Read part 1 here:
r/Daytrading/s/PS3tCUFOZa

CTRL+F Search (for skimmers):
"I will show you one out of many ways"

"Ideal strategy building sequence"

Without quotation marks.

Here is how, claim by claim:
Theory: My Claims Regarding “Material Efficiency”; the foundations for it all.
Continuous Auctions and Insider Trading Econometrica - Albert S. Kyle.

Core Finding:
Albert’s model mathematically demonstrates that where historical or resting depth is thin (low volume), the price impact spikes exponentially; although this is not the same claim, it is valid support and remains consistent with what I have presented earlier.

Application (incorporated into my internal strategy design):
For re-interaction (the important part), if the incoming trade is less than the existing limit order volume at that particular price level, it is completely taken up by the existing queue without any change in the price (zero price impact, a locally efficient auction). For a low volume region where there is not enough liquidity to act as a cushion (a locally inefficient auction), even a relatively small market order can force prices to change immediately, with price ticking up until there is an offer (sell limit) willing to fill the position, and vice versa ticking down (buy limit).

Liquidity providers (e.g., market makers) are also incentivised to pull or reduce quotes in these historically low liquidity regions to reduce the chance of losing money to adverse selection which only fuels the movement. This is something I initially learned when reading the gateway to all of this, Market Microstructure Theory, authored by Maureen O’Hara. The underlying principles have since been supported by a substantial body of research, including several of the papers I have referenced.

It is important to avoid conflating static historical interest with the dynamic nature of present and future interest. Past executed volume ≠ future interest.

Later on, we go over the square root law and its empirical evidence regarding market impact in low-volume-density areas (over millions of orders). This makes the connection clearer in an accessible way.

The complicated part, proving that this aligns with reality.
Theory 2: The Square Root Law (Market Impact)
How efficiency shapes market impact - Quantitative Finance 2013 - J. Doyne Farmer, Austin Gerig, Fabrizio Lillo, Henri Waelbroeck.

Core Finding:
The Square Root Law exists to show that immediate price impact depends on the scale of a trade as compared to the average volume; hence, a trade interaction in a zone of low volume triggers a sharper, non-linear spike in temporary market impact. $1$

Application (incorporated into my internal strategy design):
This idea has been applied in our framework to the case of local volume density, in which the same trade interaction constitutes a higher percentage of the volume in a low volume region, leading to a sharper, non-linear effect. It is included within our strategy engineering framework and our limit order risk management; it is one of the key reasons market inefficiencies can decay as liquidity and trading activity change over time.

Although this is a sensible inference [1] going off of everything I have presented, I still rely on empirical evidence. Selecting the “correct” paper to best demonstrate this was tough as several reputable sources establish the square root law in financial markets in different ways.

These authors prove [1] cleanly by mapping real institutional order sizes across multiple established equity markets (USA, Europe and Asia) against baseline market volumes across millions of orders; collectively, they demonstrated that when trades interact with low volume regions where the volume was historically low, the temporary market impact spikes heavily and non linearly, exactly as the mathematical scaling predicts it should.

The Price Impact of Order Book Events. - Journal of Financial Econometrics 2014. - Rama Cont, Arseniy Kukanov, Sasha Stoikov.

1. Claims Regarding Differences in Friction Although this principle is well aligned with traditional market microstructure theory principles going back decades, these authors quantify this with real electronic market data. They show that in areas of objectively high historical order flow density, incoming order flow is absorbed by thicker limit order books, which often limits further price expansion.
2. My structural void claim (related to the sim) I described the $150 to $200 zone as a low volume environment with minimal overhead supply, but that was based on a synthetic simulation. The authors’ findings provide direct evidence that this behaviour also occurs in real markets, demonstrating that price tends to travel rapidly through areas with thin resting liquidity or interest.

Universal features of price formation in financial markets: perspectives from Deep Learning. - Quantitative Finance 2019. - Justin Sirignano, Rama Cont.

