Actualités financières en direct

Live Events as a Reliable and Trusted News Source Addas a Reliable and Trusted News Source Add Now! (You can now subscribe to our (You can now subscribe to our ETMarkets WhatsApp channel Eurozone …

Live Events as a Reliable and Trusted News Source Addas a Reliable and Trusted News Source Add Now! (You can now subscribe to our (You can now subscribe to our ETMarkets WhatsApp channel Eurozone business activity expanded at its fastest pace in nearly two-and-a-half years in September, with strong demand helping the economy withstand rising inflation concerns linked to the war in the Middle East, a private survey showed, Reuters reported.According to Reuters, the stronger activity came even as inflation in the eurozone accelerated more than expected to 3.8% in September from 3.2% in August, driven largely by soaring energy costs. The increase has heightened expectations that the European Central Bank could keep interest rates higher for longer.The S&P Global Eurozone Services Purchasing Managers' Index rose to 53.0 in September from 51.6 in August, matching its preliminary reading and marking its highest level in 10 months.The composite PMI, which combines services and manufacturing, climbed to 53.1 from 52.0, reaching its highest level since April 2023. The reading also marked the strongest quarterly performance for the eurozone since the second quarter of 2022. A reading above 50 indicates expansion.The survey pointed to strengthening economic momentum as the bloc entered the fourth quarter. Reuters reported that IT-related services were among the strongest areas of growth, supported by increased investment in artificial intelligence as well as continued expansion in professional and commercial services.Spain remained the strongest-performing major economy in the survey, followed by Ireland. Germany's recovery also accelerated, reaching one of its strongest rates since early 2022. Italy and France, meanwhile, posted more modest growth.Services companies reported a faster increase in new business, suggesting that underlying demand remained resilient. Export orders in the services sector also expanded after 39 consecutive months of contraction, Reuters reported.Combined with the strongest increase in factory orders since early 2022, overall new orders grew at their fastest pace in 41 months. Overseas demand was particularly robust, with export orders rising at their fastest rate in more than four-and-a-half years.The improvement in activity, however, did not translate into a comparable increase in employment. A slowdown in services-sector hiring offset a modest improvement in factory job creation, resulting in weaker overall employment growth.Business confidence remained broadly stable despite the stronger activity and mounting inflation concerns.The survey also highlighted renewed price pressures across the eurozone. Both input costs and prices charged by businesses increased at their fastest pace in four months.Reuters reported that the renewed acceleration in prices could reinforce concerns that inflation may remain closer to 4% than the ECB's 2% target.The combination of stronger-than-expected economic activity, rising energy costs and accelerating price pressures is complicating the ECB's monetary policy outlook. Higher bond yields have also increased financial conditions, but have so far done little to significantly weaken business activity.Markets are currently pricing in more than two ECB interest-rate increases by the middle of next year, reflecting expectations that policymakers may need to respond more aggressively if inflationary pressures persist.(Disclaimer: This article is based on inputs from agencies. These do not represent the views of The Economic Times)

Yet another week of wavering vibes was capped off last week by action as a surprising jobs report reset expectations for the October Fed meeting. Even though the mere 29,000 jobs added in September …

Yet another week of wavering vibes was capped off last week by action as a surprising jobs report reset expectations for the October Fed meeting. Even though the mere 29,000 jobs added in September made for a "miss," the market celebrated what that meant: The… Yet another week of wavering vibes was capped off last week by action as a surprising jobs report reset expectations for the October Fed meeting.Even though the mere 29,000 jobs added in September ma…

SBI FD interest rate: Investors frequently look to calculate the maturity amounts for their fixed deposit investments with the State Bank of India. Factors such as tenure and current interest rates play significant roles …

SBI FD interest rate: Investors frequently look to calculate the maturity amounts for their fixed deposit investments with the State Bank of India. Factors such as tenure and current interest rates play significant roles in these calculations. Many investors invest in State Bank of India (SBI) fixed deposit (FD) schemes. But before or at the time of investing, many of them also want to know the maturity amount they may get in an SBI FD. Th…

The Reserve Bank of India is set to maintain the repo rate at 5.25% during the upcoming policy meeting. Despite rising inflation pressures and global economic uncertainty, the rate is expected to remain unchanged. …

The Reserve Bank of India is set to maintain the repo rate at 5.25% during the upcoming policy meeting. Despite rising inflation pressures and global economic uncertainty, the rate is expected to remain unchanged. Credit and deposit growth in India currently … New Delhi [India]: The Reserve Bank of India is expected to keep the repo rate unchanged at its October policy meeting despite rising inflation risks, before beginning a rate-hike cycle in December t…

