Crypto Taxes in Canada Explained Very Simply

I see more and more crypto tax Canada related questions in this community so decided to do a quick simple breakdown that works for most traders.

The Basic Rule: Canada treats crypto like stocks - you pay tax when you sell/trade for a profit.

Simple Example:

• You buy 1 Bitcoin for $30,000 CAD in January
• You sell it for $40,000 CAD in December
• Your profit = $10,000
• You pay capital gains tax on 50% of that profit = $5,000
• If you're in a 30% tax bracket, you owe $1,500 in taxes

Note: Include transaction fees (e.g., exchange fees) in your cost (called Adjusted Cost Base, or ACB) to reduce your taxable gain.

Crypto taxable events in Canada:

• Buying crypto = No tax
• Holding crypto = No tax
• Selling for profit = Tax on 50% of gains
• Selling for loss = You can claim the loss to reduce other gains
• Trading crypto-to-crypto = Also taxable (treat it like selling)
• Mining, staking, or airdrops: Treated as income (100% taxable) at the FMV when received. Later sales trigger capital gains/losses.

What You Need to Track:

• Date of purchase
• Purchase price in CAD
• Date of sale
• Sale price in CAD

Important Tips:

• Keep records of EVERYTHING
• If following manually is too much of a hustle, Use apps like Koinly (it's like TurboTax but for crypto in Canada). They gave our community 10% off the price via this link.
• If you're day-trading frequently, CRA might consider you a business (different rules)

Not financial advice - consult a tax pro if you're dealing with big money.

Official source for your reference: https://www.canada.ca/en/revenue-agency/programs/about-canada-revenue-agency-cra/compliance/cryptocurrency-guide.html

Hope this helps.

Entropy Diagonal Analysis

Given N binary sequences S₁, S₂, ..., S_N (length L), the Cantorian diagonal inversion is:

D = [¬S₁[1], ¬S₂[2], ..., ¬S_m[m]], where m = min(N, L)

For a binary matrix M ∈ {0,1}^{N×L}, D extracts the (i,i)-th bit of each row, inverts it, and scores entropy H(D). Entropy collapse (H << 1) signals hidden structure.

Submitting for public and technical review. I already have it under review at a journal, but I am too curious on the publics opinion to stay quiet in the meantime. This is the first empirical validation of Entropy Diagonalization Analysis (EDA), a method for exposing latent structure in large datasets that standard randomness tests ignore. You extract the diagonal from a stack of binary sequences, invert those bits, and measure Shannon entropy. True randomness yields entropy ~1; structure or bias manifests as a sudden drop.

I ran three experiments over the past year, each with full control runs (random/synthetic and shuffled):

Bitcoin blockchain: I processed 800,000+ block hashes (each 256 bits), forming sliding 256×256 matrices. For each, extracted and inverted the diagonal, then hashed that string with SHA-256 and counted leading zeros. True randoms (synthetic or shuffled) always match the geometric null; real data shows a heavy tail of high-leading-zero events, especially during difficulty jumps, confirming protocol-aligned structural bias.

Global seismic data: I ran a 42-day live deployment ingesting continuous broadband waveforms from hundreds of stations. Each station was processed independently in rolling windows, diagonalized, inverted, and tracked for persistent entropy drops. Drops cluster across stations, and the system triangulates probable earthquake origins. During the trial, entropy collapse predicted earthquake onsets with median lead time ~22 minutes (max ~45 min, matched 96% of catalogued events at high station counts). Control clusters and unmatched events were extensively analyzed.

SETI/FRB radio astronomy: I pulled two rare, multiband .fil filterbank files (256×1024 chunks) from the Green Bank Telescope containing known FRB121102 bursts. Each chunk was binarized, diagonals extracted/inverted, and entropy scored. Out of thousands of candidate events, only eight showed >5σ entropy drops, and every one matched a catalogued FRB burst. There were no false positives and no anomalies in quiet controls. Heatmaps make the result visually obvious.

All code and algorithms are fully documented. Null distributions, shuffled controls, and synthetic data are all included. The method is open, Lean formalized, and reproducible. If youre interested in seeing the paper let me know, I am seeking genuine feedback and can provide all information to back my claims.

Bitcoin vs AI - The Battle For TRUTH

Trust is unraveling. From misinformation to fragile institutions, our shared confidence is fraying at the very moment artificial intelligence is accelerating. In his conversation at the WSJ Leadership Institute, Yuval Noah Harari stresses that as AI grows in capability, our ability to cooperate and to place trust in one another becomes both more difficult and more essential, which creates a dangerous gap between power and legitimacy. YouTube The Wall Street Journal

The paradox is simple. If we want AI that is aligned with human values, we first need human systems that people actually trust. Harari’s point is that without stronger social trust and oversight, AI can amplify manipulation, centralize control, and outpace our governance capacity. He frames trust as a precondition for responsible use rather than a nice to have, which is why the current erosion of trust is so alarming. YouTube Facebook

Bitcoin offers a counterexample from a different domain. Satoshi Nakamoto designed a network that does not rely on trusting institutions or gatekeepers. As the white paper puts it, Bitcoin is “an electronic payment system based on cryptographic proof instead of trust.” The shift is from who do I trust to what can I verify. Bitcoin

That design matters in an age shaped by AI. Bitcoin shows that you can remove trust from the center of a system and still get coordination and integrity. Verification is open, rules are transparent, history is auditable, and control is distributed. Participants do not need to trust each other to settle transactions because the system embeds proof at every step. This is more than a financial innovation, it is a governance lesson. Bitcoin

When people worry about AI, they often worry about power without accountability. That worry is really a trust problem. A trustless architecture like Bitcoin provides a conceptual counterweight. It says that when complexity and power grow, you can still build systems where outcomes are constrained by verifiable rules that anyone can inspect.

The bridge between these worlds is a new approach to trust. Instead of concentrating trust in a small set of actors, we can design AI era infrastructure that embeds verifiability, transparency, and decentralization. Imagine standardizing cryptographic audit trails for high stakes model actions, publishing commitments to training data and safety constraints, and distributing oversight so that no single party can quietly change the rules. The point is not to bolt Bitcoin onto AI, the point is to learn from Bitcoin’s trustless blueprint and apply its principles where they make sense.

Harari warns that the social fabric must hold if we want AI to benefit people. Bitcoin demonstrates that there are ways to secure cooperation that do not depend on fragile promises. Taken together, these two insights form a path forward. Rebuild human trust where it is indispensable, and where trust is too brittle or too easy to abuse, replace it with mechanisms that let anyone verify the rules for themselves. If AI represents unprecedented capability, Bitcoin represents a proof that trust can be reimagined. The future will be shaped by whether we make both ideas work in tandem.