u/Kindly_Preference_54

1. Trusting without proof. Always remember this: no verified profitable account = no profitable trader = knows nothing.
2. Unwillingness to try and experiment. You didn't try it = it does not work. There is never too much trial and experimentation.
3. Mental dependence. Rules told by others = your chains.
4. Ego. If you are not profitable, don't argue with profitable traders. Learn from them. Their rules are real. Throw your "knowledge" to the garbage, and replace with theirs.
5. ? - Add your ideas

Hey everyone,

I am a systematic algorithmic trader. I have only one "psychology" rule: don't ever interfere with your automated strategy that has been proven with statistical significance.

But

What if the circumstances are unique? I currently have several positions, some in profit and some in loss. I know that if the war with Iran restarts (high risk), these positions will probably move against me due to risk aversion.

On the other hand, my strategy is always stress-tested, including during wars.

Yet, I look at these positions and I feel tempted to close them.

Would you interfere and close those positions?

a strategy capacity of $190M, and managing an external investment of €100k that has a high chance of growing to several million (paid 20% of profits).

The question to you all:

Should such a trader seek a job in the quant industry?

Hi everyone,

Two years ago I made my first million and I I honestly thought sky is the limit, so I decided I would stop trading and invest most of my capital into safer assets for my future. I traveled for a while, met people, and tried to enjoy life. But after some time I realized that when you remove the goal and struggle from your life, you start being bored. The people I met already had their own life, families, and goals, and I wasn't part of their plans.

That’s when I decided to start teaching people how to trade. Not for money. Just because I wanted to help talented people avoid the mistakes I made. I started posting here on Reddit, answering questions, coaching a few students privately (never more than 3 at the same time), and eventually recording several introduction courses. Over time I realized that teaching gave me more satisfaction than making money itself.

A lot of people ask me what strategy I use, so I’ll briefly explain the core philosophy behind it. My approach is based on multi-timeframe market structure analysis combined with liquidity sweeps, institutional order-flow concepts, volatility compression, and momentum confirmation. I mainly focus on identifying engineered liquidity zones where retail traders are statistically trapped before expansion phases. The process starts with weekly and daily directional bias. Then I refine entries on lower timeframes using: market structure shifts, fair value gaps, displacement candles, volume imbalance, Fibonacci premium/discount zones, ATR-based volatility filters, session timing, psychological price levels, and confirmation through price delivery algorithms.

Risk management is everything.

I never risk more than 0.5%–1% per trade, and discipline matters more than strategy. Most traders fail not because their strategy is bad, but because they lack emotional control and consistency. People always search for a “holy grail,” but the real edge comes from patience, journaling, discipline, and understanding market psychology. Here’s an expanded “discipline guru” section you can insert before the disclaimer to make the satire even stronger:

The biggest misconception in trading is that people lose because of strategy. They don’t. In my opinion, almost every strategy can work if the trader behind it has enough discipline.

Discipline is the real edge.

Most traders already know what they are supposed to do: cut losses, let winners run, avoid revenge trading, stay patient, respect risk, follow the plan, avoid emotional decisions. But knowing something intellectually and actually executing it consistently are two completely different things. The market is designed to attack human psychology. Fear makes people close winning trades too early. Greed makes them overleverage.Ego makes them average into losers. Boredom makes them force trades that were never there.Hope makes them hold bad positions. Impatience makes them abandon profitable systems before the edge statistically plays out. Most people are not losing against the market. They are losing against themselves. That is why I always tell my students that trading is not really about predicting price. Trading is about predicting your own behavior under stress. When I first started trading, I had to completely rebuild my mindset and daily habits. I removed distractions. I stopped checking social media during trading sessions. I created strict routines. I journaled every trade. I tracked emotional states before and after execution. I reviewed losing weeks in detail. I learned to sit still and do nothing for hours if conditions were not aligned. A professional trader must become emotionally neutral. You cannot become euphoric after winning. You cannot become depressed after losing. You cannot increase risk because you “feel confident.” You cannot break rules because “this setup looks different.” The market punishes emotional inconsistency very quickly. One thing I learned is that discipline is not something you use only in trading. It becomes part of your personality. The same discipline that allows someone to: wake up early, exercise consistently, eat correctly, avoid distractions,control impulses, and stay focused for years, is usually the same discipline that allows them to become profitable traders. People constantly ask me: “What indicator changed your trading?” Honestly? Probably none. The biggest change happened when I stopped trying to get rich quickly and started behaving like a risk manager instead of a gambler. This post is fiction. I wonder how many readers trusted the c**p above and immediately wanted mentorship, courses, or strategy details without even seeing a verified profitable live track record that proved that c**p I told in the beginning. That's exactly how "mentors" and influencers make you waste your time or your money. Once you truly understand that survival is more important than profit, your entire perspective changes. A disciplined trader understands that: there will always be another setup, missing a trade means nothing, protecting capital is the first priority, and consistency matters more than excitement. The market rewards boring behavior. Most beginners want adrenaline. Professionals want stability. And ironically, the moment you stop chasing money emotionally is usually the moment your performance improves the most.

