wash-sale-detection
Wash sale detection under 2025 US crypto rules with 61-day window monitoring, disallowed loss tracking, and safe re-entry countdown
What this skill does
# Wash Sale Detection
Detect wash sales under current US crypto tax rules (effective 2025), monitor the 61-day window around realized losses, track disallowed losses with basis adjustments, and compute safe re-entry countdowns.
> **Disclaimer**: This skill provides informational analysis only. It is NOT tax advice. Consult a qualified tax professional or CPA for guidance on your specific situation. Tax law is complex, and the application of wash sale rules to cryptocurrency may vary based on individual circumstances, IRS guidance updates, and court rulings.
## Background
Before 2025, cryptocurrency was not subject to the wash sale rule because digital assets were classified as property rather than securities. The **Infrastructure Investment and Jobs Act** and subsequent IRS rulemaking extended wash sale treatment to digital assets beginning January 1, 2025.
Under **IRC Section 1091** (as amended for digital assets), if you sell or dispose of a cryptocurrency at a loss and acquire a **substantially identical** asset within a **61-day window** (30 days before the sale through 30 days after), the loss is **disallowed** for tax purposes. The disallowed loss is added to the cost basis of the replacement position.
## Key Concepts
### The 61-Day Window
```
Day -30 ................. Day 0 ................. Day +30
|--- 30 days before ---|--- sale day ---|--- 30 days after ---|
^ ^ ^
Window opens Loss realized Window closes
```
- **Day 0**: The day you sell a position at a realized loss
- **Days -30 to -1**: Purchases in this range trigger a wash sale retroactively
- **Days +1 to +30**: Purchases in this range trigger a wash sale prospectively
- The window is **calendar days**, not trading days
### Substantially Identical Assets
For crypto, "substantially identical" generally means the **same token**. Selling SOL at a loss and buying SOL within 30 days is a wash sale. Selling SOL and buying ETH is not (they are different assets).
Edge cases that may be scrutinized:
- Wrapped vs unwrapped versions of the same token (e.g., SOL vs wSOL)
- Tokens across different chains (e.g., USDC on Solana vs USDC on Ethereum)
- Derivative tokens that track the same underlying (e.g., stSOL and SOL)
### Disallowed Loss and Basis Adjustment
When a wash sale occurs:
1. The realized loss is **disallowed** — you cannot deduct it in the current tax year
2. The disallowed loss is **added to the cost basis** of the replacement position
3. The holding period of the original position may carry over to the replacement
**Example**:
- Buy 10 SOL at $100 each (cost basis: $1,000)
- Sell 10 SOL at $80 each (proceeds: $800, loss: $200)
- Buy 10 SOL at $85 within 15 days (wash sale triggered)
- New cost basis: $850 + $200 disallowed loss = **$1,050**
- The $200 loss is not gone — it is deferred into the new position
## Prerequisites
- Python 3.10+
- No external dependencies required (standard library only)
- Trade history data in CSV or structured format with: date, action (buy/sell), token, quantity, price, proceeds, cost basis
## Capabilities
1. **Wash Sale Scanning** — Analyze a trade history and flag all wash sale violations
2. **61-Day Window Monitoring** — Track open windows for recent loss-generating sales
3. **Disallowed Loss Calculation** — Compute the exact disallowed amount per wash sale
4. **Basis Adjustment Tracking** — Show adjusted cost basis for replacement positions
5. **Safe Re-Entry Countdown** — For each token sold at a loss, show days remaining until safe to re-enter
6. **Automation Hazard Detection** — Flag copy-trade systems or bot strategies that may inadvertently trigger wash sales
## Quick Start
```python
from datetime import date
# Define your trade history
trades = [
{"date": date(2025, 3, 1), "action": "buy", "token": "SOL", "qty": 10, "price": 100.0},
{"date": date(2025, 3, 15), "action": "sell", "token": "SOL", "qty": 10, "price": 80.0},
{"date": date(2025, 3, 25), "action": "buy", "token": "SOL", "qty": 10, "price": 85.0},
]
# Check for wash sales
from scripts.wash_sale_scanner import WashSaleScanner
scanner = WashSaleScanner(trades)
results = scanner.scan()
for ws in results.wash_sales:
print(f"WASH SALE: {ws.token} — Loss ${ws.disallowed_loss:.2f} disallowed")
print(f" Sale: {ws.sale_date} | Re-entry: {ws.replacement_date}")
print(f" Adjusted basis: ${ws.adjusted_basis:.2f}")
# Check safe re-entry countdowns
for countdown in results.countdowns:
print(f"{countdown.token}: {countdown.days_remaining} days until safe re-entry")
```
## Use Cases
### 1. End-of-Year Tax Review
Scan your full year of trading activity to identify all wash sales before filing taxes. Generate a report showing total disallowed losses and adjusted cost bases.
