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trading-research-agents Autonomous trading agents for crypto that can't fudge their own results. Language models are good at reading a market and saying what they think. They are bad at arithmetic, they don't know what …

trading-research-agents Autonomous trading agents for crypto that can't fudge their own results. Language models are good at reading a market and saying what they think. They are bad at arithmetic, they don't know what a trade costs, and they will happily overrule a limit you set. This project uses them for the first part only. The system itself, fixed rules with no model involved, fetches the data and works out every figure. The agents read those figures and give an opinion. Then the system prices the trade, applies your limits, sizes it, and either writes it down or sends one order with the stop attached. Every decision and every refusal is kept on disk with its reason, so you can check afterwards what happened and why. It's an early release, so expect settings to change between versions. What works today: a universe of perpetuals found and measured by the system, a screener that picks where to look, the full autonomous cycle on live data, four analysts and up to three validators, stops and targets the analyst names inside bounds you set, the gates that decide, sizing from your own rules, real orders on a venue's practice environment behind a mandate you sign, a risk manager that can only make the desk more careful, and two auditors that read the records. Contents What it solves The usual problem with trading agents What happens here The model invents or miscalculates a number Models never compute. Every figure comes from the system, and a figure a model cites is checked against its brief, value included. Nobody knows what a trade cost until it's done The round trip (fees, spread, impact, funding, slippage) is priced before an order exists, and a trade whose claimed edge doesn't clear it is refused. The agent sizes its own position Size comes from your conviction ladder and your risk limit. No model can make a trade bigger. A model "decides" to ignore your settings Your declared thresholds decide. A model's opinion is recorded beside them, never instead of them. Something went wrong and there is no trace Each cycle keeps its snapshot, briefs, answers, the intent and what the venue said. Nothing is rewritten. A crash leaves a position with no stop The stop and take profit go to the venue attached to the entry, so they survive this process dying. Get started Six steps. You need Python 3.11 or newer and a key for one model provider (Groq has a free tier). No exchange account is needed until step 6. 1. Install. uv is the quickest way: uv tool install 'trading-research-agents[ccxt,openai,data]' Without uv, run pip install 'trading-research-agents[ccxt,openai,data]' inside a virtual environment. The extras are ccxt for exchange data and orders, openai for every OpenAI-compatible provider (Groq, DeepSeek, xAI, OpenRouter, a local server) and data for datasets. Add anthropic or google to the list if you use those. To add an extra later, run the same install again with the longer list; the per-feature pages say which extra they need. 2. Make a desk. A desk is a folder holding your settings and everything the agents decide. Name your provider and model here: tra init my-desk --profile autonomous --agent groq --model openai/gpt-oss-120b cd my-desk Add --venue okx (or any ccxt id) to trade somewhere other than Bybit. tra models groq lists the models your key can use. 3. Add your key. Copy the example file and put your key in it. Every command reads the .env next to strategy.toml on its own. cp .env.example .env # then set GROQ_API_KEY=... in .env 4. Check. tra check reads every setting against the others and names the one thing to fix next. A desk that is ready ends like this (shortened): ok execution: sandbox; nothing leaves the machine ok exits: the analyst names its stop and target; the stop is held between 20 and 300 bps ok risk per trade: 50 bps of equity at the stop, applied to every size the ladder produces ok provider groq: ready ready to run `tra run` 5. Run. tra run decides once on live market data, in the sandbox: nothing leaves your machine. tra run --every 15m keeps deciding, once per fifteen-minute bar, on the clock (:00, :15, :30, :45). Or type tra on its own for the console, which takes the same commands. tra run tra run --every 15m 6. When you want real orders. Start on the exchange's practice account: put its keys in .env , read the mandate with tra mandate , sign it with tra mandate --sign "Your Name" , and in [execution] of strategy.toml set mode = "venue" , a leverage and max_notional (the most one order may be worth). tra check lists whatever is still missing. Toward real orders has the whole path. Working on the code itself? See Development. What you see, in order 1. The console. Run tra inside your desk and it opens the console: what is ready, what isn't, and the next command to run. Under it is a prompt that takes every command and draws it right there. Arrow keys go back through what you typed, tab completes, help lists the commands and exit leaves. For scripts and cron, tra -c "run --now" runs one command and exits. 2. The pass over your instruments. Type run --every 15m and the desk first refreshes any data series that are due, one line each. Then it looks at every instrument on your list, one data pull apiece, and measures it. The screen steps through them live: 3. One cycle per instrument it takes. The system found the candidates and measured them; the screener chose among them and cited each instrument's own figures; the system's own order is kept beside the choice. The instruments taken get a full cycle. The terminal shows the whole pipeline from the start and updates it as each step begins and ends. Steps that run at the same time spin at the same time, each with its own clock, and the line underneath says who is working right now: 4. The result. When the cycle finishes, the table gives way to the pass over the universe, what was taken and what was held and why, then the data the cycle used and what the round trip costs: After that, what each validator argued, how conviction weighed the analysts, the risk at the stop, and whether the trade clears the desk's targets: 5. Between cycles. The desk says when the next pass is, on the clock and in minutes, for example next pass at 14:30:00 UTC, in 10m 57s , and counts down to it. An auditor woken by an alarm gets a spinner of its own. The same cycle as plain text ( --plain , a pipe or a log), from a real run on live data, shortened: cycle 2026-09-29T02-59-16Z BTCUSDT: 5.344s of agents, 15.042s of validators funding ccxt:bybit age 4.267s ohlcv ccxt:bybit age 32.746s orderbook ccxt:bybit age 7.544s macro risk-off: the operator declared a bullish state for US10Y; measured correlation is neutral (0.0) on a modest sample, weak evidence that does not overturn the declaration sentiment positive: declared state is bullish, and the measured figures both support it technical: long on range_reversion at 0.65 over 5 bars: the close is stretched low in the window read: zscore=-0.587, vwap_gap_bps=-3.99, ret_1_bps=1.59 validator contra oppose on 2 cited figure(s) validator pro support on 3 cited figure(s) conviction: 0.800 of 1.0 (macro 1.0, sentiment 0.5, technical 1.300) risk: 8.29 bps of equity at the stop against the declared 50 bps, size 0.010 cost: 12.87 bps round trip, 2.00 of it estimated gate: 25.0000 bps claimed clears 12.8723 bps cost plus 0.0000 bps margin refused targets: the claimed edge is 1.94x the round trip and the desk declared 2.0x; the cost is certain and the edge is not That trade was refused. It cleared the cost gate and still died, because this desk asks for an edge of twice the cost and got 1.94 times. A refusal names the figure behind it, every time. How one cycle decides flowchart TD find[["the system finds the universe and measures each instrument<br/>one data pull each; every value carries its source and age"]] --> wake wake{"trigger<br/>has anything moved since the last look?"} wake -- "no" --> held["held: one data pull, no tokens spent"] wake -- "yes" --> pick["the screener picks among what the system admitted<br/>citing each instrument's own figures"] pick -- "none taken" --> held pick -- "taken" --> briefs briefs["the system writes the briefs<br/>each role sees only the figures its job needs"] --> round1 subgraph round1["asked at the same time"] direction LR tech["technical analyst<br/>a direction, a confidence, a horizon"] macro["macro analyst"] chain["on-chain analyst"] mood["sentiment analyst"] end round1 --> price["the system prices the round trip<br/>fee, spread, impact, funding, slippage"] price --> exits{"the stop and the target<br/>the analyst's levels, held inside your bounds<br/>does the trade break even at half its kind winning?"} exits -- "no" --> refused exits -- "yes" --> round2["validators, as many as you declare<br/>a claim that cites no figure is thrown away"] round2 --> matrix{"confluence<br/>do your declared states agree with the direction?"} matrix -- "no" --> refused["a refusal that names its figure"] matrix -- "yes" --> size["conviction<br/>how much, from your ladder"] size --> risk{"risk per trade<br/>what does it lose at the stop?"} risk --> gate{"cost gate and targets<br/>does the edge clear the cost, by your margin?"} gate -- "no" --> refused gate -- "yes" --> intent["the intent, written into the cycle"] intent --> venue["venue mode only<br/>one order, the stop and take profit attached"] Who does what: Role Reads Answers Can never technical analyst candles, spread, funding, book imbalance, open interest change, and the regime on its timeframe and the higher ones you declare a direction, a setup from a fixed list, a confidence, a horizon, and where its stop and target sit size the trade, place a level outside your [exit] bounds, or run a trend setup in a range macro analyst dated rates and levels, dated text risk-on, risk-off or neutral propose a direction on-chain analyst chain and derivatives figures accumulation, distribution or neutral propose a direction sentiment analyst dated scores and notes you curate negative, neutral, positive, or not declared invent a mood no figure supports market screener what the system measured for each admitted instrument: move, spread, regime, volume, correlation to BTC which instruments to take, in what order, citing their figures add an instrument the system did not admit validators (pro, contra, neutral) the proposal and the figures behind it support, oppose or abstain, with cited claims decide, or cite a figure it wasn't given risk manager what the run recorded: round trips, the losing streak, your limits, the alarm that woke it continue, cut the size of new entries, pause instruments, or halt, each for a stated number of hours loosen a limit you set, or add size technology and quant auditors the cycles this desk recorded findings and proposed changes change anything The macro, on-chain and sentiment analysts read dated series you declare under sources/ . Each one is either a file you keep or a feed the desk refreshes itself (FRED, the Crypto Fear & Greed Index, or any paid vendor that answers JSON over https). tra sources show lists every series with where it came from and how old its last value is, and a value older than its max_age is shown as stale and never counted. Data sources has the details. Which validators run is yours to declare. A vote never decides on its own: it moves conviction by the weight you give it ( [conviction].validator_weight ), and a stance counts for what it says beyond its assignment, since a pro that supports is expected to. Analysts are allowed to disagree. Each one's view becomes a score between -1 and 1, computed from its own figures against the lines you drew, so a macro reading barely past its line counts as a small voice and one far past it counts as a big one. Conviction adds them up: agreement grows the size, opposition shrinks it in proportion, and only opposition that outweighs the case stops the trade. A short at 0.70 confidence against a barely bullish macro and sentiment scores 0.56 and trades at half size; against a strongly bullish context it scores 0 and doesn't trade. The technical analyst knows what kind of market it is working in before it decides. The system measures the regime on its timeframe (trending up, trending down, ranging or mixed) and on the higher timeframes you list in [run].context_intervals , and it hands over what your macro thresholds say this cycle as well. A setup that doesn't fit the regime, such as a trend trade in a sideways market, is refused by name; [regime] holds the lines. Everything after the analysts is arithmetic over numbers you declared. No model can place, cancel or resize an order, read a file outside the project, or edit your configuration. Agents has the full table of what an agent can reach, and The cycle walks through every step. Venues A cycle reaches venues through ccxt. Here is what that means in practice today: Venue What a cycle can do there Practice environment Run end to end by us Bybit (the default) everything: orders with the stop and take profit attached demo, testnet yes, on demo: real orders, exits attached, a resting order cancelled OKX everything demo (ccxt's testnet switch) yes, on demo: real orders, exits attached, closed by the venue's own exits Bitget, BingX, KuCoin Futures, Hyperliquid everything varies, see tra venues not yet Binance, Deribit, HTX, Gate, MEXC orders, but you carry the stop yourself ( exits = "none" ) varies not yet Kraken, Kraken Futures, Phemex data only: they can't report the position, so no order is sent "Everything" means ccxt reports the five things a safe order needs: send it, attach