Anyway, that’s my story. Just wanted to share it with all of you.

Hi everyone,

Two years ago I made my first million and I I honestly thought sky is the limit, so I decided I would stop trading and invest most of my capital into safer assets for my future. I traveled for a while, met people, and tried to enjoy life. But after some time I realized that when you remove the goal and struggle from your life, you start being bored. The people I met already had their own life, families, and goals, and I wasn't part of their plans.

That’s when I decided to start teaching people how to trade. Not for money. Just because I wanted to help talented people avoid the mistakes I made. I started posting here on Reddit, answering questions, coaching a few students privately (never more than 3 at the same time), and eventually recording several introduction courses. Over time I realized that teaching gave me more satisfaction than making money itself.

A lot of people ask me what strategy I use, so I’ll briefly explain the core philosophy behind it. My approach is based on multi-timeframe market structure analysis combined with liquidity sweeps, institutional order-flow concepts, volatility compression, and momentum confirmation. I mainly focus on identifying engineered liquidity zones where retail traders are statistically trapped before expansion phases. The process starts with weekly and daily directional bias. Then I refine entries on lower timeframes using: market structure shifts, fair value gaps, displacement candles, volume imbalance, Fibonacci premium/discount zones, ATR-based volatility filters, session timing, psychological price levels, and confirmation through price delivery algorithms.

Risk management is everything.

I never risk more than 0.5%–1% per trade, and discipline matters more than strategy. Most traders fail not because their strategy is bad, but because they lack emotional control and consistency. People always search for a “holy grail,” but the real edge comes from patience, journaling, discipline, and understanding market psychology. Here’s an expanded “discipline guru” section you can insert before the disclaimer to make the satire even stronger:

The biggest misconception in trading is that people lose because of strategy. They don’t. In my opinion, almost every strategy can work if the trader behind it has enough discipline.

Discipline is the real edge.

Most traders already know what they are supposed to do: cut losses, let winners run, avoid revenge trading, stay patient, respect risk, follow the plan, avoid emotional decisions. But knowing something intellectually and actually executing it consistently are two completely different things. The market is designed to attack human psychology. Fear makes people close winning trades too early. Greed makes them overleverage.Ego makes them average into losers. Boredom makes them force trades that were never there.Hope makes them hold bad positions. Impatience makes them abandon profitable systems before the edge statistically plays out. Most people are not losing against the market. They are losing against themselves. That is why I always tell my students that trading is not really about predicting price. Trading is about predicting your own behavior under stress. When I first started trading, I had to completely rebuild my mindset and daily habits. I removed distractions. I stopped checking social media during trading sessions. I created strict routines. I journaled every trade. I tracked emotional states before and after execution. I reviewed losing weeks in detail. I learned to sit still and do nothing for hours if conditions were not aligned. A professional trader must become emotionally neutral. You cannot become euphoric after winning. You cannot become depressed after losing. You cannot increase risk because you “feel confident.” You cannot break rules because “this setup looks different.” The market punishes emotional inconsistency very quickly. One thing I learned is that discipline is not something you use only in trading. It becomes part of your personality. The same discipline that allows someone to: wake up early, exercise consistently, eat correctly, avoid distractions,control impulses, and stay focused for years, is usually the same discipline that allows them to become profitable traders. People constantly ask me: “What indicator changed your trading?” Honestly? Probably none. The biggest change happened when I stopped trying to get rich quickly and started behaving like a risk manager instead of a gambler. This post is fiction. I wonder how many readers trusted the c**p above and immediately wanted mentorship, courses, or strategy details without even seeing a verified profitable live track record that proved that c**p I told in the beginning. That's exactly how "mentors" and influencers make you waste your time or your money. Once you truly understand that survival is more important than profit, your entire perspective changes. A disciplined trader understands that: there will always be another setup, missing a trade means nothing, protecting capital is the first priority, and consistency matters more than excitement. The market rewards boring behavior. Most beginners want adrenaline. Professionals want stability. And ironically, the moment you stop chasing money emotionally is usually the moment your performance improves the most.

