Deutsche Bank AG: Seven price sets are being approximated for DB lowest supports, DB average equilibriums and DB highest resistances using our quantitative systems with "low" to "extreme" local and stochastic volatilities. Each set contains a "percentage", which indicates if DB has been outperforming or downperforming during that day.
DB - Deutsche Bank AG Actual vs. Approximated Equity Prices - 20 Sessions: 2019-02-15 - 2019-03-15
Near-Term Price Targets Computed on Monday, 18 March 2019 ⏱ 16:08 EDT
DB - Deutsche Bank AG: Equilibrium prices are important for DB institutional investing, DB portfolio investing, DB hedge fund investing, DB diversified investing, retail investing and other types of short or long trade and investment transactions. They provide perspectives to traders for DB day/swing trading, DB low/high frequency trading, DB put/call forecasting, DB forward/ reverse trading and DB algorithmic trading. Average equilibrium prices are also significant for DB Buy/Sell/Hold decision makings and DB rating for analyst coverages. They give dynamic premarket knowledge to investing and trading entities and are beneficial for DB trading volume forecasting, DB daily volatility approximating, DB resistance and support evaluating, DB dynamic stop loss calculations, among many other quantitative advantages which common static charts cannot provide such information to traders and investors.
Close Share Price in Previous Session [$]: $8.8800
Share Price Change [$]: $0.3750
Share Price Change [%]: ****0.0422%
Source Market Share [%]: ****0.0148%
Source Volume [V]: 111,538
Average Total Volume [V]: 4,719,385
Company Market Cap [$]: $19,110,649,500.0000
PE Ratio: -8.26
52-Week High [$]: $15.7800
52-Week Low [$]: $7.6200
YTD Change: ****0.1252%
Equilibrium: DB equilibrium is an average theoretical price, wherein both buy and sell flows of DB are equal in a steady-state manner, assuming that all technical and non-technical behaviors and market conditions with respect to DB are fixed. It is important to be taken into account in near-term investing or trading decision makings. It is of utmost interest in DB algorithmic investing and trading.
Sessions: DB data from 20 days has been summarized here, each with eight price sets for DB. The first set contains the actual DB market prices. The other seven sets are our estimated equilibrium prices for DB. On the left, dynamic lowest supports of DB have been displayed and on the right, you can view the dynamic highest resistances of DB.
Volatility: "Low" to "extreme" volatilities have been enforced to our algorithmic systems for computing DB supports and resistances from set 1 to 7, respectively.
Percentage: The underneath "percentage" is an indicator of the difference in between the actual DB and our estimated equilibrium prices for DB.
Supports: DB dynamic supports have been approximated as a function of time and local/stochastic volatility.
Resistances: DB dynamic resistances have been also approximated as a function of time and local/stochastic volatility.
Narrow Supports Range: If 7 dynamic supports of DB are very close, it may illuminate various conclusions for that session, including: (a) there may not have been much trading or investing actions on DB; OR (b) DB may have solid block investors and traders; OR (c) DB might have had a low-risk and low-return session; AND (d) many similar analyses can be extracted from. How would you interpret close dynamic supports range? You might do your own research and share it with your network, boost your investing and trading ration and decision makings.
Distant Supports Range: If 7 dynamic supports of DB are very scattered, it may indicate a number of points for that session, including: (a) there might have been much sophisticated trading actions on DB; OR (b) DB might have had adept traders in that session; OR (c) DB may have had a high-risk and high-return session; AND (d) many similar analyses can be obtained. What would be your views about scattered dynamic supports range?
Narrow Resistances Range: If 7 dynamic resistances of DB are very close, it may suggest outcomes, including: (a) DB holds strong resistances that are challenging for breakouts and may require strong catalysts; OR (b) DB may have had adept reverse traders in that session; OR (c) DB may not have had sufficient upside actions in that session; OR (d) DB might have had a low-risk and low-return or negative session.
Research and Analysis: There are numerous technical methods to speculate such DB data, which are beneficial for investing and low to high-frequency trading decisions. How would you hypothesize and conclude?
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⚖ All DB information and assumptions hereof are connected to delayed 60-seconds data pipelines, extracted from version 1.0 API at https://api.iextrading.com/1.0/, in addition to other financial and economical data. All computations are entirely automatic, currently independent of all news, sentiments, external parameters and internal corporate factors. This page is an alpha prototype and is updated a few times per hour based on our dynamic indices, and may not be accessible at all times, and inadvertent or unknown inaccuracies exist herein.
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