the exits, read the position, read the open orders, cancel. Only Bybit and OKX have been run end to end with real orders, so treat every other venue as untested: start on its practice environment and read what it sends. Adding the venue you trade on is four steps, and one command writes most of it: tra venues okx # what it can do, and which keys it signs with tra venues okx --setup # the blocks to paste into envelope.toml, data.toml, strategy.toml and .env Your fees are filled in from your account when it reports them, and from the venue's standard tier until then, labelled as such; a fee you write yourself is kept and checked against the account. Venues has the full table, the keys each venue needs, and the four steps. Models and providers You choose the provider and the model; nothing is hard-coded. Each provider reads its key from the environment (or the .env beside your config) and nowhere else. Provider Key in .env Install with Structured output Tested live here groq GROQ_API_KEY openai JSON schema, falling back to JSON mode; checked by the system yes: openai/gpt-oss-120b and openai/gpt-oss-20b , every live cycle in our records google GEMINI_API_KEY or GOOGLE_API_KEY google-genai JSON schema yes: gemini-flash-latest , as the fallback anthropic ANTHROPIC_API_KEY anthropic JSON schema unit-tested; default model claude-opus-5-5 openai OPENAI_API_KEY openai strict JSON schema unit-tested deepseek DEEPSEEK_API_KEY openai JSON mode preset only mistral MISTRAL_API_KEY openai JSON schema preset only xai XAI_API_KEY openai JSON mode preset only openrouter OPENROUTER_API_KEY openai JSON mode preset only qwen DASHSCOPE_API_KEY openai JSON mode preset only; pass --base-url for your region ollama none openai JSON mode preset only; a local server local none openai JSON mode any OpenAI-compatible server; pass --base-url 9router 9ROUTER_API_KEY openai JSON schema preset only; a local router Whatever the provider, every answer is parsed against a closed schema by the system before anyone reads it, so a model that only offers JSON mode is held to the same shape as one with strict schemas. [agents] provider = "groq" model = "openai/gpt-oss-120b" effort = "medium" # minimal | low | medium | high | xhigh | max, where the # vendor takes it; left out, the vendor's default applies fallback = [ "google:gemini-flash-latest" , "anthropic:claude-opus-5-5" ] [agents.roles.sentiment_analyst] # a role that only labels can run on a cheaper model model = "a-cheaper-model" effort = "low" fallback is tried in order when a provider is rate limited or down, with the same brief. A vendor's free tier meters each model on its own, so roles can share a provider across two models and each model keeps its own allowance; tra check judges the load per model and says when one is too much. Every call is a line in usage.jsonl with its tokens and latency, and nothing caps spend unless you declare a ceiling. tra keys --probe proves each key works without sending a prompt. A vendor of your own is about thirty lines (Model providers). Toward real orders What it trades: linear perpetual futures (USDT- or USDC-margined swaps) on any venue ccxt reaches, long and short, with the leverage and margin mode you declare. Spot, dated futures, inverse contracts and options are not built yet; until they are, venue mode checks the market type on every order and refuses anything that is not a linear perpetual. Boundaries says what adding one would take. An agent desk goes through three steps, and you can stay at any of them as long as you like: Step How What happens Needs 1. Sandbox tra run the full cycle on live data; every decision written down, no order sent a model key 2. Practice account tra run with [execution].mode = "venue" and environment = "demo" or "testnet" real orders on the exchange's practice account, exits attached, positions looked after a signed mandate, a leverage, max_notional , practice keys 3. Real capital the same, with environment = "mainnet" real orders with real money all of the above, plus the ladder at its canary tier ( tra ladder promote , with evidence) Steps 1 and 2 have been run end to end on Bybit's and OKX's practice accounts. Step 3 has not been run by us; treat it as yours to verify, small, first. If you bring a scripted strategy of your own instead of the agents, it has a research path of its own before any of this: tra data get BTCUSDT 2025-01-01 and tra backtest replay a day of the exchange's archive net of every cost, tra paper --minutes 30 runs it on the live feed with nothing sent, and tra live is a small canary session behind hard caps. The replay of that day printed net -294.19 bps for the example core, and it is shown as it came out: a backtest that only shows good news is one nobody should believe. The live tier has the details. Venue mode is the only mode that can lose money, so it has a door in front of it. Read the mandate with tra mandate ; when you accept it, tra mandate --sign "Your Name" keeps it in the project as MANDATE.md and signs [execution] with your name and today's date, in one step. Signing does not switch anything on. tra check names whatever else is missing. Your money, your orders, your positions Once orders flow, three things look after the money, all of them the system's own rules and none of them a model: Before an order When it is sent While it is open the account's real equity and free margin are read your leverage is applied at the venue and read back at the start of every pass, each position is checked against the venue sizing uses the smaller of the real equity and what you allow the desk an order whose liquidation would sit inside its stop is refused one that outlives its hold limit is closed with a reduce-only order an order may use only part of the free margin a taker entry is a limit IOC within your slippage band, never a naked market order one the venue's stop or take profit closed is written up with its real fees and funding a day's loss past your limit stops new entries the stop and take profit go with the entry, so they outlive this process the kill switch flattens everything before the desk stops Who decides what, in one line each: the analysts propose the direction, your risk rule sets the size, you choose the leverage, and no model touches the last two. Leverage never makes a trade bigger. It only decides how much margin the trade ties up and how far away liquidation sits. [execution] mode = "venue" environment = "demo" # demo | testnet | mainnet leverage = 3 # yours to choose; it never makes a trade bigger max_notional = 400 # the most one order may be worth, in quote currency margin_mode = "isolated" # isolated | cross hold_limit = "timeout" # close by time after [exit].timeout_bars; or horizon, or none Underneath, on every venue: the order goes only to the environment you declared (demo, testnet or mainnet); an open position or a working order refuses a second entry; a size off the venue's