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The Framing Effect and Confirmation Bias
The framing effect describes our tendency to react to, judge, or interpret the exact same information in distinctly different ways depending on how it is presented to us, or “framed” (most commonly, whether the information is framed as a loss or as a gain). Building off of the previously discussed concepts of loss aversion and Prospect Theory, people tend to avoid risk when information is presented in a positive frame but seek risk when information is presented in a negative frame.
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The most commonly cited example of this is a 1981 Tversky and Kahneman study that asked participants to choose between two treatments, A and B, for 600 people affected by a deadly disease. Treatment A was predicted to result in a guaranteed total of 400 deaths, while treatment B had a 33% chance that no one would die but a 66% chance that everyone would die. The same two alternatives were then presented to the study's participants either under a positive frame (how many peoples' lives would be saved) or under a negative frame (how many people would die).
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When the alternatives were framed positively, 72% of participants chose Treatment A (“saves 200 lives”). When the exact same alternatives were framed negatively, however, only 22% of participants chose Treatment A (now presented as “400 people will die”). Saving 200 of the 600 lives is the exact same outcome as letting 400 of the 600 die, but the manner in which this identical treatment option was framed resulted in a massive decrease in the number of participants who chose it. Under the positive frame, the majority of participants avoided risk by choosing the treatment that resulted in a sure saving of 200 lives. Under the negative frame, however, the majority of participants sought the riskier alternative treatment that offered a 33% chance of saving all 600 lives.
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Another famous example that demonstrates the impact of framing is a study that found 93% of PhD students registered for classes early when a penalty fee for late registration was emphasized, but only 67% did so when the same number was presented as a discount for early registration.
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It is no secret that investors in the financial markets are under a constant barrage of information from all different sides - bullish, bearish, and everything in between. The exact same information can be framed by multiple sources in many different ways, biasing your interpretation of it. As you filter the stream of news and financial data that comes your way, consider the manner in which those numbers, statistics or reports are framed and think about the impact that their presentation has on the opinions they lead you to form.
Confirmation Bias and Forex Signals
Confirmation bias is the tendency to overweight, favor, seek out, exaggerate or more readily recall information or alternatives in a way that confirms our preconceived beliefs, hypotheses or desires, while simultaneously undervaluing, ignoring or otherwise giving disproportionately less consideration to information or alternatives that do not confirm our preconceived beliefs, hypotheses or desires. This inherent flaw in our cognitive reasoning leads to misconstrued interpretations of information, errors in judgment, and poor decision making. The effects of confirmation bias have been shown to be much stronger for emotionally-charged issues or beliefs that are deeply entrenched. In addition to overvaluing information that confirms our preexisting beliefs, confirmation bias also includes our tendency to interpret ambiguous evidence as supporting existing positions, even if no true relationship exists. In short, this concept says that individuals are biased towards information that confirms their existing beliefs and biased against information that disproves their existing beliefs, leading to overconfidence in our opinions and our decisions even in the face of strong contrary evidence.
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 As an investor in the financial markets, it can be difficult to maintain a separation between informed estimates or expectations and emotional judgments based on hopes or desires. By causing us to overweight information that confirms such hopes or desires, confirmation bias can affect our abilities to make sound assessments and form well-reasoned opinions about, for example, a stock's upside potential. Awareness of our natural biases towards confirming information and, perhaps more importantly, our biases against disproving information is the first step in combating the unwanted effects of confirmation bias.
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Hindsight Bias and the Availability Heuristic
Hindsight bias describes our inclination, after an event has occurred, to see the event as having been predictable, even if there had been little to no objective basis for predicting it. This is the psychological tendency that causes us, after witnessing or experiencing the outcome of even an entirely unforeseeable event, to exclaim “I knew it all along!”
 