The Reserve Bank's Monetary Policy Committee began its three-day meeting on Monday and is likely to raise rates by 25 basis points, aligning with the central banks' hawkish stance amid escalating conflicts in West …

The Reserve Bank's Monetary Policy Committee began its three-day meeting on Monday and is likely to raise rates by 25 basis points, aligning with the central banks' hawkish stance amid escalating conflicts in West Asia that pose risks for domestic inflation. An interest rate hike by the RBI in its upcoming monetary policy would mark a stance reversal, following rate cuts in 2025 and a prolonged pause thereafter, according to a PTI poll of 16 economists and bankers. "Insights, assessments, and the way forward-set to unfold soon," the Reserve Bank said in a social media post while announcing that the monetary policy statement will be announced on October 7 at 10 am. The last repo rate hike was in February 2023, when the RBI raised the rate by 0.25 per cent to 6.50 per cent. It kept the rate unchanged through 2023-24 before beginning its rate-cut cycle in 2025. Currently, the RBI's policy repo rate stands at 5.25 per cent. A majority of participants in the PTI poll expect a rate hike with a hawkish tone at the upcoming policy review on Wednesday. The opinion seems divided on whether there will be a shift in stance. Meanwhile, Madan Sabnavis, Chief Economist, Bank of Baroda, was of the view that the Reserve Bank will continue with the status quo on the short-term lending rate (repo) on Wednesday. "While we do believe that the next interest rate cycle will be of 50-75 bps in the upward direction, we think there will be a pause this time in October," he said. Sabnavis further said that a pause can help to moderate bond yields too, as the market is expecting one now. "Waiting till December will be prudent when we actually know how the kharif crop has fared and the CPI inflation rate for September and October," he said, adding that the RBI could marginally raise its forecasts of GDP and inflation. Goldman Sachs report said the August MPC minutes were materially more hawkish than the policy statement. Members acknowledged that food and fuel-driven inflation could generate second-round effects and indicated that broader, and more persistent price pressures would warrant policy action. "We therefore bring forward our RBI forecast to 25 bps repo rate hikes in October and December 2026. The MPC may also shift its stance from 'neutral' to 'calibrated tightening' or 'withdrawal of accommodation'," it said. Dipti Deshpande, principal economist at Crisil, said that since the last policy, inflationary pressures have mounted further mainly due to the re-escalation of the West Asia conflict and the pressure on energy and commodity prices. If these pressures persist, further rate hikes are expected. Sunil Pareek, Executive Director, Assetz said that from a residential real estate perspective, stability in interest rates would certainly be supportive of homebuyer sentiment, particularly in view of the festive season. "A calibrated monetary policy that keeps inflation anchored without significantly increasing the cost of home ownership would be the most constructive outcome for the residential sector," Pareek added. Vineet Nahata, Director of Power Gilt Treasuries, said that given the recent rise in bond yields globally, a 50-basis-point hike by the MPC would not come as a surprise. "If not 50 basis points, MPC would definitely pitch for 25 basis points," he said. Shrikant Goyal, Managing Director, Getfive Funds, said that with the festive season approaching, MSMEs are preparing for a pickup in orders, and timely working capital will be key to making the most of it. "We expect the MPC to hold the repo rate and maintain a neutral stance, which will give borrowers and lenders the predictability they need to plan ahead," he said. India's retail inflation accelerated to an eight-month high of 4.82 per cent in August from 4.45 per cent in July. The RBI has been mandated by the government to ensure CPI inflation remains at 4 per cent with a margin of 2 per cent on either side.

Good morning. On Fortune’s radar today: OpenAI’s Altman: Don’t worry, the risks will be worth it. Plague escapes Russian lab; quarantine imposed in eastern Siberia. The human population is about to start shrinking. Markets: …

Good morning. On Fortune’s radar today: OpenAI’s Altman: Don’t worry, the risks will be worth it. Plague escapes Russian lab; quarantine imposed in eastern Siberia. The human population is about to start shrinking. Markets: Broadly up. The Fed is hurting the … Good morning. On Fortunes radar today:OpenAIs Altman: Dont worry, the risks will be worth it.Plague escapes Russian lab; quarantine imposed in eastern Siberia.The human population is about to start s…

