Trading Sins to Overcome in 2026 — A Guide for Serious TradersTrading isn’t just about charts, patterns, and strategies. It’s a mirror — one that reflects discipline, emotional maturity, patience, and self-awareness.
Most traders don’t lose because the market is “unfair.”
They lose because the market exposes weaknesses they haven’t yet worked through.
In 2026, markets will continue to evolve — liquidity shifts, narratives change faster, and emotional pressure will only increase. The traders who survive won’t just be technically skilled. They will be the ones who understand themselves.
Below are the seven trading sins every trader must confront — not with guilt, but with awareness, compassion, and discipline.
1. Lust — Chasing Hype Instead of Discipline
Lust in trading shows up as an obsession with the “shiny object”:
• chasing hyped tokens
• entering parabolic moves late
• confusing excitement with opportunity
By the time something is everywhere on social media, attention is already priced in. Late buyers don’t join rallies — they provide exit liquidity.
Psychology insight:
Lust grows from fear of missing out on belonging — not just profits. Traders chase hype because they want to “be where the action is.”
The antidote is alignment:
• trade your plan, not the market’s noise
• define your time-horizon & objectives
• stay loyal to your strategy, not to trends
A disciplined trader doesn’t need external excitement. Consistency becomes the thrill.
2. Gluttony — Overloading Strengths and Ignoring Blind Spots
Gluttony in trading isn’t overeating — it’s over-leaning:
• only trading longs
• repeating one setup everywhere
• scaling success until it becomes weakness
A trader who thrives only in one condition is not skilled — just lucky within a narrow environment.
Psychology insight:
Gluttony is rooted in comfort bias — the brain seeks repetition of what once worked, even when the environment changes.
True maturity comes from balance:
• diversify tools, not just assets
• observe the trader on the other side of your trade
• ask: does this serve my long-term objective?
Your edge is not a weapon — it is a responsibility.
3. Greed — Wanting the Whole Move Instead of the Probable One
Greed doesn’t just mean wanting more money — it means refusing to accept “enough.”
It shows up as:
• entering too early, with too much size
• letting wins turn into losses
• trying to catch bottoms and tops
Professionals don’t chase precision — they take the meat of the move.
Psychology insight:
Greed is impatience disguised as ambition.
Traders expect mastery before they’re emotionally ready for it.
Growth mindset for 2026:
• accept that mastery takes years
• define exits before entries
• allow yourself to be “wrong small” and “right sustainable”
Profit isn’t made in a single great trade — it’s built in consistency.
4. Sloth — Under-Preparation in a Constantly Changing Market
Sloth appears when traders:
• stop reviewing markets
• avoid journaling
• rely on outdated biases
The market evolves daily.
Your preparation must evolve with it.
Psychology insight:
Sloth is rarely laziness — it is avoidance of discomfort.
Reviewing mistakes is emotionally painful, so many traders avoid reflection… and repeat errors.
Habits that beat sloth:
• pre-market routine
• ongoing self-assessment
• incremental improvements rather than radical overhauls
Discipline is not intensity — it is continuity.
5. Wrath — Revenge Trading and Emotional Overreaction
Wrath in trading is anger directed at the market — and then at ourselves.
It manifests as:
• doubling down after losses
• trying to “win back” money
• self-criticism after mistakes
The damage isn’t just financial — it’s also psychological.
Psychology insight:
Wrath is triggered when ego collides with reality.
We don’t rage at the chart — we rage at losing our self-image.
Practical antidote:
• reduce size when emotional
• normalize losses in advance
• rehearse acceptance of max loss calmly
Emotional resilience is a skill — and it must be trained outside live trading.
6. Envy — Measuring Progress Against Other Traders
Envy is subtle and destructive:
• comparing returns
• trying to “catch up”
• assuming others are ahead
There will always be someone with:
• more capital
• better timing
• bigger wins
Chasing others’ journeys leads to reckless trading.
Psychology insight:
Envy grows when self-worth is tied to account balance.
