M6 - Man, Mind, Money, Markets, Method & Madness
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Long and very good article dont go by title of article
Talks about important Procedural memory play in for profitable trader
http://stockbee.blogspot.in/2016/02/how-to-learn-to-be-profitable-trader.html
Then read this
http://blog.crew.co/the-science-of-memory-and-some-uncommon-tricks-you-can-do-to-enhance-it/
Talks about important Procedural memory play in for profitable trader
http://stockbee.blogspot.in/2016/02/how-to-learn-to-be-profitable-trader.html
Then read this
http://blog.crew.co/the-science-of-memory-and-some-uncommon-tricks-you-can-do-to-enhance-it/
Reasons Traders Lose Money In The Market
http://www.newtraderu.com/2015/01/07/10-reasons-traders-lose-money-in-the-market/
http://www.newtraderu.com/2015/01/07/10-reasons-traders-lose-money-in-the-market/
- A trader must have a trading plan with well-defined entries, exits, and position size before they make any trades. Trading with no plan creates random results, and the profits that are won as a result of chance will eventually return to their rightful owners.
- Traders must have an edge to be profitable. The traders that have discipline, have done their homework about historical price action, and stay in control of their emotions will make money.
- The biggest mistake that the majority of traders make at all levels, is that they trade too big. Big position sizes cause emotions to run high, infringing on reason. Big losses are also more financially and emotionally devastating. The position size of a trade should never put a trader’s lifestyle or trading career at risk.
- When the markets open, the trader must have the discipline to follow the plan they created when the market was closed. No system will work if the trader does not have the discipline to follow it.
- When a trader’s desire to be right is greater than the desire to make money, they will illogically let a losing trade run to avoid admitting that they are wrong.
- Fear of giving back a small profit will cause a trader to miss a bigger winning trade. Most profitability is based on the big winning trades. A winning trade should not be exited until there is a good reason to do so.
- If a trader does not take their original stop loss, they will allow small losses to become big losses. Big losses generally are what cause a trader to be unprofitable. Many good trading systems become profitable simply by removing the big losses from the trading results.
- Traders that do not account for events outside the known bell curve can be ruined. Events that have never happened before can happen. Hedges, stop losses, and position sizing are the insurance policies against the sudden risk of ruin.
- Traders with too much hubris will eventually make a decision that insures a fatal trading result.
- Personal predictions have no value, because the future does not exist in the present moment, no matter how strong a trader’s convictions.
Focus On Signals & Tune Out Noise
http://www.newtraderu.com/2015/01/06/focus-on-signals-tune-out-noise/
Signals tell you when to buy and when to sell for a profit. Noise tells you nothing but information… and if that information cannot be used to make money in the markets then it is not a signal. While watching television or while you are on social media we must have a keen eye for what matters.
This is the NOISE:
These are Signals:
“The signal is the truth. The noise is what distracts us from the truth.” – Nate Silver
The ability to filter out the noise of what does not matter is a skill just as important as finding the signals. The vast majority of traders and investors find themselves lost in the sea of noise. Find the robust signals and incorporate them into a trading plan and then profits will find your trading account.
http://www.newtraderu.com/2015/01/06/focus-on-signals-tune-out-noise/
Signals tell you when to buy and when to sell for a profit. Noise tells you nothing but information… and if that information cannot be used to make money in the markets then it is not a signal. While watching television or while you are on social media we must have a keen eye for what matters.
This is the NOISE:
- Talking heads on television. They could have no position in their picks, they could be pumping their portfolio holding, or talking down their shorts. Who really knows?
- Predictions by anyone are not trading signals. The farther out the predictions are for the more the odds are against the prediction. The complexity of the world and the markets and the randomness of so many moving parts and players make predicting a future that does not exist impossible.
- Known news events are not signals. If there are escalating geo-political risks and the same stories are on the news day after day with no major changes in the situation then that news is already priced in. Continual fear mongering from the media is just noise.
- The shorter the time frame the more random the prices. The farther you zoom out in time the more patterns and trend are identifiable. If you sit in front of your monitor all day the majority of what you see is noise you have to wait until the signal appears inside all that noise.