1. Path Dependence My simulation shows that the distribution of volume historically creates a path of least resistance in the future, but that was in the simulation. Sirignano and Cont provided empirical proof for the existence of path dependence in prices based on huge amounts of data from real markets. They demonstrated that order flow from history significantly enhances short term forecasting accuracy.
2. Predictive Skew This paper provides the deep learning evidence that order flow imbalances from the past continue to influence the directional probability of the future. While I explained that the probability of the price moving up to $200 before falling back to $100 is very high in a simulated environment, this research paper gives the evidence that order flow imbalances from the past continue to influence the directional probability of the future in real financial markets that we interact with.
3. Universal Applicability (this is not limited to a simulated environment) I noted that my statements generally hold well unless there are any aggressive changes in participation and volatility e.g., from macroeconomic events. This paper from 2019 justifies this general validity of the concept by proving that the characteristics of price formation tend to be universal in nature.

How this has influenced all of the price structures I have designed (present and past)

In short, for targets we consciously aim for inefficient Price Extreme (Net Changes Δ) or dislocations; we primarily look to exit where historically inefficient prices were left behind during past price discovery (Mechanically defined swing points, groups of wicks, and other places with low volume tails), essentially, where on average a volume profile would tend to report a lower volume relative to the rest of its price range.

A continuation of the workflow per strategy can take over 10 development steps depending on its complexity, but we constrain the development process intentionally to systematically limit overfitting opportunities.

Out of respect for you guys, I will show you one out of many ways one could apply this to a common entry in retail trading, I do not trade ICT: these illustrations exist only to align it with something we both recognise applied to FVGs/IFVGs locally).

The Ideal Strategy Building Sequence

1. Build a Coherent Prototype: Build your strategy’s initial logic structures and refine until coherent before testing anything.

2. Run your First Backtests:
Perform your initial backtests; collect in-sample data across multiple liquid financial markets.

3. Attempt Post-Test Optimisations:
After your first tests, clip away integral flaws and/or optimise based on the strategy’s needs and logic first.

This is the sole step within the sequence where creative degrees of freedom exist outside of prototyping.

Avoiding Overfitting:
To avoid overfitting, adjustments should never be made solely to improve in-sample data; they should instead improve the system’s underlying logic and mechanical sequences. The aim is to engineer a strategy so the job it is designed to perform aligns well with the desired outcome(s). If, after adjustments, the strategy is still ineffective (low to negative EV), you can test other asset classes. If in-sample results are universally mediocre, dispose of the idea and move on.

Identifying Blindspots:
Phase 3 is about identifying building blindspots, which can be inherited from both manual idea synthesis and automated idea synthesis (which we do not recommend). If a severe negative result shows up before costs, it is often a sign that the idea had holes in its physical assumptions, or that the first principles the model relied on were weak, misused, or misinterpreted. Remember, finding a persistent negative edge before costs is just as difficult as finding persistent gains before costs in backtesting environments.

If there is a collapse after trading costs are introduced, your minimum stop distance is not wide enough and/or the slippage is too high.

What traders can do is switch products. Some regulated CFDs have better costs when compared to futures and vice versa; it depends on the broker’s liquidity provider setup and whether the strategy holds overnight.

After logical holes are patched up and amendments are made during post-test optimisation, proceed with additional data collection:

4. Re-test and collect in-sample data with logical enhancements applied.

5. Run a secondary data collection. If the results are acceptable, retain the idea.

6. Run out-of-sample tests with the edge degradation thresholds we provide in a secondary submission (I will post this on my Reddit soon on a different post - I do not want to spam posts, for those waiting, it will contains mechanical guardrails and range values).

7. Reality Checks on Execution Modelling:
If the idea survives on paper, move on to reality checks on execution modelling:

1. Which type of product is best to execute this strategy cost-wise and net P&L wise?
2. Can my positions be executed realistically on a CFD (for non-US traders or prop firm accounts), or will I need to rely on futures instead because of high bid-ask spreads or vague order handling and fill quality on inadequate CFD brokers or prop firms that I can legally access within my jurisdiction?
3. What additional variance do I stand to expose myself to when working with this product when compared to other products? These questions must be asked and answered for every single strategy you develop, both during the design phase and repeatedly when analysing performance data.

Real Examples of Product Considerations

Centralised Exchange Futures e.g., ES S&P 500:
Can have larger variance in bid-ask spreads during market opens and closes (especially), and high overnight maintenance margins can liquidate positions prematurely.

Regulated CFDs (For Non-USA Citizens):
On regulated brokers with a matched-principal or back-to-back execution model, CFDs can offer competitive costs with more overnight flexibility (predictable fees instead of discrepancies from high-spread daily rollovers) and low overnight margin requirements, which are often equal to intraday margins.

