Stock futures: The Dow, S&P 500 and Nasdaq sold off Monday, led by Apple, FANG stocks and many other big techs. But two investor fear gauges remained calm. Time to be afraid?
The Nasdaq got smacked with its biggest single-session loss in more than three weeks and nearly faced a test of the psychologically important 7000 level.
The Dow today plummeted more than 400 points in another beating for stocks and the technology sector in particular.
In stock market news Monday, software stocks and the FAANG stocks faced another round of selling pressure ahead of the Thanksgiving holiday.
China cranked up the trade war with actions that market watchers weren't taking at face value. Meanwhile, Dow Jones stocks wiped out Friday's 0.5% gain.
The stock market was squarely lower after Apple slashed its iPhone production. Tesla stock is trying to break out past a buy point.
Check the Stock Market Today column to spot changes in market trend and track the best stocks to buy and watch.
Track current U.S. stock market news, and get analysis of the latest trends on the S&P 500, Nasdaq composite and Dow Jones industrial average. Plus, get timely alerts to the best stocks to buy and watch.
Stocks slipped at Monday's open as rising Apple concerns hurts techs and the Dow Jones industrials fought to hold support.
Stock futures: Choppy recoveries often follow sharp stock market corrections. That happened after the early 2018 market correction and perhaps again now. Here's what investors should do.
The key market indexes ended mixed after sharply paring early losses in the stock market today. The Nasdaq gave up small gains to finish in the red.
- Released QStudio Q154 today with a new Model Result Viewer - Periodic Returns. View your Model's returns by Day, Week, Month, Quarter, or Year, and also see the Average Return by Day of Week or Month of Year.
- Q154 also adds a new Price Component: AveragePriceHLCC.
- Finally, Q154 adds static Series methods to all the standard indicators, as an alternative to using the new keyword to create indicators in code. This can lead to more natural coding for some indicator implementations.
- We now show the post count for Models in the Model tiles.
- We're beginning a phase of beefing up Quantacula Studio's live trading capability, the first fruits of this effort is the new Quotes & Price Triggers tool released in Q153.
- Discussion postings made in Model pages were getting lost, they're now visible in the main Forum listings.
- Added Monthly Activity table to the Forum.
Based on an Index study of a German Trading Portal, I have measured the monthly profit/loss, back to 1978. According to my findings, Mar-Apr and Oct-Dec are the strongest months on several Indexes (which doesn't surprise). The purpose of this system is to reduce the risk so that you are only in the market in those time-frames when the market historically shows an absolute out-performance.
Perhaps a silly question but how (and on which place) should i place a 15% stop-loss for a short position in code?
public override void Execute(BarHistory bars, int idx)
{
int index = idx;
Position shortPosition = FindOpenPosition(0);
if (shortPosition == null)
{
if (smaFast.CrossesUnder(smaSlow, index))
{
PlaceTrade(bars, TransactionType.Short, OrderType.Market, 0, 0);
ClosePosition(shortPosition, OrderType.Stop, shortPosition.EntryPrice * 1.15);
}
}
if (shortPosition != null)
{
if (smaFast.CrossesOver(smaSlow, index))
{
PlaceTrade(bars, TransactionType.Cover, OrderType.Market, 0, 0);
}
}
}
A rotation model that invests in 3 the stocks in the Nasdaq 100 with the lowest PFE (Polarized Fractal Efficiency) weighting, rebalanced daily.
Just having trouble with the Williams Vix Fix calculation in QS. The Fear Histogram in QS goes into the wrong direction (see below). Any ideas? QS
How about another killing feature? I have a system which uses the fear and weakness of the other market participants (it’s also used by an institutional swiss investor). In general, I buy my assets into the weakness of the market, which works like this:
- If an instrument is the first time oversold, I buy the first 10% of my final investment.
- If the price goes further down, then I increase my position by another 20%.
- If the price falls again, I increase by another 30%.
- If there is actually a fourth discount, then I throw the remaining 40% into the market
Sounds cruel, but works great. So my question is: By today I’m handling this scaling-in fully in Ninja code, since Ninja doesn’t has a Position sizing feature. But QS has one. What about extending this with an option to scale-in a position over max. 4 steps? One condition is absolutely important: It should be possible drive the scaling-in feature within code.
There was an article in TASC 2017-08 called "System Development Using Artificial Intelligence". I have tested the Strategy in Ninja but I only scratched the surface. The whole thing however looks very profitable, So it might be worthwhile to test and publish this algorithm in QS.
Just got an interesting statistic from a German trading portal regarding the Dow Jones, back to 1896. They have measure the profit/loss by month. I made a quick back-test to check whether this is true or not. According to my findings, Jan/Apr/Jul/Dec are in deed the strongest months on the Dow Jones Index. That's what i could reproduce with my quick test back to 1978.
Stolen from Larry Connors Book "Buy The Fear, Sell The Greed – 7 Behavioral Quant Strategies for Traders". Here's a short description:
With VXX the goal is to get short at the proper time, climb aboard as it declines, and stay aboard as long as possible. We also want to exit early if there’s a change in trend because if there is we can safely be in cash. How do we do this? It’s fairly simple: with moving average crossovers. A moving average crossover simply takes two different period moving averages. When the shorter period crosses below the longer period moving average, you go short VXX. When it crosses above its longer-term moving average you exit and go into cash.
The rules are simple for the VXX Trend Strategy and the test results are solid, especially for a trend-following methodology.
- When the 10-period simple moving average (SMA) (we also tested an exponential moving average, EMA) crosses under its 30-period SMA, short VXX. (If you use the exponential moving average, short VXX when its 10-period EMA crosses under its 30-period EMA.)
- Stay short until the 10-period SMA (EMA) crosses above its 30-period SMA (EMA).
I have extend the System with a 15% Stop-Loss. Instead of shortening VXX, you can also go Long on SVXY or ZIV. I prefer VXX because the ETF has enough liquidity.
PriceComponents represent the available elements in a set of historical data, including open, high, low, close, and volume. They appear as indicators in the Building Block model builder, and appear in the drop down list when you edit an indicator's parameters. We just added 4 new PriceComponents to choose from:
-
AveragePriceOHLC (open + high + low + close) / 4
-
AveragePriceHLC (high + low + close) / 3
-
AveragePriceHL (high + low) / 2
-
AveragePriceOC (open + close) / 2
This addition will make life easier when implementing indicators that use these average prices as their default source data. Enjoy!
Modern portfolio theory outlined the concept of periodically rebalancing a portfolio to manage risk, and to safeguard against becoming overexposed to a particular asset type. Balanced portfolios always stay 100% invested, but periodically sell off shares of an asset that has outperformed, using the proceeds to purchase another asset whose weight in the portfolio has diminished.
This historical data extension downloads daily data from Morningstar.com. The site offers a wide array of instruments from around the world. For a comprehensive list, please see Appendix 9.1 of the document below: