Tax Loss Harvesting Assistant Calculator
Calculate tax loss harvesting assistant with our free tool. Get data-driven results, visualizations, and actionable recommendations.
Calculator
Adjust values & calculateLoss Application Breakdown
Formula
Realized losses first offset capital gains dollar-for-dollar at the applicable capital gains rate. Remaining losses offset up to $3,000 of ordinary income at your marginal tax rate. Any excess carries forward to future years. The net benefit subtracts estimated transaction costs from immediate tax savings.
Last reviewed: December 2025
Worked Examples
Example 1: Offsetting Capital Gains
Example 2: Small Loss with No Gains to Offset
Background & Theory
The Tax Loss Harvesting Assistant applies the following established principles and formulas. Large language models process text by breaking it into tokens, sub-word units produced by algorithms such as byte-pair encoding. In English, one token approximates four characters or three-quarters of a word on average, though this ratio varies considerably across languages and code. A 1000-word document typically requires around 1300 to 1500 tokens. Token count drives both context window constraints and inference billing, making accurate estimation essential for budgeting API usage. The capability of a neural network scales primarily with its parameter count. Parameters are the numerical weights adjusted during training via gradient descent. GPT-3 contains 175 billion parameters; larger models in the trillion-parameter range require correspondingly greater compute and memory. Training compute is measured in floating-point operations (FLOPs): the Chinchilla scaling laws derived by Hoffmann et al. in 2022 show that optimal training allocates roughly 20 tokens per parameter, meaning a 70B-parameter model benefits from approximately 1.4 trillion training tokens. Inference latency depends on model size, hardware, and batching strategy. Running a 7B-parameter model in FP16 precision requires roughly 14 GB of GPU VRAM (2 bytes per parameter), while INT8 quantisation halves this to around 7 GB with modest quality loss, and INT4 reduces it to approximately 3.5 GB. This quantisation trade-off between memory, speed, and accuracy is central to deploying models on consumer hardware. Perplexity measures how surprised a language model is by a given text corpus; lower perplexity indicates better predictive accuracy. Embedding dimensions determine the size of the dense vector representations used to encode semantic meaning. Models like OpenAI's text-embedding-ada-002 produce 1536-dimensional vectors, while compact models may use 384 dimensions. Context window size defines the maximum token span a model can attend to in a single forward pass. Extending context windows from 4K to 128K tokens enables document-scale reasoning but substantially increases memory requirements, as the attention mechanism scales quadratically with sequence length without architectural modifications such as flash attention.
History
The history behind the Tax Loss Harvesting Assistant traces back through the following developments. The mathematical neuron model published by Warren McCulloch and Walter Pitts in 1943 first proposed that logical functions could be computed by networks of simple threshold units, planting the seed of neural computation. Frank Rosenblatt's Perceptron, introduced in 1957 and implemented in custom hardware by 1960, could learn linear classifiers from examples and generated enormous public excitement before Marvin Minsky and Seymour Papert's 1969 book rigorously analysed its fundamental limitations, demonstrating it could not learn the simple XOR function. The first AI winter, roughly 1974 to 1980, followed as funding agencies in the US and UK grew disillusioned with unrealised promises. A second wave of interest during the 1980s produced rule-based expert systems deployed in medicine and finance, and saw the re-derivation of backpropagation by Rumelhart, Hinton, and Williams in 1986, making it practical to train multi-layer networks on real problems. A second winter from 1987 to 1993 followed as expert systems proved brittle and hardware remained insufficient for genuine deep learning. The deep learning revival crystallised at the ImageNet Large Scale Visual Recognition Challenge in 2012, when Alex Krizhevsky's convolutional network AlexNet slashed the top-5 error rate by nearly 11 percentage points compared to the prior year's winner. This demonstrated that deep networks trained on GPUs with large labelled datasets could achieve human-competitive image recognition. Subsequent years saw rapid advances in recurrent networks, sequence-to-sequence models, and the attention mechanism, culminating in the transformer architecture introduced by Vaswani et al. in 2017. OpenAI released GPT-1 in 2018, demonstrating that unsupervised pre-training on large text corpora followed by task-specific fine-tuning could transfer knowledge broadly across language tasks. GPT-2 in 2019 demonstrated surprisingly fluent long-form text generation. GPT-3 in 2020, with 175 billion parameters, showed that scale alone could unlock few-shot learning. Kaplan et al.'s 2020 scaling laws paper provided the theoretical grounding. ChatGPT launched in November 2022, reaching one million users within five days and igniting mainstream global awareness of large language models.