- Short term trading results tend to be random. Someone with a long bias that holds losing trades until they come back in a bull market looks like a profitable trader with skills. It is the long term results of a trader that signals whether they have skills in system development, discipline, and risk management. Many new traders that get lucky at the beginning and seem to be profitable are shocked when they end up giving back their bull market gains in the next bear market. Their winning streak was just noise in the long term as the market changed.
These are Signals:
- A breakout of a trading range to new highs or new lows in your time frame that holds the new price level.
- An oversold bounce off support or an overbought reversal against resistance in your time frame that is strong enough to convince you it is a high probability trade set up for a swing trade.
- When the market rallies on bad news or sells off on good news that is a signal of a possible end to the markets prevailing trend.
- When popular market sentiment is overwhelming bearish or bullish and there is a strong move against that prevailing sentiment it is a possible signal of a market top or bottom being put in.
- Many times a gap in the direction of the prevailing trend is a signal of a continuation of the trend in the direction of the gap over the long term.
“The signal is the truth. The noise is what distracts us from the truth.” – Nate Silver
The ability to filter out the noise of what does not matter is a skill just as important as finding the signals. The vast majority of traders and investors find themselves lost in the sea of noise. Find the robust signals and incorporate them into a trading plan and then profits will find your trading account.
Meet the DIY Quants Who Ditched Wall Street for the Desert - Dani Burger
http://www.bloomberg.com/news/artic...at-the-gate-the-inexorable-rise-of-diy-quants
In the high desert plain of New Mexico, Roger Hunter monitors automated trades on hog futures and currency pairs. Roger Hunter in his home office.
Roger Hunter in his home office. Photographer: David Paul Morris/Bloomberg
Four computer screens display a dizzying array of price charts and program codes in the office of his single-story, thatched adobe home in the town of Las Cruces. Out back, where scrub brush stretches into the arid plain between the nearby mountains and the Rio Grande, is a 50-foot-tall wireless Internet tower.
The 66-year-old former math professor turned DIY quant apologizes if he appears out of sorts. As chief technology officer of a two-man startup called QTS Capital Management, Hunter just pulled an all-nighter fixing a systems glitch. It’s a far cry from Wall Street, but Hunter wouldn’t have it any other way. “You are away from the hubbub and frantic activity of New York and therefore can be much calmer and more thoughtful,” Hunter said one recent morning, grinding hand-roasted coffee beans. His Kiwi accent is still thick, three decades after decamping to New Mexico to teach abstract algebraic theory. “This is particularly true when developing code and exploring new strategies.”
Even as technology reshapes industry after industry, the huge leaps in computing power are transforming modern finance in ways that few have ever imagined and giving tech-savvy, DIY types like Hunter the tools to compete on a shoestring. Armed with little more than open-source software and an Internet connection, this growing cadre of like-minded startups has razed virtually every barrier to entry in the 40-year-old world of quantitative investing -- where mathematicians code software to profit from price patterns.
And that’s leaving the status quo of once-unassailable titans like Renaissance Technologies and D.E. Shaw exposed to ever more threats. The rise of these upstarts comes as the proliferation of machine-based strategies has made it harder for traditional players to succeed. In January, Martin Taylor of Nevsky Capital closed his 15-year-old hedge fund, lamenting the distorting influence of computer traders.
Sudden Shock
“Technological edge is harder to come by because the more egalitarian these tools have become, the more difficult it is to come up with something truly new,” said Andrew Lo, finance professor at MIT’s Sloan School of Management and chairman of AlphaSimplex, a quant research firm.
Alone, no do-it-yourself quant can match the power, the influence or the money of the industry’s stalwarts. But together, there’s little doubt the DIY crowd is changing the face of financial markets and raising fundamental questions about the industry’s future.
As an ever-increasing pool of quant traders vie for an edge, the “alpha” in finance-speak, some envision a world where so many algorithms are unleashed on electronic markets that old-fashioned research is rendered obsolete and sudden shocks -- such as August’s meltdown in the U.S. stock market -- become all too frequent.
When Hunter arrived in Las Cruces to teach at New Mexico State University after completing his Ph.D. in mathematics from the Australian National University, he never imagined falling in love with the American Southwest, let alone becoming a quant.
