Day Trading Strategy Optimization for Robust Results

Okay, below you will find ChatGPT giving the best model of my strategy. It was a bitch because I had to constantly have it readjust the steps and explained thoughts. I literally spent six hours doing this with how much I had to explain why, the scales, rating system, and trying to set this up so that you could use it yourself if you wish.

This is MY formula, if you attempt to use it, you are using it on your own trial. Please do not hold me accountable for your decision to use it or deviate from it if you do. Pictures can be used of all the data and asked to be used for data.

Edit: Just noticed a small thing it changed on me that I didn’t notice in step 7 and Step 9. Changing profit lock capacity and capital allocation numbers.

Optimized Trading Strategy Formula

This is a fully structured data-driven approach that maximizes market analysis, technical indicators, options flow, and historical trends to determine the best option positions before market open.

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📌 Step 1: Market Sentiment Score (MS)

We analyze the macroeconomic sentiment to determine overall market bias.

MS = (USM + GM + PM) / 30

Where: • USM = U.S. Market Rating (1-10) • GM = Global Market Response (1-10) • PM = Pre-Market U.S. Response (1-10)

✅ If MS ≥ 0.50 → Favor Calls ❌ If MS < 0.50 → Favor Puts

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📌 Step 2: Previous Day’s Market Performance Score (MPF)

MPF = +0.05, if SPY closed > +1.5% (strong bullish momentum)
-0.05, if SPY closed < -1.5% (strong bearish momentum)
0, if SPY closed between -1.5% and +1.5%

✅ Incorporates previous market momentum.

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📌 Step 3: Technical Analysis Score (TAS)

TAS = (VW + RSI + SMA + MACD + VOL) / 50

Where: • VW = VWAP Rating (1-10) • RSI = RSI Rating (1-10) • SMA = SMA Rating (1-10) • MACD = MACD Rating (1-10) • VOL = Volume Rating (1-10)

✅ If TAS > 0.50 → Favor Calls ❌ If TAS < 0.50 → Favor Puts

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📌 Step 4: Options Market Analysis Score (OMA)

OMA = (PC + IV + V + T + D + G + HV) / 7

Where: • PC = Put/Call Ratio • IV = Implied Volatility • V = Vega • T = Theta • D = Delta • G = Gamma • HV = Historical Volatility

✅ If OMA > 50% → Favor Calls ❌ If OMA < 50% → Favor Puts

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📌 Step 5: Historical Market Data Analysis (HDA)

HDA = (Similar Market Day Trends + Overnight Gaps + Earnings/Fed Impact) / 3

✅ If HDA confirms current market setup → Strengthens bias ❌ If HDA contradicts → Adjust bias accordingly

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📌 Step 6: Final Market Direction (FMD)

FMD = (MS + MPF + TAS + OMA + HDA) / 5

✅ If FMD ≥ 0.50 → Buy Calls ❌ If FMD < 0.50 → Buy Puts

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📌 Step 7: Strike Selection & Position Sizing

Strike Selection

Strike Price = SPY ± (2, 3, 4, 5, 6)

Capital Allocation

Initial Position = 30% of Capital
Remaining Capital = 70% (For averaging down, hedging, or taking new trades) If you take a loss for the day, greater than 5-10%, next day you only use 80% of total funds. Initial Position = 20% Remaining = 80%

Explaining: If you have $10,000, and you lose $1000, you have $9000. Next day your max positions are based off $7,200 as max allowed in the capital allocation.

Repeat for consecutive losses.

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📌 Step 8: Historical & Pre-Market Adjustment

🔹 Run this formula 5-10 minutes before market open. 🔹 Assess option pricing, ETF pre-market data, and SPY chart data. 🔹 Determine the 6 most optimal option buys.

✅ This ensures we react to overnight gaps, macro data, and pre-market sentiment.

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📌 Step 9: Trailing Stop & Profit Management

Initial Stop-Loss = 20% - 25% trailing
Breakeven Adjustment = At +30% profit, stop adjusted to minimum 5% break-even.

