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Most active traders do not fail simply because they are lazy. They fail because they overfit, build strategies backwards and/or never collect enough back test data. I have been there. I have chased systems and setups which did not make entirely logical sense, maybe intuitive, but not logical to earn the title of being systematic. They also were not suitable to my schedule either so I had difficulty trying to keep up with my trading. Eventually I stopped following noise and started designing and building my strategies from bare bones. Right from the beginning. Proof that this is human written is provided towards the end. The following document will concisely break down step by step (not just rules) regarding what should be done from little trading experience. For a trader with the will and discipline to design a strategy that can take advantage of existing market edges, this is how they should go about designing their strategies. In this document, we walk through the decision tree from the very first node. The goal is to help you build a clear way of thinking about decisions, so you can develop an approach that stays adaptable as market conditions and life responsibilities change. Feel and Adjust Constraints FirstWe must figure out our initial constraints. Doing this will remove a lot of noise from your trading and subsequently will make your life easier. So, choose:
These are chosen as all rule‐building happens within constraints. If you work a day job and trade five‐minute charts, you are probably not able to trade the New York session. If you only trade during the London session, you do not build rules around the Asian session. It really depends on time zones and other factors. Higher time frames like hourly allow for higher versatility. For example, most could realistically execute once per hour if busy, but not every 5 minutes during high-volume hours. Ignoring constraints is why a lot of retail traders go nowhere – they copy others without aligning their system with their actual life. If you are “trading here and there”, then it is adding noise to your results. The more variance in consistency, the worse it is for your bottom line. Selecting One Market and Timeframe (At the start)You cannot experiment with everything. Pick one instrument and one timeframe. For instance, you may choose Dow Jones and the hourly chart. This is because different markets behave differently. Attempting to make a system that works on Nasdaq, Gold, EURUSD, and Dow Jones at once is usually unwise as you may overfit your strategy or it may break. Now, linking back to the previous section, it is hard enough as it is to trade one system on one market in your busy life, let alone multiple systems with multiple markets at different times of the day. It is already not easy to form a system for one market, let alone multiple, and to trade it without mistakes is another obstacle. One market. One behaviour set/trade setup. But if you must, then to run multiple instruments or systems, split the risk amongst them. Note that each one should be good enough such that if you were to isolate the risk, then each would perform well enough on their own. There is no space for mediocrity. Next you need to understand how your chosen market behaves, see [Note 3 and Reading 5] post-reading. Is it mean reverting, close to a random walk, or trending. These following examples must be refined and understood by yourself. This forces you to research and learn. Plenty of articles and books cover this. These examples are not absolute, they serve as a guide. Here they are, intraday examples:
These are not the only markets you should be looking at, as there are many more to consider. In Figure fig:adf_hurst, we show these “price regimes” presented as an extreme for heuristic purposes. A stationary (mean-reverting) series is shown on the top. A persistent (trending) series is on the bottom. Pay attention to the Hurst values. References at the bottom. Hurst-exponent diagnostic illustrating when a market is trending (H>0.5) versus mean-reverting/sideways (H<0.5). The demonstration of ADF and Hurst are for educational purposes only we do not try to detect short-term price regimes and use it to show anticipate them in advance through testing and preparation. ADF and Hurst (Educational Note)Warning: ADF and Hurst can be very easy to misuse as “magic indicators”, and when applied to a chart, they only show what is already happening, which everyone else can see, so there is no edge on their own. When applied to larger datasets (optional and advanced), it can be insightful to know how often a market is typically ranging intraday during certain hours. However, this kind of data mining is only useful if it is falsifiable and backed by something that fundamentally occurs within markets regularly, in other words, a mechanism, for example, consequences of market opens, midday activity, and well-established institutional activity (such as repricing events on metals) as supporting evidence. We do not try to detect short-term price regimes and use them as an entry. Instead, we use testing with an aggressive cost model applied to design strategies intended to perform under specific market regimes, and you will learn simple approaches to do this throughout the material. Note: We recommend that most people avoid this advanced form of testing, as it is very easy to overfit and it is not required (we do not do it). The underlying cause is always the most important. If you believe you have found a specific behaviour, it is important to have a maximum of three real, objective reasons why it would take place. It is not about whether it exists or not, it is about assessing its authenticity. Start Building with Logic, Not ResultsTo clarify, when you are learning, it is okay to look at charts for a while to familiarise yourself with how they look and what the candlesticks show. The key is to avoid falling into the trap of confirmation bias. You should first write an idea down and then test it. Never the other way around. Think about why your idea would work before you know what the full strategy is. Reverse engineer why it would work in the market and condition(s) and cross-examine the logic. How we deal with price in three sentences:
Rely on logic and statistics, not narrative.The basics: OHLC refers to open, high, low, close data, which is typically represented with candlestick bars. Market Regime is referring to market’s state, trending higher, trending lower or ranging. A Micro Auction is a small event where price overextends, then reverts quickly to a more balanced level before continuation or reversal. We aim to get filled on the correction, obtaining a premium price to either go long or short. In institutional microstructure related work, this is often described as a short term “liquidity shock” and recovery. The “shock” in market microstructure is what is inefficient, and the recovery is efficient. We anticipate that inefficient prices will become efficient within the sequence, and we refer to this as rebalancing, as price temporarily finds balance once again, providing superior prices and costs when compared to market orders on bar closes. We aim to take advantage of the established mechanics of modern price discovery and statistical laws regarding stationarity with clear, effective, and uniquely designed strategies Time series inefficiency: A repeatable, statistically testable, non random pattern that should not persist without further interaction after formation in a perfectly efficient market or an opportunity revealed by price to absorb aggressive order flow to provide an edge. Economists do not trade, why listen?You need to understand the causes behind market moves in an objective way so you can build tests you can reproduce. Peer reviewed institutional work from economists will not magically hand you an edge as a trader, but it can give you the insight you need to design your edge(s) or improve the one you already have. Even the abstracts from solid pieces are golden. We are not trying to mimic researchers, we are using the information they have uncovered for us to perform a very different job: trading as efficiently as possible. Do not change your rules as you go along.And most importantly! Never go searching through charts trying to find ideas to test. Start at the drawing board, not the candlesticks. Forget indicators. Forget entries. First you need structure. The following sections address what to make rules about. Trade Time Window (Tied to Constraints)You must define which hours are valid for entering trades, based on when your chosen market has high volume. For Example, 8am to 4pm NY time for US indices. Why? Because you need volatility to reach targets and you need volume at your entries for price to trend in your favour regardless of your system style (reversals, mean reversion or trend trading). Rule example: “I only take trades between 3 pm and 9 pm UK time.” This could be the time you could realistically execute trades so it is the time period you should be exclusively testing. You can mark this with a sessions indicator (e.g., “Sessions on Chart” indicator on TradingView with the 10:00 to 16:00 setting). Risk ManagementDecide what you are risking per trade, as a fixed percentage of account equity (e.g., 3%). In a live environment this value should fit your risk tolerance and goals. Your risk must be planned ahead and adhered to. It may be static or dynamic. There are advanced methods for this, but for now focus on simplicity. For prop firms, calculate your risk to comply with maximum drawdown rules. Normal example: if a system can suffer ten consecutive losses (this would be classed as -10R, where R stands for risk. $10R = 10 × risk in percentage$) and the prop firm allows up to 10% drawdown, you might trade (as a random example) 0.8% per trade to allow space for peak‐to‐trough drawdown plus a buffer (around 20% extra for instance. This is extra space for slippage, human error and general strategy instability). Again, much more advanced methods exist for these calculations. Dynamic example: Aggressive traders may opt in to back tested rules to increase risk when holding on profitable running positions. For instance, when entering another position on another rejection (scaling in), having pre-defined plans to increase risk during winning, or losing periods in live environments depending on their risk tolerance and goals. Decide your risk‐to‐reward ratio (RRR) before testing (e.g., 1:2, 1:5, etc.). Do not adjust it to chase better performance. It must based on logic. You must also be aware of your trading costs etc, we explore this further in Strategy Design Volume 2. Rule example: ‘‘I aim for a 4 to 5 RRR on reversal trades” or ‘‘I aim for a 3 to 4 RRR on continuation trades”. If the system does not work, I throw it out. Added annotation for clarity, see [Note 1] post-reading. Entry Style (Define Setup Type)Bar replay back-test only. Never scroll backward to “check” the setup again. Pick something linear and logical. Mean reversion? Reversals? Continuations? Breakouts? Then ask:
Order flow is not a system or strategy like educators teach. It is the basics of how markets move on a tick-by-tick basis. Basic example explanation: If there is a buyer at $10,000.25 who wants 100 units and only 80 are available, then price moves up one tick to $10,000.50 to fill the rest. As an example, consider the following:
{ A simplified order book example.} A buyer submits a market order for 100 units. 80 units fill at $10,000.25 and 20 units (the rest) fill at $10,000.50. Volume-weighted average fill price: 10000.25 × (80/100) + 10000.50 × (20/100) = 10000.30. Hence the average fill is $10,000.30 and the last traded price now stands at $10,000.50. This is liquidity. The only reason price moves is that there is an imbalance between the buy and sell volume relative to what is available on the order book. Nothing else. Note that a tick is the minimum price movement on an instrument. That is why markets have a highly random nature, see Fig.~(fig:bonus2). For example purposes only, see Fig.~(fig:chart): 3-wick reversal, **“I place limit orders at the beginning wick of a 2-wick consecutive rejection if it forms and closes during my valid trading hours.” On wick 3 – Sell limit filled, limit order pulled/expired if no fill on bar 3.** Definitions: Price delta (what we prioritise): net change in price. If price has increased by 100, the price delta is +100, and vice versa. Volume delta: the net difference between buy volume and sell volume within a specific time slot (as classified by the data feed, e.g., trades executed at the ask versus the bid). For example, if a candlestick has a volume delta of -1000, sell volume exceeded buy volume by 1000 for that single bar, meaning sellers dominated the volume in that time slot. There can be negative volume delta while price still rises, making it less important for our style. order flow mechanics illustration with a three-wick set-up as an example. A short example applying order-flow mechanics knowledge. A wick high in a candle is rejected by the next candle, and it closes away from the highs. In this time slot (a single bar) on a liquid market, it is more likely than not that there was more sell liquidity present at that wick than the number of people willing to buy at market (more liquidity was provided by sellers than taken by buyers), showing buyer absorption and/or follow-through selling, multiple times. This is evidence that higher prices were not accepted within that time slot (timeframe) on the chart. It does not mean there was more sell volume relative to buy volume (negative volume delta). It means that more liquidity was provided than buyers could take, so prices failed to move higher, and selling is what followed (Negative price delta – What we profit from when short in this scenario). To simplify, regardless of how the “order flow” took place, in the short term selling pressure was in excess relative to what buyers were willing to take, or the liquidity provided to buyers via limit orders was in excess. Think of this like a wall that buyers failed to breach (absorption). Price tried to trade higher, but it failed to as seen in Figure fig:chart. If price revisits that price or higher and fails again, closing. I want to sell at that price while anticipating a third rejection with the aim to absorb aggressive buyers. Long-biased examples of how one of our custom entry techniques (3 wicks) can provide a short-term edge: Why does it work and What is required? All it takes is for a wall of liquidity (sell limits and ghost liquidity) to hold it when buyers attempt to lift price, which can create a short-term reversal. If those buyers lose momentum and stop pressing higher, it is typically reflected in price $↓$. By positioning ahead of time with a limit order near the suspected absorption zone, we can attempt to fade the move if the rejection plays out. It can also be used as confirmation (first) in conjunction with another setup. e.g., 3 wicks (success) followed by a different setup when price has traded lower (different entry technique or a different time-frame for mechanical execution). Defining ghost liquidity: Ghost liquidity is what we name private instructions to buy or sell in advance, set locally (within trading software or algorithms, not on the exchange). These methods are used by larger market participants to hide their intent to buy or sell in advance by placing pending market orders and pending limit orders to fill large trades without placing limit orders on the public limit order book for everyone to see in advance (because that may influence other people’s decisions or reactions). Example 1: Standard Practice (basic example) Price is at 20000 a larger participant want to sell 50 millon worth of an instrument at 20020 instead of placing a sell limit worth 100 million for everyone to see he may place a pending limit order so when the price reaches 20020 (best ask) he can automatically start placing multiple rounds of 5 million USD order sizes to get filled refreshing each time aggressive buyers sell into it offloading his position with lower market impact. This is called iceberging. Why does he do this? It reduces slippage improving his cost basis. In plain terms it makes the price he gets out at more favorable reducing the costs of him trading. This is called iceberging. Example 2: Aggressive Price is set at 15000 and a large market participant wants to sell modest but high size let us say 10 million at 15050 he places a pending market order sell (emulates a sell limit without it being visible on the order book) this pending trigger allows him to start closing out his position as soon as the ask price hits 15050. Why does he do this? His main concern is not the type of execution he gets it is that he gets out of the trade at a point set in advance without anyone knowing until the order is triggered. **Consider this section as the surface of the Why part of our reasoning. ** Model Variations 3WCT (3 Wick Counter Trend) In