The discovery of hindsight bias emerged during the early 1970s as the field of psychology witnessed an expansion of investigations into heuristics and biases, largely led by Amos Tversky and Daniel Kahneman. Along with the uncovering of tendencies such as the hindsight bias came the discovery of the availability heuristic, a common mental shortcut that causes individuals to rely on immediate information or examples that come to mind first when evaluating a specific topic, concept, method or decision. According to the cognitive reasoning behind the availability heuristic, if something can be recalled, it must be important, or at least more so than alternatives that are not as readily recalled. As a result, individuals tend to more heavily weight recent or immediately-recalled information, creating a bias towards the latest news, events, experiences or memories.
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The Sunk Cost Fallacy
The sunk cost fallacy rests on the economic concept of a sunk cost: a cost that has already been incurred and cannot be recovered. While theoretical economics says that only future (prospective) costs are relevant to an investment decision and that rational economic actors therefore should not let sunk costs influence their decisions, the findings of psychological and behavioral finance research show that sunk costs do in fact affect real-world human decision making. Because of our tendencies towards Loss Aversion and other cognitive biases, we fall victim to the sunk cost fallacy, which describes our irrational belief that sunk costs should be considered a legitimate factor in our forward decision making when, in fact, their consideration often leads us towards inefficient outcomes.
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For example, let's say a gentleman named Fred is concerned about his weight and decides to go on a diet. As part of his cleanse, he empties his fridge of all tasty temptations. When he comes across an unopened tub of ice cream, however, he falls victim to the Sunk Cost Fallacy. Even though the $15.00 Fred spent on the ice cream is a sunk cost that has already been incurred and cannot be recovered, Fred convinces himself that he cannot let the ice cream go to waste because he previously spent his hard-earned dollars to buy it. Eating a full tub of ice cream is in no way in line with his current weight-loss objectives, as the calories he will take in by consuming it are many times the daily total target of his new diet. Still, despite the adverse consequences for his health goals, Fred is swayed into eating the ice cream because of the Sunk Cost Fallacy.
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In an investment setting, the consequences of the sunk cost fallacy can be much more severe than some unwanted calories. As the share price of a security falls, investors often begin to employ the logic that “I've already lost $XXX, it's too late to sell now.” As prices keep falling further and losses grow, the investor's commitment to the sunk cost continues to escalate. “Now I’ve lost $XXXXX, there's no way I can sell now. It has to come back eventually. I'll just hold on to it.” Improper or irrational considerations of sunk costs can lead to poor decisions that continue to spiral out of control, simply because of an incorrect perception of an expense that is irrecoverable.
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The Gambler's Fallacy
The gambler's fallacy, also known as the Monte Carlo Fallacy, is the mistaken tendency to believe that, if something happens more frequently than “normal” during a period of time, it must happen less frequently in the future, or that, if something happens less frequently than “normal” during a period of time, it must happen more frequently in the future. This tendency presumably arises out of an ingrained human desire for nature to be constantly balanced or averaged. In situations where the event being observed or measured is truly random (such as the flip of a coin), this belief, although appealing to the human mind, is false.
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The gambler's fallacy is, rather obviously, most strongly associated with gambling, where such errors in judgment and decision making are common. It can, however, arise in many practical situations, including investing. Winning and losing trades are in many ways similar to the flip of a coin and thus subject to the same psychological biases. If an investor has a series of losing trades, for example, he or she can begin to erroneously believe that, since the statistics feel unbalanced, his or her probability of making a profitable trade increases. In reality, the probability of his or her next trade being profitable is unaffected by previous losses.
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I'm installing Gekko on Windows 10. I've installed nodejs and git correctly(I believe), and when I install the dependencies after downloading gekko through git commands I get this error message:

C:\Users\baldi\gekko>npm install --only=production

> sqlite3@4.0.4 install C:\Users\baldi\gekko\node_modules\sqlite3
> node-pre-gyp install --fallback-to-build