Vizag software professional lost ₹20 lakh in WhatsApp crypto trading scam on Netobit.com; fraudsters demanded 15% “processing fee” to withdraw funds. Follow Us On Social Media Copyright © 2026 Bennett, Coleman & Co. Ltd. …

Vizag software professional lost ₹20 lakh in WhatsApp crypto trading scam on Netobit.com; fraudsters demanded 15% “processing fee” to withdraw funds. Follow Us On Social Media Copyright © 2026 Bennett, Coleman & Co. Ltd. All rights reserved. For reprint rights: Times Syndication Service

The memecoin market has been waking up again. Dogecoin, Pepe, Bonk, and dogwifhat have all posted monthly gains, while smaller names are producing violent breakouts that attract speculative capital back into the sector. Read …

The memecoin market has been waking up again. Dogecoin, Pepe, Bonk, and dogwifhat have all posted monthly gains, while smaller names are producing violent breakouts that attract speculative capital back into the sector. Read More: He Says His Binary Options Strategy Brings In $2,000 a Week—Watch Him Trade Live That makes today a good time to reassess the best memecoins October 2026 has to offer. Best Memecoins October 2026: Quick Comparison Token Approx. Market Cap Why It Stands Out Dogecoin ( DOGE ▼ $0.0842 ) $15B DogeOS could finally add an application layer Shiba Inu ( SHIB ▼ $0.00000524 ) $3.4B Shibarium gives SHIB a broader ecosystem Pepe ( PEPE ▼ $0.00000344 ) $1.9B Deep liquidity and strong speculative recognition Pudgy Penguins (PENGU) $600M One of crypto’s strongest consumer brands SPX6900 (SPX) $410M Extremely persistent community narrative Bonk (BONK) $330M Deep integration across Solana dogwifhat (WIF) $250M One of Solana’s most recognizable pure memecoins Useless Coin (USELESS) $250M Strong recent momentum Fartcoin (FARTCOIN) $180M Durable liquidity despite the joke premise Floki (FLOKI) $155M Valhalla and a larger utility ecosystem 1. Dogecoin — The Safest Memecoin Bet Dogecoin remains the obvious starting point for any best memecoins October 2026 list. Its advantage is scale. DOGE has by far the largest memecoin market cap and routinely trades hundreds of millions of dollars per day. That makes it less explosive than tiny tokens, but also much easier to enter and exit. The more interesting development is DogeOS. Related: Who Wins the AI-Crypto Race? 5 Tokens Building the Infrastructure for Autonomous Agents The project is building an EVM-compatible application layer around Dogecoin. Its Chikyū testnet is already live: developers can deploy smart contracts, and DOGE is used as the network currency. The first signal is live. 🫡 Crews are mobilizing 🔍 Intel is being passed 🐿 Ignore the squirrels 📣 Spread the wordhttps://t.co/g1q0plX3IY pic.twitter.com/VMMH3jrOFs — DogeOS (@DogeOS) May 8, 2026 For years, Dogecoin’s biggest weakness was that there was very little to do with it beyond sending, holding, and trading DOGE. DogeOS is attempting to change that. If DogeOS progresses from its current testnet into an active mainnet ecosystem, Dogecoin gets a catalyst that has little to do with Elon Musk posts or another round of meme speculation 2. Shiba Inu — The Ecosystem Bet Shiba Inu has followed a different path. SHIB began as a Dogecoin challenger, yet the project has since spent years building infrastructure around the token. The centerpiece is Shibarium, an Ethereum Layer 2 for lower-cost and faster transactions. That does not automatically create demand for SHIB. Actually, Shibarium uses BONE for gas, and the wider Shiba ecosystem includes several tokens. Still, SHIB has something that most memecoins lack: Strong infrastructure layer beneath Major-exchange liquidity Large holder base Enough brand recognition to survive multiple market cycles Its roughly $3.4 billion market cap also leaves more room for percentage gains than DOGE. Among the best memecoins October 2026, SHIB is more of a bet on the broader Shiba ecosystem. 3. Pepe — The Liquidity Play Pepe does not need a complex utility story. Its strength is that traders already know what it is. PEPE remains one of the largest memecoins, with a market cap around $1.9 billion and daily volume frequently running into hundreds of millions of dollars. It also gained nearly 30% over the past month before the start of October. That matters during speculative rallies. When capital rotates into memecoins, traders often gravitate toward assets with enough liquidity to absorb large positions but enough volatility to outperform Bitcoin and Ethereum. PEPE fits that profile almost perfectly. There is no protocol revenue or elaborate roadmap supporting the valuation. The investment case is the durability of the Pepe meme itself. 