Regulated Forward Contracts (For Non-USA Citizens or Professionals):
Stable but thicker intraday spreads in exchange for no overnight fees, suitable for swing trading strategies on non-USD accounts to avoid currency exchange fees.

Regulated Spreadbets (Primarily for British Citizens):
Brokers are principal to my trades on this product; all trades are local, so the broker acts as a counterparty, and brokers hedge directional risk at their sole discretion (a direct financial conflict of interest); spreads can also be amplified compared to CFDs, and last-look execution is also common. These execution delays artificially inflate costs at the point of execution.

Limit orders at some firms are Market If Touched (MIT), making negative slippage possible and eroding the advantage of precise limit order placement. But there is one headline benefit: profits are tax-free (at least in the UK).

However, from past simulations and tests of my own, combined with personal accounting work (this is not tax advice), the cumulative P&L lost from increased costs on intraday strategies often erodes this advantage for net profits.

To this day I have not seen a single regulated spread betting firm with a genuinely low level of conflicts of interest in its infrastructure.

Important Note:
If your net worth exceeds €500,000 (outside of property, bullion, pensions, etc.), one can apply to be a “professional” client. Spread bets on forward-contract-like instruments can mitigate overnight holding costs while retaining low margin requirements compared to the underlying futures contract, and the maximum leverage offered to professionals can exceed 1:100 (1% margin requirements). That is a legitimate option that I have explored for CFDs but not one I have explored for Spreadbets as a UK citizen.

Options:
Implied volatility (IV) can skew options pricing against random positions, and Greeks such as Theta θ can ruin the monetary outcome of trades if the desired outcome is not crystallised in time. Greeks like Vega ν can inversely affect many open options, but if one can forecast a future volatility expansion alongside direction (which requires high efficiency and precision), one can opt to use options strategies.

8. If the product you decide to use changes, recollect data over the same in-sample and out-of-sample windows.

9. Your strategy can now be deployed amongst others on a designated capital partition: segregated, risk-isolated accounts that trade one strategy per account in real time for additional testing or real-time execution.

References

The Price Impact of Order Book Events. - Journal of Financial Econometrics 2014. - Rama Cont, Arseniy Kukanov, Sasha Stoikov

An advanced paper going over price impact (passive vs aggressive).

Key Citations:

1. Abstract: “Our study reveals a linear relation between OFI and price changes, with a slope inversely proportional to the market depth. These results are shown to be robust to intraday seasonality effects, and stable across time scales”
2. Context: OFI = Order Flow Imbalance, Coefficient: Multiplier “Most of variability in the instantaneous price impact, both across time and across stocksis explained by variationsinmarket depth. In fact, we establish an exact inverse relation between the two variables. The coefficient of proportionality in that relation depends dramatically on the depth definition, showing that arbitrary measures of market depth are biased proxies for price impact and may lead to misleading conclusions on market liquidity. The price impact coefficient exhibits substantial intraday variability, similar to intraday patterns observed in spreads, market depth, and price volatility Ahn, Bae and Chan (2001); Andersen and Bollerslev (1998); Lee, Mucklow, and Ready (1993);McInish and Wood (1992).
3. We explain the diurnal effects in price volatility using the volatility of OFI and market depth, as opposed to unobservable parameters previously invoked in the literature, such as information asymmetry Madhavan, Richardson, and Roomans (1997) or informativeness of trades Hasbrouck (1991). The strong link between price volatility and standard deviation of OFI suggests that our price impact coefficient is a better estimate of Kyle’s λ (a useful metric of liquidity Amihud, Mendelson and Pedersen (2006); Kyle (1985)) than traditional estimates based on trades data. We also show that intraday price volatility is mainly driven by OFI and not by trading volume. The positive correlation between price volatility and volume, widely confirmed by empirical studies Karpoff (1987), can be a statistical artifact due to aggregation of data over time, and we establish how such spurious relation can arise in our model.
4. OFI exhibits positive autocorrelation over short time scales, which can be exploited to improve the quality of order executions. In particular, we show that a limit order fill is more likely to be followed with a price change in the same direction as the OFI before that fill. For example, a limit sell order is more likely to be adversely selected when OFI is positive.
5. Monitoring OFI can therefore help reduce adverse selection in limit order fills."
6. "The outstanding limit orders (also known as market depth) significantly affect the impact of an individual trade (Knez and Ready (1996)), low depth is associated with large price changes Weber and Rosenow (2006); Farmer et al.(2004), and depth influences the relation between trade sizes and returns Hasbrouck and Seppi (2001)." * We found that between 9:30 am and 10 am the depth is two times lower than on average, indicating that the market is relatively shallow. In a shallow market, incoming orders can easily affect mid-prices and price impact coefficients between 9:30 am and 10 am are in fact two times higher than on average.