Anyway, that’s my story. Just wanted to share it with all of you.

Please don't fall for this. Any price data for almost any asset can be obtained for free through different platforms. MT5 and Yahoo finance are just several examples. A few clicks, and the data is yours in different file formats.

Hey everyone,

I keep seeing people presenting one single long backtest as proof that their quant strategy works. That is basically not a test at all, because you use one period to develop (fit) a strategy and then expect it to work on another period. That's called a curve fit. Curve fits don't work on another curve, unless you want to be a gambler.

To evaluate whether a strategy actually has an edge, you should perform a walk-forward analysis (WFA). The idea is that the strategy has to repeatedly survive unseen market periods after recalibration. That is much harder than simply fitting one long historical sample.

If at least about 10 independent forward out-of-sample cycles remain profitable/stable, then you probably start seeing evidence of real statistical significance, rather than pure historical fitting.

Simple example of a rolling WFA:

You are currently at month 25.

Cycle 1:

• Months 13–15: optimization/recalibration in-sample (IS)
• Months 10–12: backward out-of-sample (OOS) validation
• Months 16–17: forward OOS test

Cycle 2:

• Months 15–17: optimization IS
• Months 12–14: backward OOS validation
• Months 18–19: forward OOS test

And so on.

Hey everyone,

I keep seeing people presenting one single long backtest as proof that their strategy works. That is basically not a test at all, because you use one period to develop (fit) a strategy and then expect it to work on another period. That's called a curve fit. Curve fits don't work on another curve, unless you want to be a gambler.

To evaluate whether a strategy actually has an edge, you should perform a walk-forward analysis (WFA). The idea is that the strategy has to repeatedly survive unseen market periods after recalibration. That is much harder than simply fitting one long historical sample.

If at least about 10 independent forward out-of-sample cycles remain profitable/stable, then you probably start seeing evidence of real statistical significance, rather than pure historical fitting.

Simple example of a rolling WFA:

You are currently at month 25.

Cycle 1:

• Months 13–15: optimization/recalibration in-sample (IS)
• Months 10–12: backward out-of-sample (OOS) validation
• Months 16–17: forward OOS test

Cycle 2:

• Months 15–17: optimization IS
• Months 12–14: backward OOS validation
• Months 18–19: forward OOS test

And so on.

Much success!

Hey everyone,

I keep seeing people presenting one single long backtest as proof that their strategy works. That is basically not a test at all, because you use one period to develop (fit) a strategy and then expect it to work on another period. That's called a curve fit. Curve fits don't work on another curve, unless you want to be a gambler.

To evaluate whether a strategy actually has an edge, you should perform a walk-forward analysis (WFA). The idea is that the strategy has to repeatedly survive unseen market periods after recalibration. That is much harder than simply fitting one long historical sample.

If at least about 10 independent forward out-of-sample cycles remain profitable/stable, then you probably start seeing evidence of real statistical significance, rather than pure historical fitting.

Simple example of a rolling WFA:

You are currently at month 25.

Cycle 1:

• Months 13–15: optimization/recalibration in-sample (IS)
• Months 10–12: backward out-of-sample (OOS) validation
• Months 16–17: forward OOS test

Cycle 2:

• Months 15–17: optimization IS
• Months 12–14: backward OOS validation
• Months 18–19: forward OOS test

And so on.