4. Pudgy Penguins — The Brand Bet PENGU is unusual because the token sits behind something people recognize outside the token itself. Related: Best Altcoins to Buy in October 2026 Before the Next Crypto Rally Pudgy Penguins has built one of crypto’s strongest consumer-facing brands through NFTs, toys, merchandise, games, licensing, and physical retail distribution. Pengu Card is live 🎉 🐧 Join the Huddle 💳 Spend at 150M+ merchants 🌎 Proliferate Pengu wherever you go Order your card nowpic.twitter.com/glpSkbfcvk — KAST (@KASTxyz) March 24, 2026 PENGU gives that community a liquid token. It is now one of the largest Solana memecoins, with a market cap around $600 million and unusually robust daily trading volume of over $200 million. That makes PENGU interesting for October. When speculative appetite rises, it can trade like a memecoin. Meanwhile, the underlying Pudgy Penguins brand keeps engaging the community through the market cool-off periods. For investors screening the best memecoins October 2026, PENGU offers a rare bet on the expanding IP ecosystem. 5. SPX6900 — The Cult-Community Bet SPX6900 is what happens when a memecoin turns absurdity into its entire investment thesis. The project openly says SPX has no intrinsic value, no formal team, and no conventional roadmap. Its central joke is that SPX6900 should somehow “flip” the S&P 500. Do not let your dopamine-fried brain confuse you about the current “boring sideways” period in the Crypto markets. Crypto is going significantly higher soon. BTC▲$77,666.00 to $222k SPX6900 to $200+ — Murad 💹🧲 (@MustStopMurad) March 26, 2026 Yet the community has proved surprisingly durable. SPX currently carries a market cap above $400 million despite offering very little traditional utility. That is precisely why it belongs on this list: successful memecoins are often social assets first and products second. SPX is risky even by memecoin standards, but it has retained the one thing that failed meme projects lose quickly — attention. Read more: Best Crypto to Invest in: Bitcoin, Ethereum or XRP? What the Latest ETF Flows Reveal 6. Bonk — The Solana Ecosystem Bet BONK is no longer just the dog token that helped revive Solana sentiment after FTX. It has spread across DeFi applications, wallets, trading products, gaming, NFTs, and other parts of the Solana ecosystem. The project says BONK is now held by more than one million on-chain wallets. That distribution matters. A memecoin connected to many applications has more ways to remain visible than one whose only activity happens on centralized exchanges. BONK has also gained roughly 25%–30% over the past month while its daily trading volume approaches $100 million. Among the best memecoins October 2026, BONK remains a clean way to bet simultaneously on Solana ecosystem and another memecoin cycle. However, a July governance attack that drained roughly $20 million from the BonkDAO treasury remains a significant risk to note. BonkDAO was the target of a malicious governance proposal resulting in an estimated $20M worth of BONK tokens being drained from the BonkDAO treasury. During the investigation, BonkDAO identified the exchange wallets used to purchase BONK ahead of the proposal. BonkDAO is… — BONK!!! (@bonk_inu) July 6, 2026 7. dogwifhat — The Pure Solana Meme WIF is the opposite of projects trying to manufacture utility around a joke. It is a dog wearing a hat. That simplicity helped dogwifhat become one of the defining tokens of Solana’s previous memecoin cycle, and it still trades with considerably more liquidity than most tokens its size. WIF entered October with a market cap around $250 million after gaining roughly 30% over the previous month. The bull case is straightforward: if Solana becomes the center of another speculative memecoin wave, traders already know WIF. The bear case is equally straightforward. There is almost nothing underneath that recognition if attention moves somewhere else. 8. Useless Coin — The Momentum Bet The name tells you roughly how seriously Useless Coin takes itself. But its recent price action is harder