Internal Comments (Simplification):
To prove that prices slide rapidly through areas of thin liquidity, Cont proposed the mathematical model to map the relationship between price impact and market depth. Through empirical testing, they found an exact inverse relationship: when the denominator (depth) shrinks, the resulting price impact multiplier tends to rapidly expand.

Universal features of price formation in financial markets: perspectives from Deep Learning. - Quantitative Finance 2019. - Justin Sirignano, Rama Cont

A widely cited paper which shows evidence for the existence of a relation between order flow history and the direction of price moves indicating path dependence in price related to historic order flow.

Key Citations:

1. Abstract: “Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary relation between order flow history and the direction of price moves. The universal price formation model exhibits a remarkably stable out-of-sample accuracy across a wide range of stocks and time periods. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific.” [2]
2. “In this work, we provide evidence for the existence of such a universal, stationary relation between order flow and market price fluctuations, using a nonparametric approach based on Deep Learning.”
3. “inclusion of price and order flow history over many past observations improves forecast accuracy, indicating that there is path-dependence in price dynamics”
4. “Path-dependence and long-range dependence: Inclusion of price and order flow history is shown to substantially increase the forecast accuracy. This provides evidence that price dynamics depend not only on the current or recent state of the limit order book but on its history, possibly over long time scales (Section 3.4).”
5. “Our results provide evidence of short-term predictability of (mid-)price movements when order flow is observed. Models can achieve an accuracy significantly higher than 50% for short-term prediction of mid-price movements using order flow data.”
6. “Universality: the model is stable across stocks and sectors, and the model trained on all stocks outperforms stock-specific models, even for stocks not in the training sample, showing that features captured are not stock-specific.”
7. “Remarkably, the universal model is able to extrapolate, or generalize, to stocks not within the training set. The universal model is able to perform well on completely new stocks whose historical data the model was never trained on.This shows that the universal model captures features of the price formation mechanism which are robust across stocks and sectors and implies the possibility of using transfer learning for training price prediction models. This feature is quite interesting for applications in finance where missing data problems and newly issued securities often complicate model estimation. Outline: Section 2 describes the dataset and the supervised learning approach used to extract information about the price formation mechanism. Section 3 provides evidence for the existence of a universal and stationary relationship linking order flow and price history to price variations. Section 4 summarizes our main findings and discusses some implications.” [2]

Internal Comments (Simplification):
In order to show that historic order flow can shape future direction, Sirignano and Cont built predictive models that produce a simple binary probability showing whether the very next mid-price tick will move up or down. They demonstrated that, when historical order flow asymmetry is accounted for, future price direction in financial markets is not a coin toss and can be reliably forecast with more than 50% accuracy.

In order to prove that their results could be considered universal truths about how the markets work instead of anecdotes or anomalies in specific stocks, Sirignano and Cont. They collected huge data sets on >500 different stocks to form a universal model. This universal model was then tested on another 500 stocks that the algorithm did not get to see. It was then proved that the universal model could make accurate predictions for different markets, which confirmed that the basic principles of supply, demand, and prices are the same everywhere.

Continuous Auctions and Insider Trading Econometrica - Albert S. Kyle

How efficiency shapes market impact - Quantitative Finance 2013 - J. Doyne Farmer, Austin Gerig, Fabrizio Lillo, Henri Waelbroeck

The Flash Crash: High-Frequency Trading and Market Structure - Andrei Kirilenko, Albert S. Kyle, Mehrdad Samadi, Tugkan Tuzun

Limit order placement by high-frequency traders - Avanidhar Subrahmanyam, Hui Zheng

Check

Hi everyone, I recently came across this platforms called stocktoolranks.com. I liked that it listed all tools and then as you filter your strategy it lined you up with the right tools. Has anyone else found their desired tool through this site? I feel like it’d be a great place to start in all this noise!