node-pre-gyp WARN Using request for node-pre-gyp https download
node-pre-gyp WARN Tried to download(403): https://mapbox-node-binary.s3.amazonaws....x64.tar.gz
node-pre-gyp WARN Pre-built binaries not found for sqlite3@4.0.4 and node@12.18.0 (node-v72 ABI, unknown) (falling back to source compile with node-gyp)
node-pre-gyp WARN Pre-built binaries not installable for sqlite3@4.0.4 and node@12.18.0 (node-v72 ABI, unknown) (falling back to source compile with node-gyp)
node-pre-gyp WARN Hit error Connection closed while downloading tarball file
Building the projects in this solution one at a time. To enable parallel build, please add the "-m" switch.
Building the projects in this solution one at a time. To enable parallel build, please add the "-m" switch.
C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\MSBuild\Microsoft\VC\v160\Microsoft.CppBuild.targets(378,
5): error MSB8020: The build tools for Visual Studio 2015 (Platform Toolset = 'v140') cannot be found. To build using t
he v140 build tools, please install Visual Studio 2015 build tools.  Alternatively, you may upgrade to the current Visu
al Studio tools by selecting the Project menu or right-click the solution, and then selecting "Retarget solution". [C:\
Users\baldi\gekko\node_modules\sqlite3\build\deps\action_before_build.vcxproj]
C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\MSBuild\Microsoft\VC\v160\Microsoft.CppBuild.targets(378,
5): error MSB8020: The build tools for Visual Studio 2015 (Platform Toolset = 'v140') cannot be found. To build using t
he v140 build tools, please install Visual Studio 2015 build tools.  Alternatively, you may upgrade to the current Visu
al Studio tools by selecting the Project menu or right-click the solution, and then selecting "Retarget solution". [C:\
Users\baldi\gekko\node_modules\sqlite3\build\deps\action_before_build.vcxproj]
gyp ERR! build error
gyp ERR! stack Error: `C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\MSBuild\Current\Bin\MSBuild.exe` failed with exit code: 1
gyp ERR! stack     at ChildProcess.onExit (C:\Program Files\nodejs\node_modules\npm\node_modules\node-gyp\lib\build.js:194:23)
gyp ERR! stack     at ChildProcess.emit (events.js:315:20)
gyp ERR! stack     at Process.ChildProcess._handle.onexit (internal/child_process.js:275:12)
gyp ERR! System Windows_NT 10.0.17763
gyp ERR! command "C:\\Program Files\\nodejs\\node.exe" "C:\\Program Files\\nodejs\\node_modules\\npm\\node_modules\\node-gyp\\bin\\node-gyp.js" "build" "--fallback-to-build" "--module=C:\\Users\\baldi\\gekko\\node_modules\\sqlite3\\lib\\binding\\node-v72-win32-x64\\node_sqlite3.node" "--module_name=node_sqlite3" "--module_path=C:\\Users\\baldi\\gekko\\node_modules\\sqlite3\\lib\\binding\\node-v72-win32-x64" "--napi_version=6" "--node_abi_napi=napi"
gyp ERR! cwd C:\Users\baldi\gekko\node_modules\sqlite3
gypgyp ERR! node -v v12.18.0
gyp  ERR!ERR!  node-gyp -v v5.1.0
build errorgyp
 ERR!gyp  not ok
ERR! stack Error: `C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\MSBuild\Current\Bin\MSBuild.exe` failed with exit code: 1
gyp ERR! stack     at ChildProcess.onExit (C:\Program Files\nodejs\node_modules\npm\node_modules\node-gyp\lib\build.js:194:23)
gyp ERR! stack     at ChildProcess.emit (events.js:315:20)
gyp ERR! stack     at Process.ChildProcess._handle.onexit (internal/child_process.js:275:12)
gyp ERR! System Windows_NT 10.0.17763
gyp ERR! command "C:\\Program Files\\nodejs\\node.exe" "C:\\Program Files\\nodejs\\node_modules\\npm\\node_modules\\node-gyp\\bin\\node-gyp.js" "build" "--fallback-to-build" "--module=C:\\Users\\baldi\\gekko\\node_modules\\sqlite3\\lib\\binding\\node-v72-win32-x64\\node_sqlite3.node" "--module_name=node_sqlite3" "--module_path=C:\\Users\\baldi\\gekko\\node_modules\\sqlite3\\lib\\binding\\node-v72-win32-x64" "--napi_version=6" "--node_abi_napi=napi"
gyp ERR! cwd C:\Users\baldi\gekko\node_modules\sqlite3
gyp node-pre-gypERR!  node -vERR! v12.18.0
 gypbuild error
ERR! node-pre-gypnode-gyp -v  v5.1.0
ERR!gyp  stackERR! Error: Failed to execute 'C:\Program Files\nodejs\node.exe C:\Program Files\nodejs\node_modules\npm\node_modules\node-gyp\bin\node-gyp.js build --fallback-to-build --module=C:\Users\baldi\gekko\node_modules\sqlite3\lib\binding\node-v72-win32-x64\node_sqlite3.node --module_name=node_sqlite3 --module_path=C:\Users\baldi\gekko\node_modules\sqlite3\lib\binding\node-v72-win32-x64 --napi_version=6 --node_abi_napi=napi' (1)
 node-pre-gypnot ok
ERR! stack     at ChildProcess.<anonymous> (C:\Users\baldi\gekko\node_modules\node-pre-gyp\lib\util\compile.js:83:29)
node-pre-gyp ERR! stack     at ChildProcess.emit (events.js:315:20)
node-pre-gyp ERR! stack     at maybeClose (internal/child_process.js:1021:16)
node-pre-gyp ERR! stack     at Process.ChildProcess._handle.onexit (internal/child_process.js:286:5)
node-pre-gyp ERR! System Windows_NT 10.0.17763
node-pre-gyp ERR! command "C:\\Program Files\\nodejs\\node.exe" "C:\\Users\\baldi\\gekko\\node_modules\\node-pre-gyp\\bin\\node-pre-gyp" "install" "--fallback-to-build"
node-pre-gyp ERR! cwd C:\Users\baldi\gekko\node_modules\sqlite3
node-pre-gyp ERR! node -v v12.18.0
node-pre-gyp ERR! node-pre-gyp -v v0.10.3
node-pre-gyp ERR! not ok
Failed to execute 'C:\Program Files\nodejs\node.exe C:\Program Files\nodejs\node_modules\npm\node_modules\node-gyp\bin\node-gyp.js build --fallback-to-build --module=C:\Users\baldi\gekko\node_modules\sqlite3\lib\binding\node-v72-win32-x64\node_sqlite3.node --module_name=node_sqlite3 --module_path=C:\Users\baldi\gekko\node_modules\sqlite3\lib\binding\node-v72-win32-x64 --napi_version=6 --node_abi_napi=napi' (1)
npm ERR! code ELIFECYCLE
npm ERR! errno 1
npm ERR! sqlite3@4.0.4 install: `node-pre-gyp install --fallback-to-build`
npm ERR! Exit status 1
npm ERR!
npm ERR! Failed at the sqlite3@4.0.4 install script.
npm ERR! This is probably not a problem with npm. There is likely additional logging output above.