Much success!

Hey everyone,

I keep seeing people presenting one single long backtest as proof that their strategy works. That is basically not a test at all, because you use one period to develop (fit) a strategy and then expect it to work on another period. That's called a curve fit. Curve fits don't work on another curve, unless you want to be a gambler.

To evaluate whether a strategy actually has an edge, you should perform a walk-forward analysis (WFA). The idea is that the strategy has to repeatedly survive unseen market periods after recalibration. That is much harder than simply fitting one long historical sample.

If at least about 10 independent forward out-of-sample cycles remain profitable/stable, then you probably start seeing evidence of real statistical significance, rather than pure historical fitting.

Simple example of a rolling WFA:

You are currently at month 25.

Cycle 1:

• Months 13–15: optimization/recalibration in-sample (IS)
• Months 10–12: backward out-of-sample (OOS) validation
• Months 16–17: forward OOS test

Cycle 2:

• Months 15–17: optimization IS
• Months 12–14: backward OOS validation
• Months 18–19: forward OOS test

And so on.

Much success!

Hey everyone,

I made a list of some of the most important metrics used to evaluate the quality of trading and investing strategies. I tried to make the explanations as simple and short as possible. Let me know if I missed some popular metrics or if anything is unclear.

Sharpe Ratio - Measures how much return a strategy makes compared to how volatile the ride is. A higher Sharpe means the strategy makes better returns for the amount of overall risk and instability it takes.

• Below 1 = weak
• 1–2 = decent
• 2–3 = very good
• Above 3 = excellent

Sortino Ratio - Similar to Sharpe, but only cares about downside volatility (losses). Better for strategies that naturally move around a lot but where downside risk matters most. More useful for trading systems and active strategies.

• Below 1 = weak
• 1–2 = decent
• Above 2 = strong
• Above 3 = excellent

Alpha (CAPM - Capital Asset Pricing Model) - Measures how much return a strategy generates beyond what would be expected from its exposure to the market. In simple terms: it tries to measure the “real edge” or skill of the strategy, not just gains from the market going up. Alpha is usually expressed in %. Very important for both active trading and investing.

For institutional investing:

• 2–3% can already be considered strong
• 5%+ very strong
• 10%+ is extremely rare over long periods

For trading:

• 5–15% decent
• 15–30% strong
• 30%+ very strong / unusual
• 50%+ sustained over long periods -> exceptional and often difficult to believe without verification

Beta - Measures how strongly a strategy or asset moves together with the overall market.

Example: If the stock market goes up 10%

• Beta = 1 - your investment also tends to go up around 10%.
• Beta = 2 -tends to move about twice as much as the market.
• Beta = 0.5 - tends to move only half as much.
• Beta =0 - mostly independent from the market.

t-Statistic (t-Stat) - Measures how likely it is that the results are real and not just luck.

• Below 2 = weak statistical evidence
• Around 2 = statistically significant
• Above 3 = strong evidence
• Above 5 = extremely strong

p-Value - Measures the probability that a strategy’s results happened purely by luck rather than from a real edge.

Example:

• p = 0.05 means there is about a 5% probability the observed results could have happened randomly.
• Above 0.05 = weak evidence
• Below 0.05 = statistically significant
• Below 0.01 = strong statistical evidence

Recovery Factor -Measures how well a strategy recovers after losses or drawdowns.

• Formula: total net profit / maximum drawdown.
• Very useful for trading systems.
• Below 1 = weak
• 1–2 = decent
• 2–4 = strong
• Above 4 = excellent

Calmar Ratio - Measures annual return compared to the maximum drawdown.

• Extremely popular in hedge funds and systematic trading.
• Below 1 = weak
• 1–2 = decent
• 2–3 = strong
• Above 3 = excellent

Profit Factor - Total profits divided by total losses.

• Profit Factor > 1 = profitable.

Expectancy - The average amount you expect to make (or lose) per trade over the long run. This is the mathematical “edge” of the system, but it can be misleading and should be combined with statistical significance metrics like:

• t-Stat
• p-value

Win Rate - Percentage of trades that win. Important, but misleading by itself. A strategy can win 90% of trades and still lose money if the losses are huge.