Shift the lens to internal progress:
• define your goals
• measure your improvements
• celebrate small milestones
Success in trading is personal — and deeply individual.
7. Pride — Refusing to Adapt or Admit Being Wrong
Pride is the most dangerous trading sin.
It appears as:
• ignoring stop losses
• adding to losers
• defending a biased narrative
The market humbles those who resist humility.
Psychology insight:
Pride protects the ego from pain — but destroys the account.
The professional mindset:
• build plans based on objective data
• explore multiple scenarios
• let price confirm — not opinion
Adaptability is not weakness — it is the highest form of strength.
Final Thought — Growth Over Perfection
These “trading sins” are not moral flaws.
They are human patterns — predictable, emotional, deeply psychological.
The goal is not to eliminate them — but to recognize, manage, and outgrow them.
2026 will reward the trader who:
• reflects instead of reacts
• plans instead of hopes
• evolves instead of resists
Trading mastery is not the victory of logic over emotion — it is the integration of both.
Happy New Year!
Mihai Iacob
Risk Management
Chapter 7 — HOW-TO: MARAL Supports Traders in Live MarketsChapter 7 — How -TO : MARAL Supports Traders in Live Markets (v1.1.0)
Execution Discipline, Risk Control, and Greed Management (Educational Framework)
MARAL — Execution Workflow (Build v1.1.0 — Optional Modules) is a discretionary decision-support framework built in Pine Script for TradingView.
It standardizes live execution through a repeatable workflow:
Context → Qualification → Management → Action (EDC).
✅ No automation. No trade execution. No signal service. No performance guarantees.
MARAL is designed to reduce the biggest live-market problems: overtrading, greed, impulsive entries, timeframe conflict, and weak post-entry control.
7.1 Why Traders Fail Live (Even With “Correct” Concepts)
Many traders understand structure/liquidity ideas — yet still lose because execution breaks down:
Anticipation entries (entering before permission)
Timeframe conflict under pressure (HTF bias ignored by LTF noise)
Unclear invalidation (“Where exactly am I wrong?”)
Weak post-entry control (holding too long, panic exits, SL shifting)
Greed loops (overtrade after wins, revenge after losses)
MARAL is built to control decisions under stress by converting market information into clear states and actionable gates.
7.2 MARAL’s Live Execution Architecture (Boards + Optional Modules)
A) Context Board (Market Environment)
Answers: “Is this a trade-worthy environment?”
Summarizes direction, HTF bias (1H/4H/D), structure, momentum, volatility (ATR%), trend strength (ADX), scoring, liquidity context, plus optional layers such as session/LTF bias/participation.
B) Qualification Gate (Pre-Entry Permission)
Answers: “Do I have permission NOW?”
Blocks trades unless HTF/structure/momentum/regime/liquidity/alignment requirements are acceptable, then issues:
ENTRY PERMISSION: ENTER / WAIT / SKIP.
C) Management Desk (Post-Entry Control)
Answers: “How do I manage this trade without emotion?”
Monitors trade health, phase, obstacles, exit pressure, score trend, risk state, trade age, SL mode, and action state.
D) EDC — Execution Decision Core (Unified State Summary)
Answers: “What is the correct action right now?”
Compresses the entire workflow into:
SETUP → ENTRY PERMISSION → LIQUIDITY → TRADE STATUS → ACTION STATE.
Optional v1.1.0 Modules (Advanced Live Support)
ECI Panel (Execution Confidence Index)
Session Context (ACTIVE / TRANSITIONAL / DEAD)
LTF Execution Bias (15m & 5m) + LTF Exec quality
Divergence as Risk Modifier (context only; no entry trigger)
Post-Entry Stress (Manual Tracker)
Scalp Execution & Exit Panel (permission-locked)
7.3 How MARAL Controls Greed in Live Markets
Greed is not solved by motivation — it is solved by rules + visibility.