Quant Life
An inveterate tinkerer and autodidact with a passion of new technology, Hunter began writing code on the side and developed an early version of software still used by the Federal Reserve to handle advanced math formulas. In the 1990s, he quit teaching and later began dabbling in automated strategies. Hunter teamed up with his partner Ernie Chan after stumbling on one of his investing books, launching QTS as a full-fledged quant fund two years ago.
It’s difficult to know precisely how many startups there are. And in an industry that analysts estimate has ballooned to over $1 trillion in the past year, no good numbers exist. But it’s clear their ranks are swelling.
Membership to Quantopian, a Boston-based firm which started in 2011 to provide coders the tools and software they need to build quantitative strategies, has gone from 1,570 to more than 66,000 today. That figure more than doubled in the past year alone and record numbers joined in January, fueled by the turmoil in global financial markets.
Swelling Ranks
In Manhattan, the elite hedge-fund set still rules, having built up the status and the reputation that comes with years of outsize returns. Point72 Asset Management’s quant business in midtown, with its large glass conference rooms and white walls adorned with founder Steve Cohen’s personal art collection, looks and feels nothing like a startup.
And according to Ross Garon, the head of that business, it has little to fear. Even as the open-source movement gains momentum, the biggest firms still have the best technology and brightest minds (not to mention the most money), to stave off any threat posed by smaller shops, which have yet to prove their staying power.
“The democratization of tools doesn’t necessarily mean there’s the democratization of good judgment of what to research,” said Garon, who runs Cubist Systematic Strategies at Point72, which oversees about $11 billion.
Despite those disadvantages, Hunter and Chan, who works from Niagara-on-the-Lake in southern Ontario, have held their own. QTS returned 12 percent last year, easily outstripping the U.S. stock market and the average for hedge funds globally. Since 2014, QTS assets have grown more than fivefold to $22 million.
Unseen Threat
To keep costs low, QTS uses a service called AlgoSeek to rent tick data for futures, pulling in “astronomical” amounts of information for $500 a month. The firm employs part-time contractors and uses services like Amazon Web Servers to work out complicated models. Hunter himself wrote the code that QTS’s options trade on, which would otherwise cost nearly $50,000.
“There’s a threat they’re missing,” Dan Dunn, who oversees product management and membership at Quantopian, said of traditional quants. “The reality is there are brilliant people all over the world who they have never seen or heard of until they show up and eat their lunch.”
Yet in many ways, the quant industry has become a victim of its own success. Easy adoption is leveling the playing field and making it harder to score easy profits, said David McLean, a finance professor at DePaul University. He cites research showing that three years after an academic paper on an automated strategy is published, returns based on that strategy are cut by more than half.
JPMorgan’s Marko Kolanovic pointed to the perils of a quant trade grown crowded in the events of August, when U.S. stocks plummeted 11 percent in six days. Many blamed China and the Federal Reserve. Kolanovic told clients automatic selling by “price insensitive” quants made everything worse.
Crowded Trades
Where Kolanovic sees danger, Hunter senses opportunity. QTS is considering developing code to profit from distortions that manifest in managed futures when too many quants trade the same strategies.
“We’ve thought about trying to take advantage of it, certainly if the algorithm is clearly affecting the market,” Hunter said.
Doing it yourself has never been easier. Many open-source coding languages like R and Python, which are building blocks for critical number crunching, are posted for free on online libraries. Boutique services like Estimize crowd-source earnings estimates.
“There’s so much data, so much open-sourced software and computing power available,” said Emanuel Derman, director of Columbia University’s financial engineering program and the former head of quantitative risk strategy at Goldman Sachs Group Inc. “You can get up to ground level in no time at all.”
On any given day, Hunter is testing 10 different models while executing eight strategies for clients. He often stares at his screens for hours, tweaking programs and e-mailing QTS’s brokers and data providers. His time horizon is short -- each trade takes at most a few days and is executed via a data center in New Jersey. Soon, Hunter is thinking about starting another office in his native New Zealand, so he can travel there more and continue to work.
Different View
Outside, it’s quiet. The only sound is the faint hum of a truck harvesting pecans, shaking the trunk so the nuts fall into a barrel. On warmer days, it’s not unusual to find Hunter stepping out to sip tea with his wife Sally.
It’s a life he would never dream of giving up for a bigger fund in Manhattan, especially when he has everything he needs right at home.
