Business

Here are Some Types of Trading Strategies Present for Forex

Published

5 years ago

November 3, 2020

As we know, Forex, FX, or Foreign Exchange deals with international currency trading. The process involves buying one currency and giving another for speculation. There can be a rise and fall in the values of currency with factors such as geoeconomics and geopolitics in effect. The forex traders’ concern is to profit from the change in situation anyhow.

So knowing the forex strategies is helpful to employ the right one at the right time. So let’s take a look at the various styles of trading-

The scalpers

Scalpers hold onto pips for a short duration from just a few seconds to minutes. Their main aim is to grasp small amounts of pips as frequently as possible during the heat of trade during the day- which lasts for a few hours in a day for Forex traders. So they are into a large amount of trading and make many small profits on individual trades. They try to profit from each trade with at least 5 to 10 pips. So the scalping strategy engages the trader throughout the day, making it a full-time job. The prediction has to be made fast to where the market is going, and the open position must be closed within seconds.

Day trading

The day traders begin at the start of the trading day and choose their side and end the day with profit or loss. The day traders do not clutch onto trades overnight. That means they get to avoid rollover or overnight fees from brokerages like eToro (you can click here to know more about eToro fees courtesy of Wikitoro). Day trade like scalping is a short term trade, but the duration is longer, and the day traders hold onto a single trade and close it at the end of the same day. The day traders spend their time analyzing executing and monitoring a trade. They keep abreast of information and round the globe changes, economic news, and study the charts to be able to choose the right direction. They get to the results of profit or loss at the day end. There are different types of day trading-

Trend Trading [Day trading]

Trend trading is when the traders study the chart in its long period of time, and they can conclude the overall trend. They move onto a shorter time frame chart and find opportunities in that trend’s direction. The Cowabunga System is employed. This mechanical trading system filters out trades based on a four-hour chart with aid of indicators such as EMAs, Stochastics, RSI, and MACD they trade based on a 15-minute chart.

Counter Trading [Day trading]

Similar to Trend trading only after determining the overall trend the trades are searched, but in the opposite direction. The concept is to get in early, at the end of the trade just before the trend begins to reverse. A bit riskier but the payoffs are great.

Breakout Trading [Day trading]

After studying for a few hours the range that a pair makes during the day and then trades are placed on either side so that breakout can be caught in either direction. When the pair has been tight for a while- support and resistance have lasted strongly, it indicates that it will soon make a huge move. The idea is to catch the wave when the move happens. After studying the entry points are set at above and below the levels of a breakout. The preferably same amount of pips should be targeted to define a range.

Swing trading

The traders keep the trades on for several days at a stretch. They don’t monitor the charts all day long but instead study them at night to make the right decisions and are with it where the global economies are concerned. In swing trading, the swings are identified in the medium-term trend and the trader enters only at high chances of winning.

The trader buys (go long) at “swing lows,” and the opposite is true when selling (go short) at “swing highs.” They make use of the short-lasting countertrends. The trades last for more than a day, so bigger stop losses are needed to persist volatility. A money management plan is a must. During the holding time, many trades go against due to fluctuations in prices in shorter periods, swing trader keep calm and trust the analysis. The trades are larger, so spreads don’t impact the overall profits. So, in this case, trading pairs with lower liquidity and larger spreads are fine.

The position traders have trades that have been on for weeks, months, or even years. The fundamental themes that govern currency trends are employed to analyze markets and make trading decisions. Swing Traders have a good insight into economics data that affects the future of the country. The stop losses are large because of holding onto trades for long periods.

Transition trading

Transition trading is studying the market and entering the trade on a lower timeframe. If the market is favorable, the target profits should be increased or track the stop loss on a higher time frame. The advantages are that you would get immense profits. It lowers your risk as the entry is made on a lower time frame. The disadvantages are that only a few of the trades would be big winners. The understanding of multiple timeframes is a must.

Business

MetaWorx: Building Full-Stack AI Teams, Not Just Automation

Published

6 days ago

June 6, 2025

Matthew Wiles

Automation still dominates most headlines, yet the returns often fail to meet expectations. A sprawling chatbot rollout might shave a few support tickets, but it rarely shifts the profit-and-loss statement in a lasting way.