a market that is overextended (defined with other filters, price structure based (price action) or indicator (math) based), 3WCT can show an imbalance that may show buyer exhaustion, which can lead to a shift in direction if buyers fail to present themselves. This provides a small edge when combined with a good filter, market, timeframe, and regime. Each individual component adds to the edge. 3WTT (3 Wick Trend Trading) In a long-biased scenario, in a trending market, when a small rebalancing event occurs in price (a pullback), if three wicks form on the low, it can show that active attempts by sellers to trade lower have failed. This not only offers a discounted buying opportunity if you position yourself at the potential point of absorption with a buy limit, but it also supports the underlying strength, showing there are still willing buyers within the auction. Consider this section as the surface of the what part of our reasoning. Isolated Application Explained (short positioning) Sell limit order fill, Bracketed with SL and TP (values known before the close), vice versa for long setups. Trading can be this simple. The focus should be on genuine market mechanics (microstructure), not narratives. Basis is everything.Most people who over-complicate with “smart money” or “institutional” talk are waffling. { ** “If you are using charts to execute, you are not smart money, but you do not have to be dumb money either.” **} Dismiss educator narratives on why their methods supposedly work and use critical thinking by applying order flow mechanic basics to accept or dismiss trading entry ideas. Do not sleep walk into the “institutional” narrative fallacies educators sell you. Think about why price moves on a tick by tick basis and what the candlesticks you are basing your entry off actually indicate. Markets are not driven by patterns, they are ruled by imbalances between liquidity offered (passive) and liquidity taken (aggressive). Without it, price cannot move. If a setup does not have logic like this backing up why it would succeed enough for it to be profitable besides having random luck, you are wasting your time. If your only answer to “why does it work?” is “my back-test says so”, then you are doomed.I have asked a trader why he believes his system works besides his data and silence followed for minutes whilst he tried thinking of what to say. I shown him random OHLC candlesticks with his strategy applied and he thrown in the towel. Do not be like this. Examples of what not to base your system on:
These methods are extremely random with weak foundations or are purposefully hard to test accurately and honestly without overfitting. What is unfalsifiable is of no use to us. Educators push these techniques or similar for plausible deniability when systems do not perform. A bad strategy/model is hard to hold to account if there are 1000 ways to trade it. The use of multi timeframe analysis in trading is fine as long as it is not convoluted, has clear rules, and is tested rigorously. Target and Stop Loss PlacementTargets must be placed consistently. Targets are much more important than stops. Entries are more important than targets. Why? Because a strategy is designed to win, in short, it is designed to hit the target, not cushion the stop loss. This is regardless of the win rate that your profitable systems have. The better your entry is on average, the larger the RRR you can exploit the market for. The better your target, the longer you can push average positions (if take profits/targets are used). Stops are solely for risk management to automatically close positions when trades do not work out. Your aim is to make multiples of the stop-loss size per profitable position. If using price structures e.g., support and resistance (S/R), then define the logic first, then the rules. S/R was an example only, we do not use it. For instance, someone could use swing highs/lows, S/R, clustered wicks (over 3+ bars) or rejection zones. With fixed rules to define and mark them in advance. Price will naturally attract volume at these levels, even if the instrument’s order book volume does not reflect it in real time. Ghost limit orders exist, pending stop orders and order fill algorithms trigger from the countless market participants for different reasons. It does not matter what happens when price interacts with these places. It is just more often than not that they are liquid areas. Avoid fixed-distance targets and stops – market volatility is dynamic. For example, a “100 dollar fixed target size” or a “20 dollar fixed stop size” is not going to work. It is better to use dynamic yet consistent targeting methods. A trader must define fixed rules for determining what constitutes a valid target and what does not. A dynamic target, for example, could be 110 points for one trade, 160 points for the second, and 140 points for the next—all placed at predefined rule-based levels (examples are provided in Volume 2). Fixed targets overfit strategies easily. As stated earlier, your execution costs must be factored into your system. For instance, if you use a 1:5 RRR, a 100 point target minimum, minimum stop size of 20 points, and if your max spread on your CFD is around 2 points, that is a 10.9% cost per trade. Rule example: “My target is always greater or equal to 100 points on Dow. Stop is one-fifth of target.” Why? Because it keeps costs at a modest level. Instrument-Specific RulesAgain, some markets behave uniquely. You may use existing research (find journals with related articles, a lot of this is defined more in quant related journals such as JFQA: Journal of Financial and Quantitative Analysis) rather than using deep statistics on your own.