The big players, “they’re obviously doing well, but it’s different,” Hunter said. “I’m in my little office and I can choose to do whatever I want to do. You don’t even need to be in a certain time zone, it doesn’t matter.”
http://www.bloomberg.com/news/artic...at-the-gate-the-inexorable-rise-of-diy-quants
In the high desert plain of New Mexico, Roger Hunter monitors automated trades on hog futures and currency pairs. Roger Hunter in his home office.
Roger Hunter in his home office. Photographer: David Paul Morris/Bloomberg
Four computer screens display a dizzying array of price charts and program codes in the office of his single-story, thatched adobe home in the town of Las Cruces. Out back, where scrub brush stretches into the arid plain between the nearby mountains and the Rio Grande, is a 50-foot-tall wireless Internet tower.
The 66-year-old former math professor turned DIY quant apologizes if he appears out of sorts. As chief technology officer of a two-man startup called QTS Capital Management, Hunter just pulled an all-nighter fixing a systems glitch. It’s a far cry from Wall Street, but Hunter wouldn’t have it any other way. “You are away from the hubbub and frantic activity of New York and therefore can be much calmer and more thoughtful,” Hunter said one recent morning, grinding hand-roasted coffee beans. His Kiwi accent is still thick, three decades after decamping to New Mexico to teach abstract algebraic theory. “This is particularly true when developing code and exploring new strategies.”
Even as technology reshapes industry after industry, the huge leaps in computing power are transforming modern finance in ways that few have ever imagined and giving tech-savvy, DIY types like Hunter the tools to compete on a shoestring. Armed with little more than open-source software and an Internet connection, this growing cadre of like-minded startups has razed virtually every barrier to entry in the 40-year-old world of quantitative investing -- where mathematicians code software to profit from price patterns.
And that’s leaving the status quo of once-unassailable titans like Renaissance Technologies and D.E. Shaw exposed to ever more threats. The rise of these upstarts comes as the proliferation of machine-based strategies has made it harder for traditional players to succeed. In January, Martin Taylor of Nevsky Capital closed his 15-year-old hedge fund, lamenting the distorting influence of computer traders.
Sudden Shock
“Technological edge is harder to come by because the more egalitarian these tools have become, the more difficult it is to come up with something truly new,” said Andrew Lo, finance professor at MIT’s Sloan School of Management and chairman of AlphaSimplex, a quant research firm.
Alone, no do-it-yourself quant can match the power, the influence or the money of the industry’s stalwarts. But together, there’s little doubt the DIY crowd is changing the face of financial markets and raising fundamental questions about the industry’s future.
As an ever-increasing pool of quant traders vie for an edge, the “alpha” in finance-speak, some envision a world where so many algorithms are unleashed on electronic markets that old-fashioned research is rendered obsolete and sudden shocks -- such as August’s meltdown in the U.S. stock market -- become all too frequent.
When Hunter arrived in Las Cruces to teach at New Mexico State University after completing his Ph.D. in mathematics from the Australian National University, he never imagined falling in love with the American Southwest, let alone becoming a quant.
Quant Life
An inveterate tinkerer and autodidact with a passion of new technology, Hunter began writing code on the side and developed an early version of software still used by the Federal Reserve to handle advanced math formulas. In the 1990s, he quit teaching and later began dabbling in automated strategies. Hunter teamed up with his partner Ernie Chan after stumbling on one of his investing books, launching QTS as a full-fledged quant fund two years ago.
It’s difficult to know precisely how many startups there are. And in an industry that analysts estimate has ballooned to over $1 trillion in the past year, no good numbers exist. But it’s clear their ranks are swelling.
Membership to Quantopian, a Boston-based firm which started in 2011 to provide coders the tools and software they need to build quantitative strategies, has gone from 1,570 to more than 66,000 today. That figure more than doubled in the past year alone and record numbers joined in January, fueled by the turmoil in global financial markets.
Swelling Ranks
In Manhattan, the elite hedge-fund set still rules, having built up the status and the reputation that comes with years of outsize returns. Point72 Asset Management’s quant business in midtown, with its large glass conference rooms and white walls adorned with founder Steve Cohen’s personal art collection, looks and feels nothing like a startup.