to dismiss. USELESS has climbed more than 135% over the past month and reached a market cap around $250 million. Daily trading volume has also moved into the tens of millions. That gives it one thing traders consistently chase during memecoin rallies: momentum. The problem is that momentum works both ways. A token that doubles in a month can continue running if attention compounds. It can also fall much harder once early buyers start taking profits. USELESS therefore belongs among the best memecoins October 2026 only for investors comfortable with extreme volatility. Read more: Best Crypto to Buy Before the Next Crash: Top 5 Coins to Watch Through the End of 2026 9. Fartcoin — The Survivor Fartcoin looked designed to disappear: a project built around a deliberately stupid joke. But instead, it survived. The Solana token still has a market cap around $175 million and regularly posts over $30 million in daily trading volume. Survival itself matters in memecoins. Thousands of tokens launch during every speculative wave. Most lose their liquidity and disappear from market attention. FARTCOIN has retained enough recognition and trading activity to remain relevant long after its initial viral phase. There is no fundamental valuation model here. The case is entirely based on brand recognition, and the possibility that familiar names outperform when speculation returns. 10. Floki — The Utility-Heavy Memecoin FLOKI has gone further than most memecoins in trying to build products around its token. Its ecosystem now includes: Valhalla, a blockchain game TokenFi, a tokenization platform Staking Educational products Trading bot Valhalla is particularly important because FLOKI is used inside the game’s economy rather than existing only as a speculative ticker. The token remains much smaller than DOGE, SHIB, or PEPE, with a market cap around $150 million. That gives it more upside if attention returns, but also considerably higher risk. For investors looking beyond pure memes, FLOKI is a distinctive name on the best memecoins October 2026 shortlist. Which Memecoin Has the Most Upside? Potential upside generally increases as market cap falls — but so does the chance of losing most of the investment. DOGE would require billions of dollars in new value to double. USELESS, FARTCOIN, or FLOKI require far less capital. That does not make the smaller tokens better investments. A useful way to divide the list is by risk: More established: DOGE, SHIB, PEPE DOGE, SHIB, PEPE Mid-cap momentum: PENGU, SPX, BONK, WIF PENGU, SPX, BONK, WIF Higher-risk: USELESS, FARTCOIN, FLOKI The most interesting setup may depend on what drives the next rally. DogeOS favors DOGE. Another Solana speculative wave favors BONK and WIF. Consumer-brand expansion helps PENGU. A pure risk-on frenzy could favor the smaller names. What Could Trigger the Next Memecoin Rally? Memecoins usually benefit when Bitcoin is stable or rising, liquidity spreads into altcoins, and traders become willing to move further out on the risk curve. There are also token-specific catalysts. DogeOS gives DOGE a new infrastructure story. Pudgy Penguins continues building its consumer brand. BONK is deeply embedded in Solana. FLOKI has actual products. Smaller tokens such as USELESS can move simply because momentum attracts more momentum. The broader memecoin sector currently has a market capitalization around $35 billion. That is substantial, but far below levels seen during stronger speculative periods. If capital rotates back into the sector, the best memecoins October 2026 candidates are well-positioned to capture that flow. Final Verdict: What Are the Best Memecoins to Buy in October 2026? There is no single memecoin that combines maximum upside with low risk. DOGE remains the strongest large-cap choice, particularly with DogeOS giving the token a new technical catalyst. PEPE offers one of the cleanest liquidity-driven trades. PENGU combines memecoin speculation with a genuine consumer brand, while BONK and WIF remain obvious bets on another Solana meme cycle. Further down the market-cap ladder, SPX, USELESS, FARTCOIN, and FLOKI offer larger potential percentage moves but much less room for error. That is the real tradeoff behind the best memecoins October 2026 list. The more explosive the upside looks, the less capital it usually takes to send the token in the opposite direction.