Frequently Asked Questions
Formula
Tax Savings = (Loss Offsetting Gains x CG Rate) + (min(Remaining, $3000) x Income Rate)
Realized losses first offset capital gains dollar-for-dollar at the applicable capital gains rate. Remaining losses offset up to $3,000 of ordinary income at your marginal tax rate. Any excess carries forward to future years. The net benefit subtracts estimated transaction costs from immediate tax savings.
Worked Examples
Example 1: Offsetting Capital Gains
Problem: You bought stock for $50,000, now worth $35,000. You have $10,000 in realized capital gains this year. You are in the 24% tax bracket, short-term holding.
Solution: Unrealized Loss: $50,000 - $35,000 = $15,000\nGains Offset: min($15,000, $10,000) = $10,000\nTax Savings (gains): $10,000 x 24% = $2,400\nRemaining Loss: $15,000 - $10,000 = $5,000\nOrdinary Income Offset: min($5,000, $3,000) = $3,000\nTax Savings (income): $3,000 x 24% = $720\nCarryforward: $5,000 - $3,000 = $2,000\nImmediate Savings: $2,400 + $720 = $3,120
Result: Harvest recommended | Immediate savings: $3,120 | Carryforward: $2,000 (1 year)
Example 2: Small Loss with No Gains to Offset
Problem: You bought a fund for $20,000, now worth $18,000. No capital gains this year. 32% tax bracket, long-term holding.
Solution: Unrealized Loss: $20,000 - $18,000 = $2,000\nGains Offset: $0 (no gains)\nOrdinary Income Offset: min($2,000, $3,000) = $2,000\nTax Savings: $2,000 x 32% = $640\nTransaction Cost: $18,000 x 0.2% = $36\nNet Benefit: $640 - $36 = $604
Result: Harvest recommended | Net benefit: $604 | Loss applied to ordinary income
Frequently Asked Questions
When is tax-loss harvesting most beneficial?
Tax-loss harvesting provides the greatest benefit in these situations: when you have significant realized capital gains to offset (from selling appreciated stocks, mutual fund distributions, or business sales), when you are in a high tax bracket (24%+ federal), when losses are substantial enough that the tax savings exceed transaction costs, and when you can maintain equivalent market exposure through substitute investments. The strategy is particularly valuable in volatile markets where temporary paper losses can be converted to real tax benefits. Year-end is the most common time, but opportunities arise throughout the year. High-income investors in the 37% bracket save $0.37 per dollar of short-term loss harvested.
What are the risks and downsides of tax-loss harvesting?
While generally beneficial, tax-loss harvesting has some caveats. Transaction costs (commissions, bid-ask spreads) reduce the net benefit, though with commission-free trading this is minimal. If you violate the wash sale rule, you lose the current deduction. The new lower cost basis means larger gains when you eventually sell, so tax-loss harvesting defers taxes rather than eliminating them entirely (unless you donate the shares or hold until death for a stepped-up basis). Frequent trading creates complexity at tax time and requires careful record keeping. The strategy also requires discipline to sell losing positions, which can be psychologically difficult. Finally, if your tax bracket changes significantly in the future, the deferred benefit may be at a different rate.
Does Tax Loss Harvesting Assistant Calculator work offline?
Once the page is loaded, the calculation logic runs entirely in your browser. If you have already opened the page, most calculators will continue to work even if your internet connection is lost, since no server requests are needed for computation.
What inputs do I need to use Tax Loss Harvesting Assistant Calculator accurately?
Each field is labelled with the required unit (metric or imperial). Gather your source values before starting — for example, a weight measurement in kilograms, a distance in metres, or a dollar amount — and enter them exactly as measured. The formula section on this page lists every variable and explains what each represents.
Is my data stored or sent to a server?
No. All calculations run entirely in your browser using JavaScript. No data you enter is ever transmitted to any server or stored anywhere. Your inputs remain completely private.
How do I verify Tax Loss Harvesting Assistant Calculator's result independently?
The Formula section on this page shows the equation used. You can reproduce the calculation manually or in a spreadsheet using those steps. Compare your answer against the worked examples in the Examples section, which use known reference values so you can confirm the calculator is behaving as expected.
References
Reviewed by Daniel Agrici, Founder & Lead Developer · Editorial policy