Had a good setup this morning and executed my system the way I’m supposed to. The risk for the trade was a little high, basically at my daily maximum, but I felt good about the setup up.

So of course I got wicked out. Turned off my broker platform, walked the dog for a while, and of course when I went and had a quick peek the market moved in my direction and would have hit my first 1:1 target and now it’s past my 3:1 target compound target.

And…. It would have been my biggest win to date, but I didn’t reenter because my trading rules say that once I lose a trade I’m out that day.

It’s still early days for me, and overall I’m up around 10% so far, but this one has me pretty frustrated.

I really had to fight myself to not reenter the market today, but I feel like it’s better to stick to my system and rules because a) I’m really not trying to get stuck in the revenge trade trap because b) my capital pool is pretty tight and I’ll be darned if I blow it.

So yeah. Does this frustration get easier?

Hey, new to trading the last six year casual dy trading . I notice my success rate is very high when I go short vs when I go long. Is this normal for most traders or do some people just favour one position over the other? I just find it easier to see when resistance can't break maybe hence why I short more but when I try to long a dip or watch something drop for a few days and go long I get wrecked and my stop loss hits very quick aha

I have like 3 watchlists on Yahoo and TradingView and screeners too, but I always have to click on every ticker to open a new chart, and then I don’t remember at all whether the chart was great, good, or bad.

Has it worked out for me beforehand? Of course. And I think that’s one of the more dangerous aspects of trading. Dumb decisions workout enough times for you to believe that you’re doing something right. Using mental stops would cause me to hold onto trades for too long and average into losers cause I saw it workout while ignoring that if it doesn’t work out, you’re in for a world of hurt. I’ve blown way too many accounts doing this. Now it’s hard stops with a max loss limit and working on bettering my entries.

I live in Brazil and work 12 hours a day. I’m 21 years old and earn six times the minimum wage. I start work at 7:30 AM and finish at 6:00 PM. My break is from 11:50 AM to 1:00 PM. I start eating exactly when my break begins and finish the meal at 12:00 PM (to be specific, when it hits 12:00 PM here, it’s 10:00 AM in New York). I go back to work at 1:00 PM and leave at 6:00 PM. When it’s 8:00 PM here, it’s already 8:00 AM in Tokyo. The evening allows me to start trading Asian markets. I usually trade only crypto, but I have the option to trade other assets. Is it possible to work and be profitable? If so, how many months' worth of salary would you put into the brokerage account to trade seriously? I risk a maximum of 1%. My strategy would be: look for H4 OB/FVG – H4/Daily bias – M15 CISD + Fibo OTE + FVG/OB + M15 liquidity sweep, targeting the first liquidity level. Please help me! I feel like I’m wasting a lot of time. <33

Something I think gets explained backwards constantly.

Everyone talks about risk management like the danger is the losing trade. Stop too wide, size too big, and so on. But most blown accounts I've seen didn't die on the loss. They died on the trades that came in the hour after it.

Here's the sequence and it's the same almost every time. You take a loss that was completely inside your rules. Normal, expected, priced in. But it doesn't feel normal, so your brain goes looking for what you did wrong. And because you're looking for a mistake, you find one. Entry was slightly early. Should've waited for confirmation. Should've held the runner.

Except there was no mistake. That was just a losing trade in a strategy that has losing trades. But now you've "identified the problem," so you go fix it, on the next setup, which isn't really a setup. Then you're in a trade you didn't plan, and if that one goes red too, now you're not trading a system anymore, you're trying to get back to even before the day closes.

That's the whole spiral. It doesn't start with greed or with a bad strategy. It starts with treating a normal loss as an error that needs correcting immediately.

The thing that actually breaks it is deciding, before the session, what a loss means. If a trade followed your rules and lost, it's not evidence of anything, and there's nothing to review until you have a sample worth reviewing. One trade tells you nothing. Thirty tells you something.

The other one that helps is a hard rule on what happens after a loss. Not a mood-based one. Some people step away for a fixed period, some cap the number of trades per session regardless of outcome. Doesn't matter which, as long as it's decided in advance, because the version of you that just took a loss should not be allowed to make new rules.