For nearly five years, Cathie Wood of Ark Invest has been banging the table for investors to ramp up their exposure to Bitcoin (CRYPTO: BTC). She was the first high-profile investor to put out …

For nearly five years, Cathie Wood of Ark Invest has been banging the table for investors to ramp up their exposure to Bitcoin (CRYPTO: BTC). She was the first high-profile investor to put out a $1 million price target for Bitcoin back in 2022, and she has co… For nearly five years, Cathie Wood of Ark Invest has been banging the table for investors to ramp up their exposure to Bitcoin (CRYPTO: BTC). She was the first high-profile investor to put out a $1 m…

marketdx The financial impact graph, in Python. News → who it touches and why — the causal channel, the story's lean, and the ripple — across stocks, commodities, FX, crypto, and private companies. A …

marketdx The financial impact graph, in Python. News → who it touches and why — the causal channel, the story's lean, and the ripple — across stocks, commodities, FX, crypto, and private companies. A research, screening & feature layer — direction is the news's content lean, not a price forecast. ▶ Live playground · Docs & pricing · Sample dataset pip install marketdx # add [pandas] for .to_df(): pip install "marketdx[pandas]" An API key is required. Create a free one at https://marketdx.app (sign in → API keys), then pass it to the client (keep it out of source control — read it from an env var / secret in real apps): from marketdx import MarketDX mdx = MarketDX ( api_key = "avn_live_…" ) # your key from https://marketdx.app for s in mdx . news ( megatrend = "ai-power" , impact = "indirect" ): print ( s . title , [( e . name , e . impact . net_direction ) for e in s . entities ]) That's the whole graph: every news event, every affected entity, labeled with direction, relevance, the causal aspect (the why), and whether it's the epicenter or a ripple — across five asset classes, including private companies ticker feeds can't see. No key yet? Explore everything with zero signup in the playground, then grab a free key at https://marketdx.app. Every request is authenticated with your key ( Authorization: Bearer <key> ) and metered in credits. Why the SDK (not just requests ) Typed graph — signal.entities[0].impact.aspects[0].direction with autocomplete, not raw dicts. — with autocomplete, not raw dicts. Auto-pagination — for s in mdx.news(...) pages for you. limit= caps how many you get (default 50; limit=None walks the whole match set); page.total is the full count. — pages for you. caps how many you get (default 50; walks the whole match set); is the full count. Names, not ids — megatrend="ai-power" or "AI Power & Cooling" or 10040000 all work. — or or all work. .to_df() — the whole result as a pandas DataFrame, one row per (event × entity × aspect). — the whole result as a pandas DataFrame, one row per (event × entity × aspect). Typed errors — AuthError , QuotaError , RateLimitError , BadRequestError , NotFoundError . The graph, a few ways # 1. Ripple: a themed event that also touches NON-thematic entities (our differentiator) for s in mdx . news ( megatrend = "semiconductors" , impact = "indirect" , limit = 50 ): ... # 2. Beyond tickers: private companies in a trend (OpenAI, Anthropic, Ampere, ChangXin…) for c in mdx . megatrends ( "semiconductors" ) . off_coverage (): print ( c . name , c . type , "→" , c . megatrend [ "node_name" ]) # 2b. …or just the NEWS that moves private companies — one server-side filter on the feed for s in mdx . news ( entity_type = "private" , only_scored = True ): # also: crypto / commodity / forex / stock print ( s . title , [ e . name for e in s . entities if e . type == "private" ]) # 3. Per-stock impact timeline + its news-derived rivals tl = mdx . stock ( "NVDA.US" ) . news ( aspect = "competition" ) peers = mdx . stock ( "NVDA.US" ) . competitors () # 3b. Find ANY entity — stock, commodity, crypto, forex, or an off-coverage PRIVATE company — # each hit carries a ready-to-use .ticker (+ .type) you feed straight back into by-tickers gold = mdx . stocks ( q = "gold" ) . first () . ticker # -> "GOLD" (the commodity) openai = mdx . stocks ( q = "openai" ) . first () . ticker # -> "oc:52" (private, off-coverage) news = mdx . news_by_tickers ([ "NVDA.US" , openai ]) # NVIDIA + OpenAI news in one feed # 4. News-driven screen — where the news leans positive on a theme (the model's read, for research) positive_lean = mdx . stocks ( megatrend = "ai-power" , direction = "pos" , country = "US" , order_by = "news_count" ) # 5. Semantic search — match news by MEANING, not keywords (each hit is explainable) for s in mdx . news_search ( "chip export controls to China" , limit = 5 ): print ( round ( s . similarity , 2 ), s . scored , s . title ) # how-strongly-matched + does-it-move-an-entity # 6. Brief in ONE call — pulse timeseries + top stories + winners/losers + heatmap + assets. # Scope by theme, market, news-type, impact-channel, GICS