MARAL reduces greed through:
WAIT discipline (no permission = no trade)
Setup scoring + grade (filters “almost-good” entries)
Liquidity context & obstacles (prevents holding into walls)
Exit pressure + score trend (prevents emotional holding)
Post-entry stress tracking (controls behavior after entry)
Scalp permission lock (prevents fast-market overtrading)
The result is not “more trades.”
The result is better decisions and fewer mistakes.
7.4 Key Live-Market Features (How to Use MARAL Correctly)
1) Permission-First Execution (ENTER / WAIT / SKIP)
ENTER = minimum execution quality is met
WAIT = context is not confirmed (do not force entries)
SKIP = environment is unsuitable (chop/regime weakness/conflict)
2) Alignment Score + Grade (Quality Control)
Grades are execution quality labels, not predictions.
Higher grades generally reflect cleaner agreement across direction, momentum, HTF context, structure, and liquidity environment.
3) Liquidity Context + Obstacle Ahead (Risk Awareness)
Highlights sensitive zones (PDH/PDL and swing liquidity).
This helps avoid entering into traps or holding into “walls”.
4) MTF/LTF Diagnostics (Timeframe Discipline)
MTF Status: ALIGNED / MIXED / CONFLICT
LTF Exec (optional): SUPPORTIVE / WEAK / RISKY / AVOID
5) Management Desk (Post-Entry Control)
MARAL continues beyond entries:
Trade Status: VALID / RISKY / WEAK
Exit Pressure: LOW / RISING / HIGH
Action State: HOLD / TIGHT SL / SCALE OUT / EXIT
7.5 Core Filters (Copy-Safe Disclosure)
MARAL uses 6 core market filters + multiple execution intelligence layers (structure, displacement, scoring/grades, MTF/LTF diagnostics, and post-entry management).
7.6 Post-Entry Stress (Manual Tracker) — Deep Live Explanation
Why this module exists
Most traders lose control after entry (panic, greed, SL shifting, refusing to exit, adding emotionally).
Post-Entry Stress converts post-entry behavior into objective states:
✅ RISK STATE: LOW / MED / HIGH
✅ ACTION: HOLD / REDUCE / PROTECT / EXIT
This is not a signal engine. It is a discipline engine.
What you input (manual)
Tracking ON/OFF — enable only when you have a real position
Direction — Long or Short
Entry Price — your actual filled entry (not candle close)
Stop Loss (recommended) — your planned SL (manual or ATR-based)
What it monitors (conceptually)
MAE (ATR) — adverse excursion measured in ATR units (stress magnitude)
MFE (ATR) — favorable excursion measured in ATR units (progress magnitude)
Rejection pressure (wick aggression)
Volatility expansion
Opposing pressure (conditions flipping against your trade)
SL safety context (when SL is provided)
How to read it live
LOW → trade is behaving normally → HOLD
MED → stress building → REDUCE / PROTECT (rule-based)
HIGH → risk is dominant → PROTECT / EXIT
SL compromised → trade is compromised → EXIT
Professional rule:
If the stop is compromised, the trade is compromised.
7.7 MARAL v1.1.0 Feature Index — 56 User-Facing Features (Panels)
A) Context Board — 18 Features (Environment + Alignment)
1.DIRECTION — Bullish / Bearish / Neutral bias derived from the master scoring engine.
2.H1 CONTEXT — HTF1 bias state (ON/OFF; Bull/Bear/Neutral).
3.H4 CONTEXT — HTF2 bias state (ON/OFF; Bull/Bear/Neutral).
4.DAILY CONTEXT — Daily bias state (ON/OFF; Bull/Bear/Neutral).
5.STRUCTURE — Bull Struct / Bear Struct / Neutral Struct (swing structure mapping).
6.MOMENTUM — UF-RSI momentum state: BULL / BEAR / NEUTRAL.
7.VOLATILITY (ATR%) — ATR as % of price for stability/regime awareness.
8.TREND STRENGTH (ADX) — ADX-based trend quality reading.
9.LONG SCORE + Grade — Long alignment score + grade (A++/A+/A/B/No-Trade).
10.SHORT SCORE + Grade — Short alignment score + grade (A++/A+/A/B/No-Trade).