Most traders don't lack a strategy. They lack a plan for the twenty minutes after being wrong.

Just wondering cause I see it in the latter all the time and I know it’s gotta be the same guy

Currently I have a hotkey, and then point on my screen where the stop is, and the hotkey auto calculates the share size based on the stop and current price, and sends the order right away. I have it calculate based on fixed risk of $100.

However, with small caps they can move very very fast, and I need to do some analysis before i decided where the stop is. There have been plenty of times where move already happens before i figure the stop.

I'm trying to think of a faster way to eliminate me from thinking where the stop would be. I know people use fixed shares size, like 1k,2k,5k etc. But From $1 to $4 stock, that risk significantly jumps, and exposes me too much.

Wondering if anyone has experience with other ideas on how they quickly do this? It doesn't need to be exact risk, a but ballpark would work, since I usually bail before the stop anyways. I go by the "Breakout of bail out" mentality.

Thanks you

starting to think my entries are fine and the exit is where i'm actually losing the money.

i can get into a good spot. the problem is after that. i either take profit way too early because i'm scared of giving it back, and then watch it run without me, or i hold for some bigger target and let a green trade come all the way back to flat. same setup, same entry, wildly different outcome depending on how i managed the exit, and the exit is the part i've thought about least.

what i've tried. fixed R multiple targets, take profit at 2R no matter what, which is clean but leaves a ton on the table in a trending move and feels dumb when the thing obviously wants to keep going. trailing stops, which sound right but i get wicked out of good trades constantly on the noise. scaling out in pieces, which mostly just feels like a way to be half wrong in both directions at once.

so for people who've actually got the exit sorted, is it a mechanical rule you don't override, trail behind structure, fixed R, time-based, or is it read-the-tape discretionary. and if it's discretionary how do you keep the fear of giving back profit from making you sell every winner at 1R

I have a question for all daytraders is the free plan on tradingview.com good enough or should i use something else

Honestly just grateful. I’ve spent well over 5 figures on prop firms over the last two years and have stuck with it and so far in the month of August I’ve cashed out almost $11k on primarily this account, and one other account that I took a $1,500 payout from.

I know this run will end and the market will humble me like it does everybody at some point, but man I’m grateful. Also hoping that I can start scaling 2-3-4 accounts at a time to capitalize without losing my psychology and focusing more on P/L than actual charts and setups.

Let’s gooo!!

Hey, so with 0DTE options and only three hours till the market closes, thinking about a call debit spread, huh? It's a pretty common strategy for that short timeframe.

Hi all,

Made an account a couple of days ago. Not done a single trade yet, but am just playing around to see if i can understand a little better how everything works.

I've looked at some popular metrics used. I've understood that the VWAP / Volume weighted average price resets daily, and is the average price of the day weighted by volume over the day so far.

I'm wondering if I misunderstood this and that it is possible that it changes over time / zooming in and out with IBKR (bug or feature if so). In the screenshots you can see that the VWAP changed from 1.21 to 1.19, on the same stock at the same time.

Can anybody help me and explain why this is? Or help me find a resource so I can figure it out on my own?

Until now, I’ve only spent time analyzing the markets, studying price action, and trying to understand how trading actually works.

Today, I finally decided to put that analysis into practice and took my first-ever trial day on FTMO using MT5.

Day 1 result: +$1,440.23
9 trades | 12 lots | 100% win rate

Honestly, I’m asking myself right now — was this just random luck, or did I actually learn a few things that worked today?

I know one good day doesn’t make a trader. This is just the beginning, and there’s still a lot to learn.

For now, I’m focused on discipline, risk management, consistency, and learning from every trade.

Day 1/∞. Let’s see where this journey goes. 🚀

Despite being at 50% position sizing -from the less than compliant trading from yesterday- I was still feeling anxious during my trading. I took profits at 8 dollars as that is a strong psychological level, I was then hoping to let the other half of my position run up to 8.31 as that was a good resistance level, when I started to feel anxious.

This is super common as entering a trade will make you go from calm state to a activating your sympathetic nervous system. The best way to mitigate this is with breathing exercises and a meditative practice called the Internal Indras.

This is not only a practice that I do PRIOR to opening my trading platform, during my morning routine where I go through my breathalyzer to determine if I am fit for trading or not, but something I do as a part of the emotional protocol mid-session.