And according to Ross Garon, the head of that business, it has little to fear. Even as the open-source movement gains momentum, the biggest firms still have the best technology and brightest minds (not to mention the most money), to stave off any threat posed by smaller shops, which have yet to prove their staying power.
“The democratization of tools doesn’t necessarily mean there’s the democratization of good judgment of what to research,” said Garon, who runs Cubist Systematic Strategies at Point72, which oversees about $11 billion.
Despite those disadvantages, Hunter and Chan, who works from Niagara-on-the-Lake in southern Ontario, have held their own. QTS returned 12 percent last year, easily outstripping the U.S. stock market and the average for hedge funds globally. Since 2014, QTS assets have grown more than fivefold to $22 million.
Unseen Threat
To keep costs low, QTS uses a service called AlgoSeek to rent tick data for futures, pulling in “astronomical” amounts of information for $500 a month. The firm employs part-time contractors and uses services like Amazon Web Servers to work out complicated models. Hunter himself wrote the code that QTS’s options trade on, which would otherwise cost nearly $50,000.
“There’s a threat they’re missing,” Dan Dunn, who oversees product management and membership at Quantopian, said of traditional quants. “The reality is there are brilliant people all over the world who they have never seen or heard of until they show up and eat their lunch.”
Yet in many ways, the quant industry has become a victim of its own success. Easy adoption is leveling the playing field and making it harder to score easy profits, said David McLean, a finance professor at DePaul University. He cites research showing that three years after an academic paper on an automated strategy is published, returns based on that strategy are cut by more than half.
JPMorgan’s Marko Kolanovic pointed to the perils of a quant trade grown crowded in the events of August, when U.S. stocks plummeted 11 percent in six days. Many blamed China and the Federal Reserve. Kolanovic told clients automatic selling by “price insensitive” quants made everything worse.
Crowded Trades
Where Kolanovic sees danger, Hunter senses opportunity. QTS is considering developing code to profit from distortions that manifest in managed futures when too many quants trade the same strategies.
“We’ve thought about trying to take advantage of it, certainly if the algorithm is clearly affecting the market,” Hunter said.
Doing it yourself has never been easier. Many open-source coding languages like R and Python, which are building blocks for critical number crunching, are posted for free on online libraries. Boutique services like Estimize crowd-source earnings estimates.
“There’s so much data, so much open-sourced software and computing power available,” said Emanuel Derman, director of Columbia University’s financial engineering program and the former head of quantitative risk strategy at Goldman Sachs Group Inc. “You can get up to ground level in no time at all.”
On any given day, Hunter is testing 10 different models while executing eight strategies for clients. He often stares at his screens for hours, tweaking programs and e-mailing QTS’s brokers and data providers. His time horizon is short -- each trade takes at most a few days and is executed via a data center in New Jersey. Soon, Hunter is thinking about starting another office in his native New Zealand, so he can travel there more and continue to work.
Different View
Outside, it’s quiet. The only sound is the faint hum of a truck harvesting pecans, shaking the trunk so the nuts fall into a barrel. On warmer days, it’s not unusual to find Hunter stepping out to sip tea with his wife Sally.
It’s a life he would never dream of giving up for a bigger fund in Manhattan, especially when he has everything he needs right at home.
The big players, “they’re obviously doing well, but it’s different,” Hunter said. “I’m in my little office and I can choose to do whatever I want to do. You don’t even need to be in a certain time zone, it doesn’t matter.”
You’re not good enough to be disappointed
Listen to podcast in above link 16 mins
Then read article by james clear
Good Disappointed
Listen to podcast in above link 16 mins
Then read article by james clear
Good Disappointed
Why NPS is not a good investment - Vipin Khandelwal
http://economictimes.indiatimes.com...ot-a-good-investment/articleshow/51559241.cms
Friends want to know whether they should invest in the NPS. With tax saving driving investment decisions, people are drawn to the additional Rs 50,000 deduction under Sec 80CCD(1b). But here is why I wouldn't be investing in the NPS.
Very long lock-in period
All tax-saving investments have lockin periods, but none as long as that of the NPS. The NPS can only be withdrawn at the age of 60. If you start at the age of 25-30, the lock-in period is 30-35 years. Even then, only 60% of the corpus can be withdrawn, and the remaining 40% will have to be put into an annuity for a monthly pension.