step is refused rather than rounded; a clock more than a second off the venue's refuses before anything is signed; and Ctrl-C or a service stop cancels a resting order before the process leaves. Every close goes to positions.jsonl and every finished trade to round_trips.jsonl . Capital, orders and positions has every setting, every refusal and the arithmetic behind each one. One thing to know before you rely on it: the position checks run at the start of each pass of tra run --every . With no desk running, an open position has its venue-side stop and take profit and nothing else, so a desk meant to run unattended belongs under a supervisor. Orders cross the book by default ( taker-taker ): they always fill, which is how most desks trade. Point [run].venue_key at a maker-taker channel and entries rest on the book as post-only orders instead - a lower fee, fewer fills, since a resting order can be left behind by the market. Either way the cost gate prices the fee of the channel you chose. Watching a desk, and what is kept The console runs commands in its own process, so closing it ends what it was running. A desk that should keep running belongs under a supervisor (systemd, a service manager, tmux ), and then you look at it from anywhere with: tra watch --every 5s # a read-only view built from the records the desk writes tra watch opens no connection, holds no key and takes no lock, so opening and closing it does nothing to the desk. What is kept, and where: What Where Notes everything one cycle did runs/cycles/<id>/ snapshot, briefs, answers, intent, placed.json , cycle.json , and inputs.json.gz with the raw inputs; cycle.json holds a sha256 of each file the pass over the universe runs/screens/ every instrument measured, what the screener chose and why every model call runs/cycles/usage.jsonl tokens, latency, price when the desk ran and what stopped it runs/cycles/scheduler.jsonl one line per start and stop every position closed, by time, by the venue or by the kill switch runs/cycles/positions.jsonl venue mode every finished trade with entry, exit, fees and funding runs/cycles/round_trips.jsonl venue mode; what the quant auditor reads the first equity reading of each day runs/cycles/equity.jsonl what the daily loss limit is measured from what you typed at the prompt .tra-history a convenience only; tra -c keeps none Command line Every command says what it did. Exit code 0 means cleared, 1 means a gate said no, 2 is a usage error. --json gives machine-readable output and --plain gives text instead of panels. Getting started Command What it does tra opens the console tra init my-desk --profile autonomous a project wired for the agents, every file commented tra check every setting read against the others, with what to fix tra keys --probe proves every key works; no key is printed, no prompt or order sent tra models groq the models your key can use on a provider, read from the vendor; no prompt sent tra init my-desk --profile autonomous --venue okx a desk wired for one exchange: data, orders, practice account, keys tra venues okx --setup what a venue can do, and the blocks to paste to trade on it tra mandate the text venue mode requires; --sign "Your Name" keeps it as MANDATE.md and signs [execution] in one step Running Command What it does tra run one autonomous cycle tra run --every 15m --cycles 12 twelve scheduled passes: the first now, then one on every fifteenth minute of the clock tra run --now ask the roster even if the trigger would have held a quiet market tra watch --every 5s a read-only view of a running desk tra audit both auditors over the records; nothing is applied tra backtest , tra paper , tra live replay, shadow, canary Data Command What it does tra data get BTCUSDT 2025-01-01 download a day, verify its checksum, audit it, write Parquet and funding tra data audit export.csv --mapping mapping.toml your own CSV, through the same audit tra data pull BTCUSDT one live snapshot through the chains in data.toml tra sources show , tra sources pull where the reading analysts' series come from and how old they are; refresh them now tra sources add macro us10y --from fred --id DGS10 --high bearish a series onto the shelf in one step: pulled now, its lines drawn from its own history tra universe which perpetuals discovery finds on the venue now, and what each filter took out tra runs verify every cycle's files against their hashes, and its briefs against its raw inputs tra runs prune --keep 90d what old cycle directories would be removed; add --yes (and --archive ) to do it The ladder, research and hosts Command What it does tra ladder show , promote , demote which tier the desk stands at; one step up with evidence, or down tra chain floor , observe , check , validate the research chain, layer by layer (The falsification chain) tra ledger show every idea that died, and what killed it tra agent session , eval , trace the research session, model comparison, a run read back tra mcp serve the read-only gates as tools for any MCP host What it guarantees, and what is not built yet Some rules are guarantees, enforced by the system and tests, and every change has to keep them: No model touches an order or a key. Models read figures and answer; the system sizes, prices, limits and sends. Models read figures and answer; the system sizes, prices, limits and sends. No model can loosen a control. A model can tighten a limit, step the ladder down or arm the kill switch; never widen, promote or disarm. A model can tighten a limit, step the ladder down or arm the kill switch; never widen, promote or disarm. One decision, at most one order. A venue error is recorded and the next cycle decides again; nothing is retried around real capital. A venue error is recorded and the next cycle decides again; nothing is retried around real capital. Stale or missing data is refused, never read as zero or as current. Other things are simply not built yet, and contributions are welcome: spot, inverse, dated futures and options; decentralised venues; more than one account; transfers and treasury; routing and execution algorithms; portfolio accounting. Boundaries lists each one with what it does today and what a contribution would need. The example strategy cores are examples: your edge is yours to declare. Liability says plainly where responsibility sits when real capital is involved. Documentation The full documentation is at docs.bijectivelabs.dev. Its source is in docs/ , so a change in behaviour and the page that describes it go in the same pull request. Development git clone https://github.com/BijectiveLabs/trading-research-agents && cd trading-research-agents uv sync --all-extras uv run ruff check . && uv run ruff format --check . uv run mypy uv run pytest CONTRIBUTING.md has the branch flow and the standards the code is held to. License MIT. See LICENSE; NOTICE lists the third-party software this package builds on. Built by Bijective Labs.