CAGR (Compound Annual Growth Rate) - The “true” average yearly growth rate after compounding.

Volatility - Measures how wildly returns move up and down.

Value at Risk (VaR) - Estimates the worst loss a strategy is expected to suffer over a certain time period under normal market conditions.

Example:

• “95% monthly VaR = 10%” means that statistically, in 95% of months, the strategy is expected to lose less than 10%.
• But in the remaining 5% of months, losses could be worse.
• Very common in professional risk management and hedge funds.

Time Under Water (TUW) - Measures how long a strategy stays below its previous all-time high.

MAR Ratio - Similar to Recovery factor, but with CAGR, instead of total net return: CAGR / Max drawdown. Very popular for hedge fund evaluation.

• Below 1 = weak
• 1–2 = decent
• Above 2 = strong

Correlation - Measures how similarly two assets or strategies move.

• Low correlation is valuable because combining uncorrelated strategies can reduce portfolio risk. Extremely important in portfolio construction and diversification.
• +1 = move almost identically
• 0 = mostly unrelated
• -1 = move in opposite directions

P.S. If you want to measure some of these metrics for your strategy, let me know. I made a nice instrument for that.

Hey everyone,

I made a list of some of the most important metrics used to evaluate the quality of trading and investing strategies. I tried to make the explanations as simple and short as possible. Let me know if I missed some popular metrics or if anything is unclear.

Sharpe Ratio - Measures how much return a strategy makes compared to how volatile the ride is. A higher Sharpe means the strategy makes better returns for the amount of overall risk and instability it takes.

• Below 1 = weak
• 1–2 = decent
• 2–3 = very good
• Above 3 = excellent

Sortino Ratio - Similar to Sharpe, but only cares about downside volatility (losses). Better for strategies that naturally move around a lot but where downside risk matters most. More useful for trading systems and active strategies.

• Below 1 = weak
• 1–2 = decent
• Above 2 = strong
• Above 3 = excellent

Alpha (CAPM - Capital Asset Pricing Model) - Measures how much return a strategy generates beyond what would be expected from its exposure to the market. In simple terms: it tries to measure the “real edge” or skill of the strategy, not just gains from the market going up. Alpha is usually expressed in %. Very important for both active trading and investing.

For institutional investing:

• 2–3% can already be considered strong
• 5%+ very strong
• 10%+ is extremely rare over long periods

For trading:

• 5–15% decent
• 15–30% strong
• 30%+ very strong / unusual
• 50%+ sustained over long periods -> exceptional and often difficult to believe without verification

Beta - Measures how strongly a strategy or asset moves together with the overall market.

Example: If the stock market goes up 10%

• Beta = 1 - your investment also tends to go up around 10%.
• Beta = 2 -tends to move about twice as much as the market.
• Beta = 0.5 - tends to move only half as much.
• Beta =0 - mostly independent from the market.

t-Statistic (t-Stat) - Measures how likely it is that the results are real and not just luck.

• Below 2 = weak statistical evidence
• Around 2 = statistically significant
• Above 3 = strong evidence
• Above 5 = extremely strong

p-Value - Measures the probability that a strategy’s results happened purely by luck rather than from a real edge.

Example:

• p = 0.05 means there is about a 5% probability the observed results could have happened randomly.
• Above 0.05 = weak evidence
• Below 0.05 = statistically significant
• Below 0.01 = strong statistical evidence

Recovery Factor -Measures how well a strategy recovers after losses or drawdowns.

• Formula: total net profit / maximum drawdown.
• Very useful for trading systems.
• Below 1 = weak
• 1–2 = decent
• 2–4 = strong
• Above 4 = excellent

Calmar Ratio - Measures annual return compared to the maximum drawdown.

• Extremely popular in hedge funds and systematic trading.
• Below 1 = weak
• 1–2 = decent
• 2–3 = strong
• Above 3 = excellent

Profit Factor - Total profits divided by total losses.

• Profit Factor > 1 = profitable.

