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Wednesday, 4 November 2026 · LondonENع
Rayan Azhari.Sustainability · Energy · Carbon · Built Environment · Data Science
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Seven Hundred and Fifty Times

In March 2025 the International Renewable Energy Agency put Syria's off-grid solar at 2 megawatts for 2024. A year later it put the same year at 1,500. Neither edition says why. Around that revision sit a rooftop build-out that, as far as I can find, Syrians mostly paid for themselves, and a state credit scheme that on my arithmetic reached at most one or two per cent of it.

By Rayan Azhari · 9 min read · Essays
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How a Cost of War Became a US$923 Billion Rebuild

An International Labour Organization factsheet says rebuilding Syria could cost "USD250 to USD 900+ billion". Follow its footnote and the upper figure turns out to be something else: the UN Development Programme's estimate of what the war cost, most of it output that was never produced. Here is the chain, link by link, and what the one number from a full assessment actually measures.

· 10 min

If Twenty-Five Billion Dollars Arrived Next Month

A hypothetical: the Syrian government receives US$25 billion next month. Could its banks hold the money, its offices turn it into projects, its laws carry the contracts and its people build them, and how long would it take? On the record I keep, the answer is not yet, not at that scale and not quickly, with exceptions the record names: multilateral grants, budget support and operators' own capital.

· 31 min

Fail Closed Data Validation: Why a Trading Bot Should Refuse to Boot on Bad Data

A data-quality gate that crash-loops on corrupt, stale or all-NaN data is not broken, it is working: the alternative is fail open, trading on garbage with silent defaults. Here is the war-story from both sides, the honest flip where an over-eager gate locked me out of a market, and the one portable rule: on bad data, refuse to trade and alert loudly, never proceed with a default that looks like data.

· 9 min

I Corrected My Own Book

My earlier book on Syria went on sale the week the World Bank published its estimate of what rebuilding the country would cost. My book said US$250 billion to US$400 billion. The Bank said US$216 billion. Here is what my range was missing, what else did not survive being checked, and why the corrections became a second book.

· 10 min

Fifty Billion Dollars of What? The Number Behind Syria's Sovereign Fund

The Financial Times reports that Syria's Sovereign Fund was valued at US$50 billion last year by the finance minister. The figure traces back to an unpublished ministry estimate relayed by an economist, with no date, method or basis. The only asset value the fund's own officials have given in public is about US$2.5 billion, and its deputy director said in August that its assets were still being counted. Set against the economy and the World Bank's capital stock, damage and reconstruction figures, here is what fifty billion would have to mean, and why, without a stated basis, it cannot yet be checked.

· 15 min

Bar Timestamp Convention: The Open vs Close Stamp That Leaks a Full Bar of Look-Ahead

A bar carries one timestamp, but that stamp is ambiguous: is a bar labelled 09:00 the open of the coming interval or the close of the last one? Vendors disagree, and reading the wrong edge is a clean one-bar look-ahead that sails through review. Here is the war-story, why the leak is invisible, and the ingest rule I now enforce on every feed.

· 9 min

Adjusted vs Unadjusted Close: The Dividend Double-Count That Fakes a Backtest Edge

Total-return and price-return series look identical on a chart and differ by percent-per-year underneath. Mix them, or fold dividends in twice, and a long-horizon backtest drifts a few percent above the honest one every year and compounds into a phantom edge. Here is the reconstruction check I now run: rebuild total return from price plus the dividend stream yourself and diff it against the vendor's adjusted series.

· 9 min

Survivorship Bias in a Backtest: The Delisted-Fraction Smell Test

Survivorship bias is the term every quant knows and almost nobody makes actionable. Here is the one-number diagnostic I run before I trust any cross-sectional backtest: count the dead names as a share of your universe over a decade. If it is near zero, your data quietly deleted the losers and every result is biased upward.

· 9 min

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