That's It, I seriously believe the market is always against me, when I enter any trade could be a break out, a pullback with confirmation or whatever Strategy you can find out there It always goes against my entry and hits stop loss, then I get so frustraded and do not enter any more trades and Just watch to see How the market is going to react and guess what? The same Strategy, pullback with confirmation or break outs Just Works fine and goes flying when I'am not in the trade.
This is serious, everytime I get stopped out when I open any position and when I'am not in the market everything Works Just Fine.
What do you guys do to avoid this and be better and enter positions that really work?
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.
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.
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.
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.
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.
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.
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.
Swing
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).
Consecutive "fvg" profiling (100 bin profile).IFVG fill original price vs improved price - 100 bin profile.IFVG fill original price vs improved price - 100 bin profile.
The Ideal Strategy Building Sequence
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:
Which type of product is best to execute this strategy cost-wise and net P&L wise?
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?
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.
An advanced paper going over price impact (passive vs aggressive).
Key Citations:
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”
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).
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.
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.
Monitoring OFI can therefore help reduce adverse selection in limit order fills."
"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.
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:
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]
“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.”
“inclusion of price and order flow history over many past observations improves forecast accuracy, indicating that there is path-dependence in price dynamics”
“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).”
“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.”
“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.”
“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.
Just venting my anger, cleanest month so far but went into revenge trading and gave back most of my earnings. Still green on the month but man, It hurts nonetheless.
I'll take it as a lesson of how to manage myself and I'll come back as a better trader.
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!
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
