here's how AI confidence scoring works in practice with Beancount:

# Example 1: High-confidence transaction (Score: 0.95)
2025-05-15 * "Monthly Rent Payment" "May 2025 rent"
  Expenses:Housing:Rent              2000.00 USD
  Assets:Bank:Checking             -2000.00 USD
  confidence: "0.95"  ; Regular monthly pattern, consistent amount

# Example 2: Medium-confidence transaction (Score: 0.75)
2025-05-16 * "AWS" "Cloud services - unusual spike"
  Expenses:Technology:Cloud           850.00 USD  ; Usually ~500 USD
  Liabilities:CreditCard            -850.00 USD
  confidence: "0.75"  ; Known vendor but unusual amount

# Example 3: Low-confidence transaction (Score: 0.35)
2025-05-17 * "Unknown Vendor XYZ" "Consulting services"
  Expenses:Professional:Consulting   15000.00 USD
  Assets:Bank:Checking             -15000.00 USD
  confidence: "0.35"  ; New vendor, large amount, unusual pattern
  risk_factors: "first-time-vendor, high-value, no-prior-history"

# Example 4: Pattern-based confidence scoring
2025-05-18 * "Office Supplies" "Bulk purchase"
  Expenses:Office:Supplies           1200.00 USD
  Assets:Bank:Checking             -1200.00 USD
  confidence: "0.60"  ; Higher than usual amount but matches Q2 pattern
  note: "Similar bulk purchases observed in previous Q2 periods"

# Example 5: Multi-factor confidence assessment
2025-05-19 ! "International Wire" "Equipment purchase"
  Assets:Equipment:Machinery        25000.00 USD
  Assets:Bank:Checking            -25000.00 USD
  confidence: "0.40"  ; Multiple risk factors present
  risk_factors: "international, high-value, weekend-transaction"
  pending: "Documentation review required"