Profit-Locking Strategy:

At +40% profit, stop tightened to 15%
At +70% profit, scale out of trade

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📌 FINAL FORMULA

FMD = (MS + MPF + TAS + OMA + HDA) / 5

✅ If FMD ≥ 0.50 → Buy Calls ❌ If FMD < 0.50 → Buy Puts

Strike Selection = SPY ± (2, 3, 4, 5, 6) OTM
Position Size = 40% Initial, 60% available for adjustments
Trailing Stop = 20% - 25% initially, adjusted as profit increases

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📊 Strategy Evaluation

✔ Strengths

✅ Incorporates historical trends to optimize accuracy ✅ Uses live pre-market data for real-time adjustments ✅ Accounts for market sentiment, technicals, and options flow ✅ Risk management ensures controlled losses & locked-in profits ✅ Dynamic and flexible for daily trading scenarios

⚠ Weaknesses

❌ Unexpected macroeconomic events could disrupt signals ❌ If SPY gaps too much overnight, ideal entry points are lost ❌ High IV can lead to significant price swings, requiring discipline in execution

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📈 Profitability Expectation • Win Rate Expectation: 70% - 85% (assuming disciplined execution) • Risk-Adjusted Profitability: Expected 5%-20% gains per trade • Max Drawdown Risk: If not stopped properly, losses could reach -20% per trade

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🚀 Final Takeaway

This is a highly structured, probability-based trading model that balances market sentiment, technicals, options flow, and historical data for optimal trade execution.

📌 Next Steps:

✔ Run this model 5-10 minutes before open daily. ✔ Incorporate pre-market options pricing to refine entry. ✔ Use historical performance tracking to fine-tune win rate.

This approach is scalable & adaptable for long-term profitability!

Caught this trade this morning during NY session (with the strategy I mastered after 4 years of daytrading: weekly range range (from monday high/low) + structure)), sharing the setup with yall hopefully it’ll help things click into place for you

(1-2) red box is the weekly range I draw on/from monday high/low

Price stayed in range all week (bounce up from the low, dip from the high)

(3-4-5) uptrend market structure after bouncing from low provided buy entries which I took for a nice 1:5 RR / 25 pips profit and done for the day after trading for less than an hour ready to enjoy my friday lol

I recently discovered an extremely predictable strategy that has thus far not yielded me a losing trade. This strategy was developed to exploit specific forced market mechanics that effectively put extreme sell pressure on stocks during specific time windows.

This strategy is the convertible note strategy. It goes like this:

1) Company issues a press release announcing a convertible note issuance.

2) Go and check the filing. There will be an exhibit 99.1 as an attachment. Read this, and look for a pricing window (if not already price) This pricing window is generally a VWAP during a small timespan on the next trading day. If the filing is release in the pre-market, it will be that same day. Here is the recent filing from MARA on Wednesday. It mentions 2pm through 4pm EST.

3) Open a PUT contract (short duration is riskier but reward is insane) shortly before the pricing window starts. I would suggest like 1-2 hours prior. If you open one in the morning, the price will likely bounce around a bit before declining into the window. The only thing that matters for the pricing here is the VWAP during the window.

4) Sell the PUT shortly after the pricing window starts. Often, stocks will flatline. Here is another example of the exact same thing. Every time I have seen this happen, price action is almost the exact same, and I will explain why.

This price action isn't due to normal bullish/bearish mechanics, or even shares actually being sold into the market. It is due to institutional bond hedging. When an institution buys the bonds, or intends to buy the bonds, they hedge their positions... by selling/shorting the underlying stock. This is a mechanical process that happens every single time a bond is issued.

Sometimes convertible note announcements are pre-priced and the note selling takes place the next trading day. What is the plan then?

The plan is the same. As the bonds get sold to qualified institutional buyers, these institutions short the underlying to hedge the position, and generally these institutions are allowed to short naked. Here is ASTS, which happened today. Due to the convertible note selling, there was excess sell pressure on the stock. Even though the stock is in a bullish pattern on the daily, the sell pressure from the hedging today overwhelmed the buy pressure.

While this strategy isn't an every day occurrence since companies don't release these kinds of filings all the time, it is definitely something to keep in the toolkit since it can yield 100%+ returns consistently if done correctly. I personally generally paper hand out when I get a minimum of 20% gain since that is still a big win for me.

This strategy doesn't use chart patterns, TA, or anything... it exploits forced institutional hedging mechanics, which yield predictable and repeatable chart patterns.

Part-time trading, from initial multiple account blowouts to now consistently profitable for many years. This isn't bragging, but sharing the lessons learned from years of losses.

1. Overleveraging: Profits can be quick, but losses can be fatal. Once you're wiped out, you have to start all over again. Therefore, position management is crucial.

2. Not setting stop-loss orders and stubbornly waiting for a rebound: A small loss can grow into a large one, and a large loss can lead to a wipeout. Therefore, you must cut your losses promptly when the trend changes.