Reason to trust Strict editorial policy that focuses on accuracy, relevance, and impartiality Created by industry experts and meticulously reviewed The highest standards in reporting and publishing How Our News is Made Strict editorial …

Reason to trust Strict editorial policy that focuses on accuracy, relevance, and impartiality Created by industry experts and meticulously reviewed The highest standards in reporting and publishing How Our News is Made Strict editorial policy that focuses on accuracy, relevance, and impartiality Ad discliamer Morbi pretium leo et nisl aliquam mollis. Quisque arcu lorem, ultricies quis pellentesque nec, ullamcorper eu odio. Geneva, Switzerland, October 12, 2026 — TRON DAO, the community-governed DAO dedicated to accelerating the decentralization of the internet through blockchain technology and decentralized applications (dApps), today highlighted key findings from its State of TRON Q3 2026 ecosystem report. The report examines network activity, stablecoin settlement, liquidity, tokenomics, developer activity, decentralization, and protocol governance during the third quarter of 2026. TRON surpassed $30 trillion in cumulative transaction volume in Q3 2026, marking another milestone for a network used as infrastructure for stablecoin settlement and digital-asset transfers. At the same time, stablecoin supply and total value locked continued to expand, while the network’s active-address base grew and transaction activity remained broadly steady. Key Insights In Q3, TRON surpassed $30 trillion in cumulative transaction volume. Stablecoin settlement reached $2.21 trillion in the Q3 reporting period, with average daily volume rising 3.9% from Q2 to $24.05 billion. Canary Capital launched the Canary Staked TRX ETF (TRXS) on September 9, 2026. TRXS gives investors exposure to the spot price of TRX—the native utility and governance token of the TRON blockchain network—alongside passive net staking rewards reflected directly in the fund’s daily net asset value. TRON DAO’s team rang the Cboe Closing Bell in Chicago on September 29 to celebrate its launch. TRON expanded its ecosystem collaborations in Q3 2026 , adding institutional custody and staking, new trading venues, and digital dollar products through Anchorage Digital, Bitnomial, Backpack, Ethena, and Ducat . Payment, wallet, cross-chain, and developer integrations also broadened access and strengthened the infrastructure for payment, treasury, and DeFi applications. Stablecoin supply and Total Value Locked continued to expand. USDT and USDD supply reached $93.88 billion, up 5.5% from Q2, while TVL including staking hit $28.21 billion and TVL excluding staking (DeFi TVL) hit $13.05 billion The active-address base widened while transaction frequency held steady. Average rolling monthly active addresses increased 9.8% to 18 million. Network fee generation moderated. Fees totaled $701.4 million during Q3, averaging $7.62 million per day, down 4.0% from Q2. The average daily transaction fee fell from $0.668 to $0.643. TRON’s ecosystem continued to broaden in Q3, with new integrations spanning institutional services, trading, stablecoins, payments, wallets, and developer infrastructure. Anchorage Digital added TRX staking and TRC-20 custody, while Bitnomial and Backpack expanded access to TRX markets. Ethena brought USDe and sUSDe to TRON, and Ducat connected its Bitcoin-backed financial infrastructure to the network’s stablecoin ecosystem. The quarter also brought new capabilities for users and developers. MoonPay introduced gasless transactions through Trust Wallet, removing the need to hold TRX for network fees, while Bitcoin.com Wallet added native support for TRX and USDT-TRC20. MetaMask connectivity expanded across B.AI, SUN.io, JustLend DAO, and BitTorrent, while Fireblocks Flow added support for payments from TRON wallets. Eco and STON.fi expanded cross-chain stablecoin connectivity, and KiiChain linked TRON assets with its 24/7 foreign-exchange and payment infrastructure. For developers, Privy expanded wallet, transaction, and policy-management capabilities, while OpenZeppelin brought its smart contract libraries and security tooling to the network. Together, these integrations strengthened the infrastructure available for payments, treasury management, and DeFi applications on TRON. TRON’s third-quarter developments underscore continued growth in stablecoin settlement, liquidity, network activity, and ecosystem infrastructure, reflecting sustained demand for its financial infrastructure. With these trends strengthening, TRON enters Q4 with an increasingly established role in global stablecoin settlement and digital asset activity. Read the full report from TRON here. About TRON DAO TRON DAO is a community-governed DAO dedicated to accelerating the decentralization of the internet via blockchain technology and dApps. Founded in September 2017, the TRON blockchain has experienced significant growth since its MainNet launch in May 2018. Today, TRON hosts the largest circulating supply of USD Tether (USDT) stablecoin, which currently exceeds $94 billion. As of October 2026, the TRON blockchain has recorded over 408 million in total user accounts, more than 15 billion in total transactions, and over $28 billion in total value locked (TVL), based on TRONSCAN. Recognized as the global settlement layer for stablecoin transactions and everyday purchases with proven success, TRON is “Moving Trillions, Empowering Billions.” TRONNetwork | TRONDAO | X | YouTube | Telegram | Discord | Reddit | GitHub | Medium | Forum Media Contact Yeweon Park press@tron.network