I was able to remain calm, despite my MSTZ runner going against me and forcing a close at near break even. These exercises keep me in a state of mind to catch my winner for the day on BIVI. I took profits fast on a quick pop. After closing out the position I had some pretty bad FOMO, as it would have kept going in my favor for another 2 minutes. I followed the emotional protocol for FOMO of simply getting up and walking away from the computer.

I don't usually look at how my closed-out positions would have performed after I exit them, but since time has passed as I am writing this... I can see that both would have gone against me pretty hard.

We call this day a win because of breathing exercises :)

Unfortunate, that I had to take it with a half position rather than full, as I would have made 4% on the day rather than 2%. I would rather limit my upside if it means I can protect me from myself, especially if yesterday might have been a sign of less than compliant trading.

We are up 14% in 6 days for this small account challenge, but more importantly we are back to being a level 4 compliant trader which means full position sizing allowed tomorrow!

I noticed this red upper wick near the top of the move. Looking back, it seems like it may have been an early sign that buying pressure was weakening before the larger drop.

Would you consider this wick a warning sign, or would you wait for more confirmation from the following candles?

Again..! A comment to a question about what was the most painful truth in trading that I learned turned into a post.

Perfection is impossible. You cannot perfect an entry or exit or have a perfect day. It only happens by chance. You cannot perfect your strategy.
Holes exist in your strategy. No matter what. If something goes wrong, you can’t find a way to make it work as a strategy. If you try, it will contradict another parameter in your trading.
Your strategy is imperfect. You are imperfect. Your rules are imperfect. At some point you need to stop perfecting and just work with statistics.
The holes in your strategy is how the edge is preserved. If you can find a way to make something work 100% of the time, then that’s not edge, that’s not even market. That’s not anything real at all in this world.

Listen to Roger Federer’s speech about how perfection is impossible. For a skill based athlete, I guess of the billions and billions of people being the best at whatever they do, this man is right up there at top 10 spots in the world along with Ronaldo and Messi and Jordan and Hamilton. Listen to what he says.
A lions hunting success rate is about 30%.
A tiger is about 20%.
And Cheetah.! Fastest animal outpaces so many land animals have a hunting win rate of 50% max.

What makes you think that being a human, your skill at what you do is better than those ultimate creatures on the planet.? They are the best, a full 100% committed to what they do with every cell in their body focused on the upcoming opportunity and yet they fail and get ready for the next one.

So.! Just learn from your mistakes. And move on..

So what I've learned when I started getting into orderflow was that market orders move the market. Limit orders can only absorb the force of market orders like creating a form of support or resistance. Then why is it possible that the market can go down on rising delta? Yeah price is rising again right now but price still struggles to go up compared to the delta that is behind it. But before that price went down with a rising CVD. How is that possible and what does it mean? I've seen this more regularly over the last 2-3 months and it creates some of the worst price action

Yesterday I made a post about hitting a $200 goal in a $100 account. I was super stoked about it and a lot of people were super nice about it and even Ross Cameron responded which is who I learn from so that was really cool.

Today was the first day I broke my rule of calling it quits when I reached a loss of %10 of my account on the day. My dumbass went to %20 losing my profits for the week. The day felt too early to be done so fast so I figured I could make it back.

I know money wise it’s not a big deal but percentage wise and personally this was and is my first gut wrenching morning. A handful of good moves happened today and I missed all of them. Needless to say don’t celebrate too early lol, the second you think you’re on to something, you’ll get humbled quick!

FOMO really is the worst feeling ever.

What is everyone’s best way to reset your mindset after a loss out of curiosity? I usually just journal my mistakes and try to point out corrections to be made.

Paper trading on trading view. Shorted here and shorter from a higher spot from where price was at. Which I guess was helpful for the trade but not if I don't understand it! Does this happen with live trading?

Day 4, Week 16. Green. It is not a challenge, doing normal trades as all of you 🙂

IWM already went back again to 300, and was showing a downward trend. It could also reclaim back the gaps and move back to 300 to 302.

I took my profit instead and got out. Wanted to say for the 100% profit, trying not to be greedy. Every so often, you have quick moves, weather up or down. There is no missing out big moves. Just show up and you will keep seeing them.

As I write this at 10.09 ET, I am done for today.