sector, or any mix (>=1 scope). brief = mdx . theme ( "ai-power" ) . summary ( window = "qtd" ) # theme brief (== brief(megatrend="ai-power")) brief = mdx . brief ( news_type = "commodity_supply" , country = "JP" , window = "90d" ) # "commodity news, in Japan" brief = mdx . brief ( gics = "2550" , country = [ "GB" , "DE" , "FR" , "IT" , "ES" , "NL" ]) # "European retail pulse" (sector x region) # Find a GICS code by name — mdx.gics() returns the whole bounded taxonomy (filter client-side): hits = [( g . code , g . name ) for g in mdx . gics () if "retail" in g . name . lower ()] # -> ('2550','Consumer Discretionary Distribution & Retail'), … print ( brief [ "pulse" ][ "story_count" ], brief [ "pulse" ][ "net_direction" ]) print ([ w [ "ticker" ] for w in brief [ "winners" ]], "vs" , [ l [ "ticker" ] for l in brief [ "losers" ]]) print ([ a [ "name" ] for a in brief [ "top_assets" ]]) # commodity / forex / crypto the theme moves Straight to pandas .to_df() returns the same columns as the sample dataset ( impact-signals.csv ) — so anything you prototyped on the free CSV runs unchanged on the live graph: df = mdx . news ( megatrend = "ai-power" , impact = "indirect" ) . to_df () # published_at · title · brief_text · entity_name · entity_ticker · entity_type · direction · # aspect · reason · relevance · impact · impact_score · node_name · entity_country · publisher · url df . groupby ([ "entity_type" , "direction" ]) . size () # who the news lands on, +/− by asset class (the model's read) df [ df . aspect == "tariff" ] . entity_name . value_counts () # who the tariff channel touches Filtering — read this before you filter Feed filters are EVENT-level, not row-level. news(direction=…, aspect=…, news_type=…, country=…) selects articles that contain at least one matching impact and returns the whole article with all its entities and aspects. So direction="pos" can return an article that also moves something neg , and aspect="supply" can return one whose other entities are hit via monetary . For exact per-row filtering, post-filter the entities: for s in mdx . news ( news_type = "commodity_supply" , direction = "pos" ): for e in s . entities : for a in ( e . impact . aspects if e . impact else []): if e . type == "commodity" and a . direction == "pos" and a . aspect == "supply" : ... # exact row you asked for Two different "direct" axes (don't conflate): Signal.impact_type ( direct | indirect ) = the article's relation to the queried node — epicenter ( direct ) vs ripple ( indirect ). Set by news(megatrend=…, impact="indirect") . ( | ) = the relation to the — epicenter ( ) vs ripple ( ). Set by . Entity.direct ( True | False ) = whether that entity is factually mentioned in the article ( True ) vs impact-only / not named ( False ). Narrow the feed by entity — server-side. news() (and news_search() ) filter by entity_type / only_scored / min_relevance in the API, so page.total stays the exact filtered count (no wasted paging). These keep the whole article — for an exact per-row cut, still post-filter the entities as above. mdx . news ( entity_type = "commodity" ) # feed → only stories that move a commodity mdx . news ( entity_type = "private" ) # only stories moving a private co (OpenAI, SpaceX) mdx . news ( only_scored = True ) # drop mention-only articles (keep judged impact) mdx . news ( min_relevance = 0.8 ) # only a strongly-relevant scored entity mdx . news ( megatrend = "ai-power" , entity_type = "crypto" ) # entity filters compose with megatrend scope mdx . news_search ( "oil supply shock" , entity_type = "commodity" ) # search supports entity_type too mdx . news_by_tickers ( "NVDA.US" ) # a ticker's news (direct + indirect by default) mdx . news_by_tickers ( "NVDA.US" , link = "direct" ) # only where NVDA is factually named (not ripple-only) mdx . news_search ( "oil shock" , megatrend = "ai-power" ) # semantic search, scoped to a trend mdx . megatrends ( "ai-power" ) . off_coverage () # private / off-coverage roster Entity filters do not apply to impact="indirect" (the ripple feed) — the API returns 400 if you combine them. On news_by_tickers , the ticker set already scopes the entities. Only entities with a scored impact (many are mentioned-only) — only_scored=True narrows to such articles server-side; then read the scored entities off each signal: scored = [ e for s in mdx . news ( megatrend = "ai-power" , only_scored = True ) for e in s . entities if e . impact and e . impact . aspects ] What to expect in entities (not bugs): Mostly mention-only. An article's entities mix two kinds: mentioned ( e.direct is True , e.impact is None ) — every ticker the article names — and scored ( e.impact is not None ) — the ones the model judged materially moved. Mentions usually outnumber scored (a story names many tickers but moves a few). Want just the movers? only_scored=True or filter e.impact . An article's mix two kinds: mentioned ( , ) — every ticker the article names — and scored ( ) — the ones the model judged