Entry Model influence Examples: Example 1: If you want mean reversion or early trend entries, Dow is a better choice than Nasdaq. (It is more probable for Dow to reverse for intraday) Example 2: If you want to press trades or let positions run, Nasdaq is a better choice than Dow. This is because trends are more pronounced on Nasdaq compared to Dow for intraday. Either can have a trend or mean reversion model, but different strategies will tend to work better if aligned with the instrument’s nature. Strategy Risk Management Setup Influence Examples: Example 1: If you have a strategy idea that includes rules to manually trail your stop loss in profit or uses large targets relative to stop size, Nasdaq would likely be a better choice compared to Dow. (Nasdaq trends more during intraday which compliments this idea; Dow tends to mean revert, reducing the potential for home run trades.) Example 2: If you have a mean reversion strategy idea with a hard take profit and stop loss as risk management (most common), the Dow would likely be a better choice, as its intraday trends are less pronounced compared to the Nasdaq. Either market can have trend and/or mean reversion characteristics, but different entry and risk management strategies will tend to work better if aligned with the instrument’s nature. These guidelines are not absolutes.Note: Trending means larger price extensions. Mean reversion means higher likelihood of returning to the average price. Start From Blank ChartsInstead of top-down start bottom up. People look at charts for ideas when you need to consult logic for inspiration, not recency biases from recent price action, see [Note 3] post-reading. Back testing is there to put an idea to the test. Before building rules based on the chart, define a hypothesis. For example, “What if I traded Dow Jones reversals using 3-wick setups with a 5 RRR limit order entry?” Then test this on the charts. You are not trying to make it “fit”, you do it to ask yourself:
Only then can you write the rules to test. Write Rules as If You Are Giving Them to a MachineYour rules must be:
For example, if you risk $100 and your RRR is 1:5, but after adding spread, average slippages, and other costs, then your new effective RRR after accounting for costs becomes 1:4 which means you only make $400 per winning trade but always lose around $100 before slippage. $≈$ 20% costs. The following are some examples of bad and good rules. Bad Rule: “If the market is ranging, I do not trade.” There is no way to identify a range nor can you define it exactly. Good Rule: “If a 3-wick setup forms between 3–9pm GMT time, and the high/low of setup is beyond/below my filter, I will place sell-limit at the top wick or buy-limit at the low wick.” This rule is not based on intuition and is intuition-free. It is systematic. Define everything clearly – conditions, the logic, the filter, etc and why. { the filter, logic, } Stress-Test the System by Breaking ItOnce rules are written, test them brutally. Ask yourself: Is this rule based on logic or emotional comfort? Be emotionally detached (e.g., break even or partial profits may reduce a strategies net profit – so why use them?). Partials or break-even reduce strategy expectancy more often than not. Does it work over 3+ months of data (The length of the backtest depends on time frame). Log the data and process it in the backtesting sheet For instance, each day has a number of losses and wins, and you can aggregate them by writing them like so: -1R+4R-1R-1R, in each cell. Essentially, just write all of your data down neatly so you can analyse it later; see Fig.~(fig:ssbt). Spreadsheet filled out with each trading days losses and wins to be used for further analysis. What if market conditions flip? Test on conditions against the system’s nature. Test mean reversion and reversal systems on trending weeks. If you are using trend trading systems, then test them on mean reverting/ranging weeks. See your system struggle. An extremely basic test is shown in Fig.~(fig:file). For example, August 8th to September 13th, 2024, on mean reversion systems for YM/Dow Jones is a good place to stress test due to the relentless intraday trends exhibited. What if trading costs rise 20%? Then the size of profits reduces by around 20%. Consider that after the initial rejection candle close, if there is an additional rejection, should I scale in/increase the risk on the trade? The second entry typically has a higher win rate as compared to the first when scaling in for my systems, for example. Testing will confirm whether it is worth doing. Scaling in is only worth doing if the win rate of the second entry is superior to that of the first. For example, a 45% win rate second entry versus a 40% win rate for the first. Most systems do not benefit largely from it, so be careful. Note: an entry is an individual trade execution. Each entry has 1R risk. Two entries would have a risk of 2R, so for 3% risk that gives 6% total risk. Furthermore, ask yourself:
You are not seeking perfection, you are seeking robustness. If a small change breaks your system, it is most likely due to over fitting. Types Of Trading
Discretionary trading is an approach where an individual trader makes decisions to buy, sell, or hold assets based on their personal judgement, analysis, and experience in real-time, rather than following pre-set rules. Discretionary trading causes decision fatigue and emotional strain for most trades, which often produces poor results.