npm ERR! A complete log of this run can be found in:
npm ERR!     C:\Users\baldi\AppData\Roaming\npm-cache\_logs\2020-06-09T21_46_04_806Z-debug.log

What am I doing wrong?

Hello, guys, I want to start to build my apps on a better platform than we have right now I see so many flaws in the current models.
 
However, what if there was an option in just 30days? An option like no other to be perfectly honest. I'm not here to waste time and money on the poor system, I was the best of the best for my business. Now, This Libonomy is a new breed compared to the rest, seems like the founders were real normal people who don't give a crap about the politics or bankers of this world, They have designed us all a new way forward.  AI consensus Algorithm engine helping support a level of new support layers, including a unique, exploit finding node. This will be used to stop hackers. So much more reading into the road map, but I'm here to ask for your support and check this out. This and we all builders would be a nice new option for all of us to use.
 
Google Libonomy on Github, they have some P2p to view Testnet data there.
 
Looking for support only people. Is this worth our time?
 
Over and Out

T

Hi Guys

i have been having moderate success with the bot,but am finding a strange issue, i am trading in USDT/BTC, but when the bot creates a sticky buy and fills it, so buys btc with usdt, there is always a balance left behind in the usdt account, it never seems to do a 100% buy.

Any ideas?


Thank you in advance

How do I get Gekko to never sell under buy price, and keep the open position even if the market takes a plunge?

Thanks

Hi at all,

I have a NAS, which allows to run Docker-Cotainer. Finally I figured out, how i can run Gekko / GreenGekko.


Some Papertrading Bots are working (in most results with negative PnL ;-) )

How can I edit files e.g. config or strategies? Or use Plugins e.g. TelegramBot?

In the UI is no option for this.

Which folder should I mount as a Volume? Or how i can interact with Gekko? I still confused.

I am a Windows-User, so i thought it is a good idea to use a Container for the *nix stuff.

And which Docker-Container is maintained e.g. a green Gekko

Or is there a more common fork out there?


Many questions, i hope for some answers.

Thanks

maal

Asset Allocation and Diversification
The term "asset allocation" is often used to describe a money management strategy that defines how capital is distributed within an investment portfolio and Forex Signals . This usually includes specifying the amount of the portfolio that should be distributed to different asset classes, or broad types of investments such as stocks, bonds, commodities, and cash. The goal of asset allocation is to improve the mix of investments in different asset classes in order to maximize the return on the investment portfolio while minimizing potential risks, based on the investor’s time frame, risk tolerance, and long-term investment goals. There is evidence to suggest that certain classes of assets work better or worse depending on economic conditions, market forces, government policy, and political influence. The goal of an asset allocation strategy is to define these terms and allocate resources appropriately.
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The concept that is closely related to asset allocation is "diversification", and in practice these terms are often used interchangeably. However, asset allocation is primarily concerned with capital allocation in different asset classes. For example, a typical asset allocation strategy might dictate that your portfolio must invest 50% in stocks, 30% invest in bonds, 10% in commodities, and 10% in cash. Diversification is usually associated with capital allocation within these asset classes. For example, within allocating shares to the same portfolio, investments can be allocated to 50% of shares of large companies, 20% of shares of medium-sized companies, 10% of shares of small companies, 10% of international shares, and 10% of market shares Emerging. The concept of diversification involves the allocation of assets within individual asset classes - while risks are distributed among asset classes in the overall portfolio, diversification reduces the risk within each asset class.
 