Stock Market Today: Stocks Are Mixed to Begin Big Week of Earnings; Bond Market Closed TodayInvestopedia S&P 500 is little changed to start the week; Micron leads tech lower: Live updatesCNBC Stock market today: …

Stock Market Today: Stocks Are Mixed to Begin Big Week of Earnings; Bond Market Closed TodayInvestopedia S&P 500 is little changed to start the week; Micron leads tech lower: Live updatesCNBC Stock market today: Dow gains, Nasdaq slips as oil prices riseYahoo… The Fine Print: The following comments are owned by whoever posted them. We are not responsible for them in any way.

It's crunch time. With less than a month to go until the 2026 midterm elections, Democratic and Republican candidates for the U.S. House of Representatives nationwide are making one last push to persuade voters …

It's crunch time. With less than a month to go until the 2026 midterm elections, Democratic and Republican candidates for the U.S. House of Representatives nationwide are making one last push to persuade voters in their districts — and help their party win or retain control of the chamber on Nov. 3. The stakes are high. President Trump has enjoyed complete Republican control of Congress for the first half of his second term. Flipping the House would give Democrats the power to block parts of the president's agenda, investigate his administration and even impeach him (again) . Historically speaking, a president's party almost always loses seats when voters return to the polls to judge his first two years in office. (The average number lost since World War II? Twenty-six .) Sometimes, the backlash is so pervasive that the president's party loses seats across a wide range of competitive districts — a phenomenon known as a "wave" election. Advertisement Advertisement Advertisement Advertisement But the past doesn't necessarily predict the future, and Republicans have certain advantages today that they didn't have before. Here's what needs to happen for each party to come out on top. The races to watch The first thing to know about any House election is that while all 435 congressional districts are technically up for grabs, roughly 83% of those contests (representing about 360 seats) are not considered competitive. House incumbents have always had huge advantages: money and name recognition, mostly. But their head start has only widened in recent years as partisan polarization and redistricting have helped make the vast majority of congressional districts "safe" for one party . These days, incumbents tend to focus more on fending off primary challengers. The general election can be a breeze in comparison. As a result, this year's true battleground is relatively small. According to the nonpartisan Cook Political Report's " race ratings ," just 21 contests are genuine "tossups" at this point. Another 12 "lean Democratic," and 9 "lean Republican." There's also a third competitive category that includes 16 seats where Democrats are the "likely" winners, and 18 seats where the same could be said about Republicans. That's 76 competitive districts — again, out of 435. Advertisement Advertisement It's clear from looking at this year's ratings which party is playing more defense and which is playing more offense — and where. Six Democratic seats are too close to call; two more lean Republican; another three are likely to break for the GOP; and three are rated solidly Republican. That's a total of 14 potential blue-to-red flips for Democrats to worry about. Republicans, by contrast, have to worry about 25 red-to-blue flips — including a full 15 in the tossup category. Geographically, this year's tossups are concentrated in certain kinds of places: Rust Belt districts in states that have swung from party to party in the past (such as Michigan, Pennsylvania and Ohio); Mid-Atlantic suburbs (in states such as New York, Virginia and North Carolina) where Trump has scrambled some of the usual coalitions; and Sun Belt districts (think Florida, Texas, California and Nevada) where Latinos factor heavily. Trump carried many of these areas in 2024 . Overall, the map gives Democrats more pickup options — which could mean more paths to a House majority. The path to a Democratic House Senate and governor contests can be quirky — more state-specific and personality-driven. But national dynamics tend to play a larger role in shaping the composition of the incoming House. Advertisement Advertisement So it's not just a matter of who has more targets on the board. It's that the board itself tends to tilt to one side or the other, taking most of those targets with it. A recent example was the 2010 midterm election, when impatience with America's slow recovery from the 2008 financial collapse and frustration with President Barack Obama's response helped Republicans gain 64 House seats. Beyond the history of midterm backlash against the president's party, Democrats have two big advantages right now. One is that the current Republican majority is so minuscule — just 219 votes to 214, including an independent who caucuses with Republicans — that Democrats only need a net gain of three seats to win back the House. The other is that the board seems to be tilting in Democrats' favor — assuming the polls are right. Surveys suggest there's one underlying factor pulling the national electorate to the left: dissatisfaction with Trump. According to the data journalists at Silver Bulletin , 60% of Americans, on average, currently disapprove of how Trump is handling his job compared with 37% who approve. That puts the president's net disapproval rating at about 23 percentage points. Advertisement Advertisement Trump is earning even lower marks on voters' top issue this year: the cost of living . Shortly after he returned to office, roughly equal numbers of Americans approved and disapproved of how the president was handling inflation. But today — as tariffs and the Iran war help drive prices up — 72% disapprove and 24% approve . That's a 48-point gap. President Trump arrives to speak at a campaign rally at F&M Bank Arena on Oct. 10, 2026, in Clarksville, Tenn. (AP Photo/Alex Brandon) Democrats aren't particularly popular. But for the party in power, it may be hard to overcome this level of dissatisfaction with the status quo. That's why, when pollsters ask their so-called generic ballot question — If an election for U.S. Congress were held today, which party's candidate would you vote for in your district? — Americans are currently giving Democrats a substantial lead of about 9 percentage points , on average. Making matters worse for Republicans, that lead has grown over the last month as some voters who favored Trump in 2024 — including some Latinos and independents — say they're planning to vote Democratic. Over the last two weeks, Cook has shifted 33 of its race ratings toward the Democrats as well. To put a 9-point lead in perspective, Democrats were leading by an average of 7 points on the generic ballot at this point in 2018, with Trump as president and Republican majorities in the House and Senate. Four weeks later, they picked up 41 seats and flipped the House. Advertisement Advertisement If the polls are right, Democrats might win control of the House again. The path to a Republican House But are the polls right? In recent days, Republicans have pointed to some surprising survey results on the Senate side — including Democratic leads in rural red states such as Iowa , Kansas and Nebraska — to argue that pollsters might be misjudging public sentiment. They're not wrong to say the polls have missed in the past. Polling errors are commonplace; they happen in most elections. But they tend to be random and to change directions from one cycle to another . The Trump era has been a little different. In each of the three presidential contests that Trump himself has competed in, the final average of polls conducted before Election Day underestimated Republicans' performance in the popular vote by an average of 3.8 percentage points, according to data journalist Nate Silver . The theory is that a phenomenon called nonresponse bias — in which Democratic voters are more eager or available to respond to pollsters than Republican voters — might have made Democratic support look stronger than it actually was. Advertisement Advertisement Pollsters try to adjust for this by weighting their results — that is, using statistical methods to give less weight to respondents who are overrepresented demographically and more weight to respondents who are underrepresented demographically. But it's difficult to predict in advance who will show up on Election Day. This time around, there are signs that rural Republicans are not responding to pollsters in certain states . Could that mean Republican candidates in those places will outperform their polling? Possibly. But experts say it may also mean that rural Republicans are less supportive of the GOP than they were in 2024 — potentially voting Democratic or staying home on Nov. 3. A voter walks toward his polling place. (REUTERS / REUTERS) After all, there's been virtually no polling bias toward Democrats in Trump era elections when Trump himself wasn't on the ballot. In fact, the polls since 2016 have overestimated Republican support in midterm elections (by an average of 0.2%) and off-year, special and runoff elections (by an average of 1.3%), according to Silver . Advertisement Advertisement Either way, pollsters aren't seeing signs of this rural effect in most states — and even if they were, it might make only a minor dent in Democrats' 9-point national lead . Redistricting is the other Republican advantage. House redistricting efforts typically take place every 10 years, after each new census reveals changes in population distribution. But last summer, Trump successfully pushed GOP legislators in Texas to pass new congressional district boundaries five years earlier than normal to help Republicans win five additional House seats in 2026. This kicked off a tit-for-tat cycle that saw nine states adopt new congressional maps for the midterms — some to benefit Democrats, others Republicans. Now that the dust has settled, Cook projects that Republicans could win nine seats because of redistricting (in Alabama, Florida, Louisiana, North Carolina, Ohio, Tennessee and Texas), while Democrats could win five (in California and Utah). That would net the GOP four seats overall. It's not what Republicans initially hoped to gain from gerrymandering. "We're not gonna win all 13 of those redistricted seats. That's correct. We're not," GOP House Majority Whip Tom Emmer of Minnesota told Axios recently . But it's also not nothing. Advertisement Advertisement