Taxation on maturity
The NPS is not really tax-friendly. No one talks about the taxes that will apply at maturity. Out of the 60%, this Budget has proposed to make 40% tax-free on withdrawal, but the remaining 20% will be taxed as income. You can avoid taxation by putting this amount in an annuity, but pension received from an annuity is fully taxable. This is where the NPS loses me.
Low annuity returns
The annuity market in India is nothing to write home about. The yields offered range from 5% to 7%, less than what a bank deposit offers. The highest annuity rate can be earned if you give money to the insurance company and get a pension for life, without return of principal. Even if your investment earns high returns in the accumulation phase, the poor returns on forced annuity will undo most gains.
What about tax savings?
Fans of the NPS say tax saved at the beginning makes up for the drawbacks. For instance, if you invest Rs 50,000 in NPS, you save Rs 15,000 in tax (if you are in the 30% bracket). To test this claim, I compared NPS returns with those of a mutual fund (see chart). While the NPS investor gets Rs 94.97 lakh, and would be taxed for 20% of it, the MF yields Rs 1.02 crore, tax-free.
http://economictimes.indiatimes.com...ot-a-good-investment/articleshow/51559241.cms
Friends want to know whether they should invest in the NPS. With tax saving driving investment decisions, people are drawn to the additional Rs 50,000 deduction under Sec 80CCD(1b). But here is why I wouldn't be investing in the NPS.
Very long lock-in period
All tax-saving investments have lockin periods, but none as long as that of the NPS. The NPS can only be withdrawn at the age of 60. If you start at the age of 25-30, the lock-in period is 30-35 years. Even then, only 60% of the corpus can be withdrawn, and the remaining 40% will have to be put into an annuity for a monthly pension.
Taxation on maturity
The NPS is not really tax-friendly. No one talks about the taxes that will apply at maturity. Out of the 60%, this Budget has proposed to make 40% tax-free on withdrawal, but the remaining 20% will be taxed as income. You can avoid taxation by putting this amount in an annuity, but pension received from an annuity is fully taxable. This is where the NPS loses me.
Low annuity returns
The annuity market in India is nothing to write home about. The yields offered range from 5% to 7%, less than what a bank deposit offers. The highest annuity rate can be earned if you give money to the insurance company and get a pension for life, without return of principal. Even if your investment earns high returns in the accumulation phase, the poor returns on forced annuity will undo most gains.
What about tax savings?
Fans of the NPS say tax saved at the beginning makes up for the drawbacks. For instance, if you invest Rs 50,000 in NPS, you save Rs 15,000 in tax (if you are in the 30% bracket). To test this claim, I compared NPS returns with those of a mutual fund (see chart). While the NPS investor gets Rs 94.97 lakh, and would be taxed for 20% of it, the MF yields Rs 1.02 crore, tax-free.
TP,
Strongly disagree. Over period of time, I would say MF's are always a better option. If the holding period is as long as the Pension scheme, MF's will wins hands down as the minimal return MF's would far outweigh that from NPS. What you get with NPS is possibly a negative return considering inflation i.e less money than what you put in. So in my view, not taking a well considered risk on MF's would be in fact a bigger risk.....
Strongly disagree. Over period of time, I would say MF's are always a better option. If the holding period is as long as the Pension scheme, MF's will wins hands down as the minimal return MF's would far outweigh that from NPS. What you get with NPS is possibly a negative return considering inflation i.e less money than what you put in. So in my view, not taking a well considered risk on MF's would be in fact a bigger risk.....
TP,
Strongly disagree. Over period of time, I would say MF's are always a better option. If the holding period is as long as the Pension scheme, MF's will wins hands down as the minimal return MF's would far outweigh that from NPS. What you get with NPS is possibly a negative return considering inflation i.e less money than what you put in. So in my view, not taking a well considered risk on MF's would be in fact a bigger risk.....
Strongly disagree. Over period of time, I would say MF's are always a better option. If the holding period is as long as the Pension scheme, MF's will wins hands down as the minimal return MF's would far outweigh that from NPS. What you get with NPS is possibly a negative return considering inflation i.e less money than what you put in. So in my view, not taking a well considered risk on MF's would be in fact a bigger risk.....
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