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Asset distribution date
Using asset allocation strategies as a form of risk management is not a new concept. The idea of "not to put all your eggs in one basket" is something we learned when we were young and lived in for thousands of years. However, the term asset allocation was not present in the investment community until recently. Even before the emergence of modern financial markets, people understood that one's assets must be divided between different categories such as land, business ownership, and (cash) reserves. Until the mid-twentieth century, this concept of asset allocation as a fact of life remained relatively unchanged.
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So what has changed to create the asset allocation models that we know today? In 1952, an American economist named Harry Markowitz wrote a paper in the finance magazine called "Portfolio Selection," in which he developed the first mathematical model that emphasizes volatility in the portfolio by combining investments and different yield patterns. This paper was the basis for what would have become a standard in portfolio management known as "modern portfolio theory".
 
Before Markowitz contributed to asset allocation to equity portfolios, diversification was a process that focused on the yield and risk characteristics of individual securities regardless of how the returns relate to each other. After Markowitz invented his mathematical models for wallet construction, his ideas soon became accepted in academia. A large number of research papers have been published to verify the benefits of asset allocation and have quickly become popular with financial professionals as well.
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 In 1974, the Employees Retirement Income Guarantee Act (ERISA) was enacted as a federal law setting minimum standards for investment allocations in pension plans. After ERISA became law, asset allocation and modern portfolio theory became standard practices for portfolio managers required to comply with the law when allocating invested capital in pension plans.
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Modern Portfolio Theory and Concepts (MPT)
The modern portfolio theory (MPT) has greatly influenced the way investment portfolio managers create portfolios. The concept of MPT is fairly straightforward. However, it requires the investor to make several assumptions about the financial markets; In addition, the mathematical equations used to calculate correlation and risk can be somewhat complicated.
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The basic premise of MPT is simple: by bringing together securities of different asset classes that are not closely related, one can reduce portfolio volatility and increase risk-adjusted performance. In other words, a combination of unrelated assets will lead to the most efficient portfolio - the portfolio that yields the greatest return for a given amount of risk.
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 Returns of assets do not have to be passively or even disconnected to provide diversification benefits, as they cannot be fully correlated. For example, in the chart below, international stocks (as represented by the EAFE index) are compared to US domestic shares (as represented by the S&P 500). Using the correlation coefficient, you can see that the relationship is positive for most of the five-year period. However, it is not completely correlated (i.e. a correlation coefficient of 1.0).
 
 
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There are periods of low to negative correlation received during this time period. By investing in both US domestic stocks and international stocks, general volatility can be reduced as the correlation varies enough between the two asset classes to provide meaningful diversification. The MPT concept demonstrates that adding a volatile asset to a portfolio can reduce overall volatility if returns have differences in correlation. This is an intriguing concept - portfolio volatility can generally be minimized by bringing together asset classes together which, in themselves, have returns with higher volatility.
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The assumption is that by combining asset classes that are not fully correlated when one asset’s value declines, the value of another asset in the portfolio increases over the same time period. So even if all asset classes themselves are very volatile, when combined into one portfolio, volatility is reduced.
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The extreme example of negative correlation is shown in the following chart of the US dollar compared to the price of gold over the past five years. If the investor had invested in these two volatile assets together, the overall volatility of the portfolio would have decreased significantly due to the negative correlation.
 
As mentioned earlier, the MPT requires that the investor make some assumptions about financial markets in order to calculate the potential benefits of the theory. The main assumptions are that ...
 
Financial markets are efficient.
Market returns are distributed randomly.
Investors are rational.
These assumptions are necessary to accurately calculate standard deviation and correlation using the normal distribution or bell curve.
 
By using the normal distribution function (which determines risk as the standard deviation of return), the risk and the correlation can be calculated mathematically for individual assets as well as portfolios. However, if the markets are in fact not fully effective, the returns to the assets do not necessarily follow the normal distribution and the correlation accounts and risks used in the MPT may be flawed. With the extreme market volatility seen during the dot com bubble in the early 2000s and the 2008-2009 financial crisis, the assumptions used in the MPT have been largely examined.
 
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