A rule-based method for making trading decisions, using pre-set criteria to produce buy or sell setups, manage risk, and execute trades. The primary goal is to remove emotion and human bias from the process, leading to more consistent and objective trading. Mechanical trading causes less emotional strain and little to no intuitive decisions. This is what we prefer. Why do we have a preference for mechanical, aside from the psychological benefits? Discretionary trading is viable but cannot be measured accurately, so I have never been seriously interested in including intuition in short-term trading.
Most discretionary trading strategies rely on intuition; If two traders use the same strategy, they are likely to achieve different results. One trader could make money, while the other could lose money, making the ‘effectiveness’ subjective and open to interpretation; every trader’s brain is different. **Mechanical trading strategies have fixed preset rules that are planned ahead; ** If two traders followed the exact same rules, they would get the same trade setups, targets, and stop losses, resulting in similar outcomes, making the effectiveness objective if the testing data were of high quality. Bonus tip: When in Doubt, Zoom OutAsk yourself: Does this decision happen on every trade? If yes, write a rule. If not, STOP, think, and evaluate the logic. You should:
Extremely basic test. Old testing data shown from 2022. No edge is possible on this chart, see Fig.~(fig:bonus2). It is 100% a random walk and is eerily very similar to a real market. I am not saying the market is efficient. I am saying it is very close. Therefore, you need to be refined in your approach, you need to be accurate, you need to be systematic and calculated. Completely random-walk chart example. No edge exists here. StructureStructure before everything. Logic before data. Consistency before optimisation. Logic $->$ Rules $->$ Data $->$ Optimisation (idea-driven, not driven by curve fitting). Always ask “why” before “what”. Every rule is based on:
The PurposeCreating ideas based on sound logic is not just to avoid over-optimising systems that will not perform well in real time or forward tests. It is about using the logic of established market truths within your trading, if your idea/theory is just as sound, it should perform well, testing is used to verify. This is the ideal mindset before testing. “This makes sense; my idea(s) should work in one of these markets.” Remember, this is before you approach a chart for testing; this is in the development and thinking phase. Ideas should be tested on a couple of instruments at most that align well with the idea to see if the approach I am developing is effective post-development. For example testing GC and HG (Gold and Copper) if the idea(s) apply to metals in general or a specific established behaviour present in an individual market e.g., strong trends with a logical underlying cause. Before we explore the consequences of deviation we must go over the general principles of deductive and inductive reasoning and its importance in strategy development.
Deductive reasoning starts with general rules or assumptions and works down to specific, testable outcomes. When you are developing your own trading strategy, this looks like taking a well established falsifiable market theories (for example, “mean reversion tends to occur after extreme deviations” – Supply and Demand related.) and using that as foundations to turn it into concrete entry, exit, and risk rules. And only after that we check whether the strategy behaves as the theory predicts when you run it through testing such as backtests and forward testing to confirm its efficiency.
Inductive reasoning starts with observations in data and works up to a general rule or hypothesis. In trading strategy development, this is looking at historical price data, noticing a repeatable behaviour (for example, certain strong trending, mean reversion or reversals around specific conditions), then generalising it into a strategy idea that you formalise into rules and test, knowing that the pattern might weaken or disappear out of sample (this is typical, which is why testing is important). This is why we prioritise deductive reasoning paths to find trading edges, as this protects us from confirmation bias and overfitting. Before taking any of these discoveries we pin it to something real, if there is no reason for why it happens e.g., market opens, indirect but consistent market activity underpinned by something that fundamentally happens. Common Consequences Of DeviatingOverfitted Systems: Overfitted strategies perform well on historical data but do not hold up in forward walks (forward test and live trading) ¹. They work well on the backtest but blow up live Overoptimisation: Overoptimised strategies have a long, sometimes convoluted sequence to get the valid trade. For example, data snooping across multiple time frames (using multiple time frames inconsistently) or using multiple different confluences but never the same exact ones and sequence for every setup. This leads to low sample sizes, which can create an illusion of efficiency over long time horizons. This is a data issue, but in real time the system can be extremely sensitive to market changes depending on how it is designed. Optimisations like confluences should only be taken into account as a secondary step. Optimisation of a tested trading idea should only happen to something that already has an edge when isolated, and that should be done with rigour to avoid curve fitting. Your job is to test ideas that tend to work. The second you try to make something work is the second you start ruining your strategy’s quality. Strategy engineering should as close to a deductive process as possible in the beginning that is built upon with inductive input selectively later. submitted by /u/TheMarketAristocrat to r/Daytrading |