McKinsey’s 2025 workplace survey pegs AI’s long-term productivity upside at $4.4 trillion, but only one percent of enterprises say they’ve reached true “AI maturity.” MetaWorx, a Dallas, Texas-based AI employee agency founded by Rachel Kite, argues that the shortfall has nothing to do with models and everything to do with people.

“Treat AI like a point solution and you’ll get point-solution results,” shares Kite. “You need a roster that can carry the ball from raw data to governance, or the whole thing stalls at the proof-of-concept phase.”

The pod blueprint

When a plug-and-play automation script collapsed under real-world data drift, costing Kite a lucrative contract, she sketched the six-person “pod” that now anchors every MetaWorx engagement:

An infrastructure architect to tame compute costs.
A data engineer to secure and shape pipelines.
An applied scientist to prototype models against live feedback loops.
An MLOps engineer to automate rollback and retraining.
A domain product lead translates forecasts into features users actually notice.
Ethics and compliance analysts to stress test outputs for bias and keep the audit.

The team’s first sprint still delivers a quick-win bot — “small enough to calm the CFO,” jokes Kite — but the roadmap quickly pivots to reliability, explainability, and eventually optimization. By tying every algorithmic decision to a quantifiable business metric, the pods turn AI from a science project into a growth lever.

Recruiting for curiosity, not credentials

With Bain & Company predicting a global AI-skills crunch through 2027, MetaWorx has stopped chasing unicorn résumés. Instead, it hires “adjacent athletes”: a computer-vision PhD who hops from medical imaging to warehouse surveillance, or a former journalist who recasts her nose for story into prompt-engineering finesse.

“Domain expertise expires fast,” Kite says. “What doesn’t expire is the instinct to ask better questions.” The result is a lattice of overlapping skills that stays flexible when models wander into the long tail of edge-case data.

A culture of rapid experiments

Inside MetaWorx, every idea faces the same litmus test: ship something — anything — into a user’s hands within 21 days. The “three-week rule” forces prototypes into the wild early, where failure is cheap and feedback is swift. Post-mortems, including cost overruns, are circulated company-wide, erasing any stigma associated with missteps.

That laboratory mindset powers velocity. “Our first model is almost always wrong,” Kite admits, “but version 1.0 is the tuition we pay for version 2.0.” The philosophy echoes her TEDx talk on resilience: progress is iterative, not heroic.

How leaders can steal the playbook

Executives itching to replicate MetaWorx’s results don’t need a blank check. Kite offers a five-step sequence:

Inventory pain points, not tools: Walk the P&L line by line and tag the friction you can measure.

Map the stack to the problem: A recommendation engine, for instance, requires behavior data, retraining triggers, and feedback capture — automation alone won’t suffice.

Stand up a pod: Reassign existing talent into a cross-functional tiger team before hiring externally; the chemistry test is free.

Measure the story, not just the statistic: Pair model accuracy with human-scale metrics like ticket backlog or employee churn.

Budget for the boring: Reserve at least 30 percent of spend for MLOps and governance; Stanford’s HAI review links most AI failures to neglected upkeep.

Taken together, those steps shift AI from a pilot novelty to an operational habit that compounds value rather than topping out after an initial PR splash.

Character still scales faster than code

MetaWorx plans to double its headcount this year, yet Kite insists the secret isn’t a proprietary framework or a monster war chest. It’s credibility. Clients see a founder who has wrestled with the same outages and surprise bills they face. That authenticity converts skeptics faster than any algorithmic novelty.

“Tools level out,” Kite says. “Culture compounds.”

The insight lands in a marketplace still dazzled by generative fireworks. Yes, MetaWorx ships models and dashboards, but its true product is a mindset: resilience over rigidity, questions over credentials, experiments over edicts. In Kite’s world, automation is merely the appetizer. The main course is a full-stack team that knows why the model matters to the business and who owns its success after launch day.

And that, Kite argues, is how AI finally graduates from cost-cutter to growth engine, one curious pod at a time.