Oil, Treasury yields and spot ETF outflows pushed Bitcoin lower in October as market sentiment drifted from greed back to neutral The Crypto Fear and Greed Index has fallen to 56, down from an …

Oil, Treasury yields and spot ETF outflows pushed Bitcoin lower in October as market sentiment drifted from greed back to neutral The Crypto Fear and Greed Index has fallen to 56, down from an August peak of 82, as Bitcoin absorbed hits from oil prices, bond yields and exchange-traded fund outflows in October. How Bitcoin got here As of October 12, 2026, Bitcoin was changing hands between $82,400 and $83,600. That followed a dip to a low of approximately $80,337 on October 8. The move left Bitcoin down about 3% on the week. The pullback lined up with heavy selling in US spot Bitcoin ETFs. On October 7-8, those funds saw a net withdrawal of $484.9 million. That was the largest single-day outflow since June. Advertisement BlackRock’s IBIT accounted for about $207.7 million of those redemptions. The Fear and Greed Index had been sitting near 80 in early October before sliding into the mid-50s by October 12. The news moving money, markets, and the world—before your day starts. Daily. Free. Join 34,000+ readers across crypto, finance, and policy. Email address Subscribe free We respect your privacy. Unsubscribe anytime. The macro squeeze The US 10-year Treasury yield climbed to approximately 5.3%. Brent crude, meanwhile, pushed above $100 per barrel amid geopolitical tensions in the Middle East. Still, Bitcoin has held above several key moving averages, including the 50-week average, which sits at approximately $77,200. A familiar range, and an uncomfortable anniversary For much of the past month, Bitcoin has bounced inside a range of $80,000 to $87,000. The October 8 low tested the bottom edge of that band without decisively breaking it. The pullback arrived around the one-year anniversary of the October 2025 liquidation event, when more than $19 billion in positions were wiped out. What this means for investors The most important signal here may be the ETF flows. Spot Bitcoin ETFs have become a key channel for institutional money, and a near half-billion-dollar exit in a single day shows that some of that money is getting cautious. The $80,000 floor of the recent range is the obvious line in the sand. Holding it, alongside the 50-week average, keeps the longer-term structure intact. A neutral score of 56 reflects a market that has cooled off from a greed reading of 82 in August and is waiting for a reason to commit in either direction. For now, the forces moving Bitcoin are coming from Washington’s debt market and Middle East energy flows, not from anything happening on-chain.