### 2. Real-Time Monitoring
Before placing a buy order, check whether the token has an open wash sale window from a recent loss. Avoid inadvertent wash sales by waiting for the countdown to expire.
### 3. Copy-Trade and Bot Audit
Automated trading systems (copy-trading bots, DCA bots, grid bots) frequently trigger wash sales because they buy and sell the same tokens repeatedly. Run this scanner on bot trade exports to quantify the tax impact.
### 4. Tax-Loss Harvesting Coordination
When executing a tax-loss harvesting strategy, use the safe re-entry countdown to plan when you can re-enter positions. Swap into a non-identical asset during the 30-day window if you want to maintain market exposure.
### 5. Multi-Account Wash Sale Detection
The wash sale rule applies across all accounts controlled by the same taxpayer. If you trade SOL on multiple exchanges or wallets, aggregate the trade history before scanning.
## Edge Cases and Automation Hazards
### DCA Bots and Grid Bots
Dollar-cost averaging bots that buy a token weekly will almost certainly trigger wash sales if the token is also sold at a loss during the same period. The scanner flags overlapping buy/sell patterns within the 61-day window.
### Copy-Trading
If a copy-trade system sells a token at a loss and the leader re-enters within 30 days, your copied trades inherit the wash sale. There is no "I didn't place the trade" exception.
### Partial Fills and Multiple Lots
When a sale at a loss is followed by multiple smaller purchases, the wash sale applies to each purchase up to the quantity of the loss-generating sale. The scanner handles partial matching.
### Cross-Wallet Transfers
Transferring tokens to another wallet you control and selling there does not avoid the wash sale rule. The rule follows the taxpayer, not the account.
## Safe Re-Entry Strategy
After selling a token at a loss:
1. **Wait 31 calendar days** before repurchasing the same token
2. During the waiting period, consider holding a **non-identical substitute** (e.g., sell SOL, hold ETH for exposure to crypto broadly)
3. Use the countdown timer to know exactly when re-entry is safe
4. Set calendar reminders for window expiration dates
```
Token: SOL
Sale Date: 2025-03-15
Loss: $200.00
Window Closes: 2025-04-14
Days Remaining: 12
Status: DO NOT BUY — wash sale window active
```
## Basis Adjustment Walkthrough
Detailed step-by-step basis adjustment example:
```
TRADE 1: Buy 100 SOL @ $150.00 → Basis: $15,000.00
TRADE 2: Sell 100 SOL @ $120.00 → Proceeds: $12,000.00, Loss: $3,000.00
TRADE 3: Buy 100 SOL @ $125.00 → Basis before adjustment: $12,500.00
(within 30 days of Trade 2)
WASH SALE TRIGGERED:
Disallowed loss: $3,000.00
Adjusted basis: $12,500.00 + $3,000.00 = $15,500.00
Effective price: $155.00 per SOL (not $125.00)
Later sale at $160.00:
Proceeds: $16,000.00
Adj. basis: $15,500.00
Gain: $500.00 (not $3,500.00)
The $3,000 loss is recovered through the higher basis.
```
## Files
### References
- `references/planned_features.md` — Wash sale rules in depth, 61-day window mechanics, basisRelated in Data & Analytics
clawarr-suite
IncludedComprehensive management for self-hosted media stacks (Sonarr, Radarr, Lidarr, Readarr, Prowlarr, Bazarr, Overseerr, Plex, Tautulli, SABnzbd, Recyclarr, Unpackerr, Notifiarr, Maintainerr, Kometa, FlareSolverr). Deep library exploration, analytics, dashboard generation, content management, request handling, subtitle management, indexer control, download monitoring, quality profile sync, library cleanup automation, notification routing, collection/overlay management, and media tracker integration (Trakt, Letterboxd, Simkl).
querying-soql
IncludedSOQL query generation, optimization, and analysis with 100-point scoring. Use this skill when the user needs SOQL/SOSL authoring or optimization: natural-language-to-query generation, relationship queries, aggregates, query-plan analysis, and performance or safety improvements for Salesforce queries. TRIGGER when: user writes, optimizes, or debugs SOQL/SOSL queries, touches .soql files, or asks about relationship queries, aggregates, or query performance. DO NOT TRIGGER when: bulk data operations (use handling-sf-data), Apex DML logic (use generating-apex), or report/dashboard queries.
app-store-optimization
IncludedApp Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking changes.
habit-flow
IncludedAI-powered atomic habit tracker with natural language logging, streak tracking, smart reminders, and coaching. Use for creating habits, logging completions naturally ("I meditated today"), viewing progress, and getting personalized coaching.
app-store-optimization
IncludedApp Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking changes.
visualizing-data
IncludedBuilds dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.