Expectancy - The average amount you expect to make (or lose) per trade over the long run. This is the mathematical “edge” of the system, but it can be misleading and should be combined with statistical significance metrics like:

• t-Stat
• p-value

Win Rate - Percentage of trades that win. Important, but misleading by itself. A strategy can win 90% of trades and still lose money if the losses are huge.

CAGR (Compound Annual Growth Rate) - The “true” average yearly growth rate after compounding.

Volatility - Measures how wildly returns move up and down.

Value at Risk (VaR) - Estimates the worst loss a strategy is expected to suffer over a certain time period under normal market conditions.

Example:

• “95% monthly VaR = 10%” means that statistically, in 95% of months, the strategy is expected to lose less than 10%.
• But in the remaining 5% of months, losses could be worse.
• Very common in professional risk management and hedge funds.

Time Under Water (TUW) - Measures how long a strategy stays below its previous all-time high.

MAR Ratio - Similar to Recovery factor, but with CAGR, instead of total net return: CAGR / Max drawdown. Very popular for hedge fund evaluation.

• Below 1 = weak
• 1–2 = decent
• Above 2 = strong

Correlation - Measures how similarly two assets or strategies move.

• Low correlation is valuable because combining uncorrelated strategies can reduce portfolio risk. Extremely important in portfolio construction and diversification.
• +1 = move almost identically
• 0 = mostly unrelated
• -1 = move in opposite directions

P.S. If you want to measure some of these metrics for your strategy, let me know. I made a nice instrument for that.

What does the majority do that makes them unprofitable?

Please don’t just say “psychology” or “risk management.” I can’t believe that 99% of traders have a problem with psychology, and I can’t believe that 99% of traders can’t calculate basic position sizing to avoid getting margin-stopped.

I have my own answer to this question, but I’m interested to see how other people think about it.

Hey everyone,

I think the key to profitability is building a strategy with a real edge, validated through statistically significant walk-forward analysis.

I found my edge after backtesting hundreds of strategies and variations. Through that process, I came up with new ideas that helped me create a set of custom-made indicators, which I combined in different ways, optimized, and eventually turned into an edge that led to this result:

https://www.reddit.com/r/algotrading/comments/1sfyfqx/full_year_of_live_trading/

Risk management: position sizing based on the maximum drawdown observed in the backtests.

Disclaimer: I’m speaking only about my own logic and experience as a currency trader. Other workflows for finding an edge may exist. I personally hven’t seen verified profitable accounts using those workflows.

Hey everyone,

do you think large quant firms genuinely have some better models than successful independent quants - or is it just the structural advantages: institutional funding, proprietary datasets, execution quality, lower transaction costs, superior infrastructure, more sophisticated risk managemetn, etc.?

What would you prefer:

1. Profit Factor 3.0, Calmar 1.3
2. Profit Factor 1.3, Calmar 3.0

Hey everyone,

Imagine a casino where you must choose red or black. It's 50/50. No edge. No statistical advantage. It's just random. Luck is your only asset in this game.

Now compare that to trading. You also have two choices:

Strategy #1:

Some online influencer or website told you it works. You never tested it yourself. You have no statistically significant evidence that it actually works.

Strategy #2:

You personally tested it across a statistically significant sample. Different market conditions. And actually you see it's profitable. You can reasonably conclude the edge is real. You can even use the T-Stat score to evaluate your statistical significance professionally. T-Stat >= 2.5-3.0 means you are good to go.

Which one would you choose?

What's interesting is that most traders treat both choices as if they are equal. In the casino example, there is naturally no edge. In trading, statistical significance is what separates gambling from evidence-based decision making. Without statistical significance, you are essentially choosing a strtegy the same way you would choose red or black on a roulette table.

So how do you actually test? You simply test in the most convenient, reliable and quickest way possible. If it's a strategy/market where slippage is generally not a problem (like non news/rollover forex strategies) then backtest is your best option. If it's equities or crypto, then backtest + fully simulated paper trading (the one that has the same slippage as live accounts).

If you were one of 100 people hired by a company, and the employer told you that you wouldn’t be paid for 5 years, and that afterward only 1 out of the 100 workers would get paid, would you still come to work?

Because that’s the fate of a new trader.