materially moved. Mentions usually scored (a story names many tickers but moves a few). Want just the movers? or filter . entities can be empty. include=entities attaches entities if the article maps to any; a macro / policy / commodity story that names no covered company (e.g. "China bans helium exports") legitimately returns entities == [] . It means no entity resolved, not a dropped/failed enrich. Provenance. A scored impact ships its evidence, not just a label: aspect + direction + relevance (the judgment), reason (why), Entity.direct (named vs affected-only), and impact.label_version — the labeling-scheme version (currently "1.0" ), a per-label stamp that bumps when the model/prompt/taxonomy changes so you can detect and re-evaluate shifts. Audit or gate on it: e.impact.label_version . stock(t).news() is a stock-centric timeline — a StockNews (the stock's own impact / trend / relevance ), not an entity graph (no entities[] ). For the full graph of an article, use news() . Story-collapse (on by default). The same story is often republished / rewritten across outlets. Every news feed — news() , news_search() , news_by_tickers() and stock(t).news() — merges those near-duplicates into a single signal by default (cosine-similarity grouping, server-side) so a feed reads one-story-one-row. Pass collapse=False when you want the raw, un-deduped stream — e.g. to measure coverage volume: merged = mdx . news ( megatrend = "ai-power" ) . to_list () # deduped (default) raw = mdx . news ( megatrend = "ai-power" , collapse = False ) . to_list () # every republication Each collapsed signal also carries its cluster metadata — group a dashboard by story_id and see reach + lifespan without double-counting: for s in mdx . news ( megatrend = "ai-power" , limit = 5 ): print ( s . story_id , s . dup_count , s . first_seen , s . latest_seen ) # cluster id · outlets · broke · last echo The brief — the whole picture in one call mdx.theme(id).summary(...) (a theme = a megatrend node; also mdx.megatrends(id).summary(...) ) returns a pre-composed analyst brief so you don't stitch 5+ requests together. It's a fixed composite dict , not a paginated list: brief = mdx . theme ( "ai-power" ) . summary ( window = "30d" ) # 7d/30d/90d/180d/1y or mtd/qtd/ytd (or from_/to) brief [ "pulse" ] # story_count, net_direction, pos/neg share + a `series` (volume+sentiment/bucket) brief [ "top_stories" ] # epicenter, deduped; market-wraps & no-member-named stories deprioritized brief [ "ripple" ] # indirect (ripple-in) stories, each with `via` brief [ "winners" ], brief [ "losers" ] # member stocks by net direction brief [ "aspect_heatmap" ] # which channels the theme is playing out through brief [ "top_entities" ] # operating companies most in the news brief [ "top_assets" ] # commodity / forex / crypto the theme moves (split out from companies) Every count is story-deduped (20 outlets on one story = 1). The pulse.series is the momentum signal — there's no single momentum scalar (the latest bucket is the current, partial period). Cost: 15 credits. Any scope, not just a theme. mdx.brief(...) composes the same object over any AND-combination of megatrend / news_type / country / aspect / gics (≥1 required) — a theme, a market, a news category, an impact channel, a GICS sector, or a mix: mdx . brief ( country = "JP" ) # how is Japan doing right now? mdx . brief ( news_type = "commodity_supply" , country = "JP" ) # commodity news, in Japan mdx . brief ( aspect = "tariff" , window = "90d" ) # everything moving via tariffs mdx . brief ( megatrend = "semiconductors" , country = "US" ) # a theme, narrowed to one market applied_scope echoes what you filtered. node + ripple appear only when a single megatrend anchors the brief (without a theme there's no ripple). Under a megatrend scope winners / losers are the theme's members; otherwise they're the top +/- companies in the scope. theme(id).summary(...) == brief(megatrend=id, ...) . megatrend / news_type / country / aspect / gics each take one value or a list. Metering & errors Every call carries X-Credits-Charged / X-RateLimit-* ; check your balance any time (free, unmetered): mdx . account () # {'plan': …, 'credits': {'balance', 'daily_quota', 'resets_at', 'unlimited'}, 'rate_limit': …} from marketdx.errors import QuotaError , RateLimitError try : signals = mdx . news ( megatrend = "ai-power" ) . to_list () except RateLimitError as e : time . sleep ( e . retry_after or 1 ) except QuotaError : ... # daily quota spent — resets 00:00 UTC Enum values ( aspect , direction , entity_type , …) are type hints for your editor — the API is the source of truth, so new values work without upgrading the SDK. The live list: mdx.enums() . Reference news · news_search · news_types · megatrends ( .stocks / .off_coverage ) · gics ( .stocks ) · stocks (search + screener) · stock ( .news / .competitors / .peers ) · enums · account . Full API docs: marketdx.app. License MIT. Built by MarketDX — democratizing financial data.