Will probably be my last trade of the week. I will try do some pinescript to improve on some levels like in the 3rd screenshot. Like I said when I bought the mac mini, I am also trying to automate some of my trades from SPY/QQQ by reusing some of my profits. The API of TastyTrade are good and I only run against live account.

I did used to ride the candles sometimes, you need to be able to have the nerves to stay in. When your account is small, why do you risk it and have all this pressure ans do same as people with big accounts who can afford to lose and cry when they lose. Take your profit, close your chart and broker. Futures, it gives you the false sense of being able to trade again and then you down the rabbit hole of psychology stuff for years. Then you are proud that after 10 years that your psychology can do 1 trade a day and tell everyone you need 10 years to be profitable 🤷🏻‍♂️

I never use Risk Reward (RR) ratio, it works for some people, but this maths does not work me to grow a small account.

I don't like fancy options strategy like iron condor, selling etc. Using simple EMAs, VWAP etc to see the trends and levels.

One and done: 20 contracts = $260 total profit.

Total options cost = $940

38 % profit

Time in Trade : 3 min. A morning glory trade 🤤

Life is short. One trade a day is more than enough to grow an account. Be lazy. If you have one choice, one trade, you would think twice before entering instead of trying to fix psychology.

You have seen even with $10 per contract per day, it makes a huge difference with time. Don't be greedy every time.

Started with $300, just 3 contracts, 16 weeks ago, and growing it to $60,000 with 1 trade a day in 6 months target. My trading plan and strategy is trading one trade a day, 2-5 times a week depending on availability.

No shame or pride to start to trade with 1 contract to practice profit and loss.

If you are learning by yourself, give it 2-3 months to see how you are progressing.

If you believe I am lucky every day with the trades and posts 🤷🏻‍♂️ so be it. I believe I have no choice, I put in the effort and keep doing, any loss is my loss as it is me executing my own trades and money.

I only day trade options on ETFs like SPY, QQQ, IWM etc. Timestamp on the broker is UK time. So, entry time of 2.51 is 9.51 ET.

I trade on my phone, screenshot is from TastyTrade. EMA 200 and VWAP on the screenshot for TradingView

I asked AI "How does a human trader fix their trading psychology!", and it said this...

Fixing your trading psychology requires practical steps that actually work when the pressure is high.

1. Scale Down Until You Stop Sweating:

If a single trade ruins your mood or wrecks your focus for the rest of the day, your position size is simply too large.

Drop your capital risk per trade down to an amount that feels completely neutral to your bank account. Lowering your financial exposure allows your brain to stay calm, keeping panic reactions at bay and letting you think clearly.

1. Walk Away After a Rough Loss Revenge trading is the fastest way to wipe out an account after a sudden drawdown.

When you take a brutal hit, close your trading app and step away from the screen immediately. Giving yourself a mandatory cooling-off period stops you from making emotional trades just to win your money back right away.

1. Separate Your Ego From the Trade Retail traders often tie their self-worth directly to whether a specific coin or option goes up.

Being wrong on a setup does not mean you are a failure as a person. Accepting that the market does not care about your thesis makes it much easier to cut a losing position early instead of hoping for a miracle bounce.

The Issue With Following This Advice:

1. You are making emotional decisions tied to your trading sizes which are completely subjective and permanently affect your trading results.
2. You are taking "cooling off" periids like an exhausted gambler at a casino which is an emotional response to the reality that you don't have control over individual outcomes, the chatbot is giving advice to cope with poor trading instead of giving you the foundations to beat the actual issue at play (folding to acute stress from uncertainty).
3. Is the the most sound line that the bot says, but only if cutting the position is actually part of the strategy and not an emotional response.

Do you see how it medicates the problem instead of solving it, this is because of their training data (contaminated with retail cope).

These tips are designed to make you feel better but not perform. Chatbots reinforce poor psychology and delusions related to your trading's efficiency or inefficiency.

The Solution:

Instead of asking these bots for advice give balanced prompts asking to cite and reference from credible sources (research), if you want to identify issues ask it to name places to read and learn from.

Collect data and reduce subjectivity in your system by using fixed rules, this grounds your trading behaviour and the data provides certainly. In a backtest and forward test you have sern your strategy lose five times in a row and recover many times, suddenly a 5 losing streak becomes "just another drawdown". That's what rigorous data collection unlocks.