3. Too many trades mean more opportunities to give money away to the market. Never make uncertain trades. I currently make 3-4 trades per week, sometimes none. Fewer trades mean more consistent results. Improving your win rate is crucial.

4. Emotional trading: Wanting to win back losses leads to doubling down. Feeling invincible after winning often results in losing everything. Strict trading discipline is essential.

5. Chasing highs and lows, ignoring trends: Repeatedly entering and exiting the market, buying on every dip. The market has no top or bottom, only ups and downs. Follow the trend and optimize your risk-reward ratio (win more, lose less).

Finally, I've fallen into all these traps. I've had margin calls, lost money, and questioned my life choices. My eventual stable profitability isn't due to genius, but simply learning risk management, controlling losses, and pursuing profits while preserving capital. I won't elaborate further on strategies and trading mindset for now. If you're interested, we can discuss these topics together. Feel free to ask questions. Best of luck to everyone!

Some background on me… I spent about 17 years in quant, mostly as a researcher / quant dev. My academic background is computer science, and at some point I picked up a CFA because when I first started I didn’t know anything about finance. Most of my career was institutional stuff… long horizons, low turnover, low tracking error portfolios. More enhanced indexing than pure alpha.

Now that I’m out and no longer need pre-clearance from compliance to trade stocks, I started looking at what retail traders are doing on the systematic side. I kept running into things like SMC and ICT. To me it felt like technical analysis with fancier names. That said, some people here do seem to make money with it, so I wanted to see whether there’s any real signal there or if it’s mostly data mining.

So I built a backtesting platform around backtesting.py. To get breadth quickly, I used an LLM to help translate a lot of these qualitative SMC/ICT “rules” into Python. It generated ~80 strategy variants… liquidity sweeps, FVGs, order blocks, ORB, Fibonacci retracements, etc. To be honest, I don’t fully understand half of them and I’m skeptical of most of it, but the goal was to test, not believe.

Once I had the strategies, I pulled an API I found on Users that tracks the most mentioned stocks across subs like , , , etc. I took the top 50 mentioned names and run all strategies across four timeframes: 5m, 15m, 1h, and 4h.

I have 1m OHLC data, but I skipped it for now. Feels like alpha probably decays too fast there, and I haven’t thought seriously about retail execution yet.

Single-name backtests run insanely fast compared to the portfolio optimization work I used to do in institutional quant (Axioma optmizer, Barra risk models, ITG transaction cost curves, etc).

Net result:

50 stocks × 80 strategies × 4 timeframes = ~16,000 backtests per run.

Lookback varies by timeframe:

• 5m → 14 days
• 15m → 30 days
• 1h → 60 days
• 4h → 180 days

I score each backtest using a composite that includes Sharpe, alpha return (vs buy & hold), win rate, number of trades (penalize higher turnover to loosely proxy costs), and max drawdown.

Obviously, if you run 16k backtests, you’re going to find some god-tier equity curves. My instinct is that I’m staring straight at a multiple-testing bias problem.

So a few questions for the group:

1. Regime momentum: My working theory is that these strategies aren’t evergreen, but might work during short-lived regimes (2 weeks on 5m, longer on higher timeframes). Has anyone here had success ranking strategies by recent performance and essentially riding the hot hand?
2. Penalizing 16k trials: I know Lopez de Prado talks about effectively deflating Sharpe by the number of tests run. I’ve been looking at the Deflated Sharpe Ratio, but I’m not sure if that’s overkill for heuristic-based retail strategies like this.
3. OOS validity: Is a 14-day lookback on a 5m strategy even long enough to justify any meaningful OOS test, or am I just looking at noise no matter what?

At this point I’m trying to figure out whether I’ve built a legitimate discovery engine… or if I’m just quantifying retail delusions with better tooling. Would love to hear from anyone who’s tried to bridge institutional risk discipline with faster-moving retail-style strategies.

After 500+ trades, I realized most traders (including me) were looking at the wrong numbers. Here's what actually moves the needle:

1. Win Rate alone is meaningless A 30% win rate can be profitable. A 70% win rate can blow your account. It depends entirely on your average win vs average loss size.

2. Profit Factor is the real signal (Gross wins / Gross losses). Above 1.5 = you have something. Above 2.0 = you have an edge. Below 1.0 = stop trading that strategy immediately.

3. Expectancy per trade tells you your hourly rate (Win% × Avg Win) - (Loss% × Avg Loss) = what you make per trade on average. If this number is negative, no amount of screen time fixes it.

4. Max consecutive losses reveals if you can survive Most strategies have 7-12 losing trades in a row at some point. If you can't stomach that drawdown, you'll quit before the edge plays out.