Vitalik Buterin, co-founder of Ethereum, has announced an ambitious strategy aimed at upgrading the Ethereum network by 2030. The plan focuses on implementing zero-knowledge proofs and enhancing throughput to improve the network’s capacity and …

Vitalik Buterin, co-founder of Ethereum, has announced an ambitious strategy aimed at upgrading the Ethereum network by 2030. The plan focuses on implementing zero-knowledge proofs and enhancing throughput to improve the network’s capacity and security. This announcement is part of a broader roadmap that includes innovations such as recursive STARKs and improved consensus mechanisms, designed to make Ethereum more scalable and privacy-focused. While these are roadmap goals rather than immediate changes, Buterin’s announcement appears to have influenced market sentiment regarding Ethereum’s future price trajectory. Advertisement Market participants are currently evaluating the potential impact of these proposed upgrades on Ethereum’s price. At the time of the announcement, Ethereum traded between $2,526 and $2,560, with a market capitalization hovering near $310 billion. The pricing in prediction markets suggests that Buterin’s strategy could enhance investor optimism about Ethereum’s ability to reach higher price targets by the end of October 2026. Key markets tracking Ethereum’s price movements show varied expectations, with some suggesting moderate increases in the likelihood of Ethereum reaching $3,300 by the end of the month. Current market sentiment appears influenced by Buterin’s outlined upgrades and their potential to attract increased network activity and investment interest. Key Takeaways Buterin’s announcement appears to suggest positive sentiment around Ethereum’s long-term scalability and security improvements. Markets are showing moderate increases in the likelihood of Ethereum achieving higher price targets, consistent with potential positive impacts from proposed upgrades. Ethereum’s current price range and market capitalization indicate a stable environment, but future developments could shift these dynamics. What to Watch Market participants will closely monitor any further announcements from Buterin or the Ethereum Foundation that could confirm timelines or progress toward these upgrades. Upcoming testnet rollouts and mainnet activation dates could serve as key indicators, potentially influencing market expectations and Ethereum’s price trajectory. Additionally, broader macroeconomic factors and regulatory developments may play a role in shaping investor sentiment and market pricing related to Ethereum.

Synopsis Finance ministers and central bankers will meet in Bangkok to discuss challenges affecting the global economy this week. The key concerns include the economic fallout from the US-Iran war and rising inflation. Policymakers …

Synopsis Finance ministers and central bankers will meet in Bangkok to discuss challenges affecting the global economy this week. The key concerns include the economic fallout from the US-Iran war and rising inflation. Policymakers face difficult choices regarding spending and support for vulnerable populations amid growing financial pressures. Investment in artificial intelligence presents new opportunities and challenges, especially for developing nations. The meetings will shape responses to these ongoing global issues.

US investors will track major bank earnings, September inflation data and Treasury yields this week for clues on corporate profitability and the Federal Reserve’s rate outlook, as persistent price pressures and rising borrowing costs …

US investors will track major bank earnings, September inflation data and Treasury yields this week for clues on corporate profitability and the Federal Reserve’s rate outlook, as persistent price pressures and rising borrowing costs test the stock market ral… US stock investors will turn their attention this week to quarterly earnings from major banks, inflation data and other economic releases for clues on corporate profitability and the outlook for inte…

Indian mutual funds are set to sell more government bonds after offloading $4.5 billion since July, four fund managers said, as the ​central bank's policy shift raised expectations of a longer, steeper rate-tightening ​cycle. …

Indian mutual funds are set to sell more government bonds after offloading $4.5 billion since July, four fund managers said, as the ​central bank's policy shift raised expectations of a longer, steeper rate-tightening ​cycle. The Reserve Bank of India raised rates for the first time in nearly four ‌years and signalled further increases as inflation rises. Mutual funds have reduced their exposure to longer-dated government bonds as rate expectations have changed, said Mansi Sajeja, a fixed-income fund manager at SBI Mutual Fund. Asset managers sold a net ₹5,100 crore ($527.08 million) of government bonds in the three sessions after the rate increase, adding to sales of ₹37,800 crore in July-September. "The policy tone sounded more cautionary on inflation generalization than growth outlook, giving it a hawkish tilt," Amit Somani, a deputy head of fixed income at Tata Asset Management said. Somani expects long-term bond yields to rise to a range of 7.20%-7.40%. The five-year bond yield topped 7.05%, rising by ‌almost 70 basis points in two months, while the 10-year bond yield was close to 7.35% levels, highest in nearly three years. "Several factors, including global rate hikes, multi-decade high global bond yields, expectations of an earlier-than-anticipated domestic rate-hike cycle, and continued uncertainty in energy markets, prompted mutual funds to reduce duration risk by trimming government bond exposure," said Vikas Garg, head of fixed income at Invesco Mutual Fund. Heavy open market sale of bonds by the RBI and risks of a repeat ​of such operations have also altered demand-supply dynamics, fund managers said. The RBI sold bonds worth ₹1 trillion last month, notching its highest net sale for any financial year in over a decade. SHIFT TO SHORT-TERM ASSETS While selling long duration government bonds, mutual funds are moving to ultra-short ​duration assets across ‌government and corporate debt. Axis Mutual Fund now prefers one-year to three-year high-quality corporate bonds, where favourable carry, ‌ample liquidity and limited issuance of banks' certificates of deposit create an appealing opportunity, the fund house said in a note. It may look to tactically add duration only when rate-hiking cycle ‌progresses ​and yields become ​more attractive. Aditya Birla Sun Life AMC likes the ultra-short term debt while Bandhan Asset Management has slashed its weighted average bond duration to less than a year, from around ‌12 years, the ​fund houses said in recent interviews with Reuters.

On Monday, Indian government bonds experienced a significant decline due to central bank measures affecting liquidity. The benchmark 6.94% 2036 bond yield rose to its highest level in three years. The Reserve Bank of …

On Monday, Indian government bonds experienced a significant decline due to central bank measures affecting liquidity. The benchmark 6.94% 2036 bond yield rose to its highest level in three years. The Reserve Bank of India announced it would sell 250 billion … Indian government bonds fell sharply on Monday as the central bank's measures to directly drain banking system liquidity, alongside foreign exchange operations that could indirectly reduce the surplu…