5. Time-of-day analysis is the hidden edge I found 73% of my winners came from the first 90 minutes. Afternoon trades were net negative. I literally made money by trading less.

The hard part isn't knowing these metrics — it's actually tracking them consistently. I built a spreadsheet that auto-calculates all 5 from raw trade entries (works in Excel and Google Sheets).

Happy to share more details on the setup if anyone's interested.

How do people actually set up their strategy?

I keep hearing about people trading with strategy, but it feels like there’s no clear path. Price action? Volume? Liquidity? SMC? ICT? Drawing random lines on chart?

What’s realistic for someone starting from scratch? curious about the "step by step" to start building strategies that actually work and not just “have discipline with good mindset lol"

Thanks, very interesting and helpful, it's similar to what I had in mind.

I’ve been working on strategy development and noticed it’s very easy to keep tweaking parameters after every backtest.

For those with more experience how do you decide when a strategy is “good enough” to move to forward testing instead of optimizing further?

Is there a rule or framework you follow?

Forgive my ignorance, why are only some touches circled? Price went through it once, too, doesn’t that break the structure or why did you choose to ignore the failed breakout below? Could have happened on your entry as well and triggered your stop loss.

All I see is a 50:50 call if the breakout is getting rejected. Like I get it, the breakouts only have a chance of less than 1/3 or something last I have read and it’s a valid strategy that absolutely works, I’m just not familiar with the particulars, hence my questions.

Most of my trading mistakes didn’t come from a bad strategy.
They came from decision fatigue.

I had rules. I had backtests.
But in real time, under pressure, I kept re-deciding things that should’ve been locked.

What changed things for me wasn’t a new indicator or timeframe.
It was reducing how many decisions I was allowed to make while in a trade.

A few examples:

• Entry rules became binary (yes/no), not “almost”
• Risk was fixed before the week started
• No discretion after entry — only execution
• Journaling focused on decision quality, not P&L

Counter-intuitively, this made trading feel boring — and that’s when results stabilized.

I’m curious:

• Do you struggle more with strategy selection or decision execution?
• At what point does flexibility start hurting more than helping?

Whats the purple box, and why is just placed in the middle, if you could explain that it would be good thanks

This is a rare great post on this sub.

You’re clearly a bot, but who’s behind you? I’m starting to think it is users itself, simulating engagement

Interesting. Can you elaborate how you calculate the Profit Factor?

You’re discovering what most experienced system traders eventually learn. Optimization is useful, but it is mostly a diagnostic tool, not a performance tool.

If a strategy only works at one or two precise parameter values, it is fragile. Robust systems usually perform similarly across a wide range of parameters. That is far more important than finding the absolute best number.

What experienced quants tend to do is optimize lightly, then stress the system hard. Randomized trade order, parameter perturbation, worse slippage, different regimes. The goal is to see if the edge survives abuse.

Tools like WealthLab make this pretty obvious when you run parameter sweeps. The good systems have broad plateaus of acceptable values. The bad ones have a single sharp peak.

Optimization can help you understand a strategy. It should not be the reason it works.

Optimization isn’t the problem.

Optimization without separation is.

If you optimize and validate on the same data, you’re just fitting noise.

Robust workflows usually include:

–in-sample / out-of-sample split

–walk-forward testing

–parameter stability analysis (flat plateaus > sharp peaks)

–Monte Carlo reshuffling

–slippage + spread stress

If a system collapses under small perturbations, it wasn’t optimized it was curve-fit.

In my experience, experienced traders don’t avoid optimization. They just optimize for stability, not max return.

Fragility is usually a signal that the edge is too narrow.

I’m still developing my personal manifesto of trading rules over time, but honestly keeping shit simple and determining my immovable stop-loss (no touchy after entry, except to move ONLY IF trade goes my direction) based on percentage risk is what flipped me from “learning” into trading profitably.

Do I second guess myself? All the time. Pretty much every trade.

But I stopped letting the idea of being “right or wrong” determine my execution of trades, and just let the trades prove or disprove my theses.

It’s a lot less exhausting than overthinking, for sure. And far less emotional. I don’t take losses personally, and while I feel good about a win, I really manage my mind and emotional state around it all so that I can keep doing it forever 🙂

Great approach. Mastering execution has been key also for me to become profitable.

Same problem! Backtest was always profitable, but when it comes to real time execution...pure chaos!

Do you have that spreadsheet available. I’d love to run my stuff thru it

what I must start learning for profitable after 6 month or more

I must use indicators ?