11.ALIGNMENT SCORE — master execution score used for live filtering.
12.LIQUIDITY CONTEXT — HIGH / NEUTRAL / LOW (event/near/eventless context).
13.PARTICIPATION (optional) — STRONG / NEUTRAL / WEAK (participation quality context).
14.MTF STATUS — ALIGNED / MIXED / CONFLICT (timeframe agreement diagnostic).
15.SESSION (optional) — ACTIVE / TRANSITIONAL / DEAD / OFF (session context).
16.15m BIAS (optional) — 15-minute execution bias state (Bull/Bear/Neutral).
17.5m BIAS (optional) — 5-minute execution bias state (Bull/Bear/Neutral).
18.LTF EXEC (optional) — SUPPORTIVE / WEAK / RISKY / AVOID (micro execution quality).
B) Qualification Gate — 8 Features (Permission to Execute)
19.SETUP — LONG / SHORT / WAIT based on qualified candidate conditions.
20.HTF CONTEXT — OK / WARN / BAD (direction compatibility check).
21.STRUCTURE — OK / WARN / BAD (structure confirmation strength).
22.MOMENTUM — OK / WARN / BAD (momentum confirmation + chop avoidance).
23.VOL/REGIME — OK / WARN / BAD (volatility + trend regime suitability).
24.LIQUIDITY — HIGH / NEUTRAL / LOW (execution safety context).
25.ALIGNMENT — score vs threshold (example: 78 / 65).
26.ENTRY PERMISSION — ENTER / WAIT / SKIP (final execution gate).
C) Management Desk — 11 Features (Post-Entry Control)
27.TRADE STATUS — VALID / RISKY / WEAK (idea health state).
28.MARKET PHASE — IMPULSE / PULLBACK / CONTINUATION / RANGE (phase awareness).
29.OBSTACLE AHEAD — YES / NO (PDH/PDL or swing proximity risk).
30.EXIT PRESSURE — HIGH / RISING / LOW (risk escalation logic).
31.MOMENTUM HEALTH — STRONG / WEAKENING / WEAK / NEUTRAL (post-entry momentum state)
32.SCORE TREND — IMPROVING / DETERIORATING / STABLE (quality drift).
33.RISK STATE — OVEREXTENDED / NORMAL (distance vs volatility).
34.TRADE AGE — FRESH / MID / LATE (time-in-trade awareness).
35.SL MODE — BE OK / TIGHT / NORMAL (stop behavior guidance).
36.ACTION STATE — HOLD / TIGHT SL / SCALE OUT / EXIT (rule-based action).
37.ACTIVE WINDOW — ON / OFF (management window after last setup).
D) EDC — Execution Decision Core — 5 Features (Unified Action)
38.EDC: SETUP — LONG / SHORT / WAIT.
39.EDC: ENTRY PERMISSION — ENTER / WAIT / SKIP.
40.EDC: LIQUIDITY — HIGH / NEUTRAL / LOW.
41.EDC: TRADE STATUS — VALID / RISKY / WEAK / —.
42.EDC: ACTION STATE — HOLD / TIGHT SL / SCALE OUT / EXIT / —.
E) ECI Panel (Optional) — 3 Features (Execution Confidence)
43.ECI SCORE + Grade — confidence context derived from alignment score (graded).
44.RISK MOD (optional) — POSITIVE / NEGATIVE / NEUTRAL (divergence-based modifier).
45.CAP NOTES — automatic constraints summary (why quality is capped).
F) Post-Entry Stress Panel (Optional Manual Tracker) — 6 Features
46.TRACKING — ON / OFF (manual tracker state).
47.DIRECTION — Long / Short (tracked position side).
48.MAE (ATR) — adverse excursion measured in ATR units (stress magnitude).
49.MFE (ATR) — favorable excursion measured in ATR units (progress magnitude).
50.RISK STATE — LOW / MED / HIGH (stress classification).
51.ACTION — HOLD / REDUCE / PROTECT / EXIT (stress-driven behavior).
G) Scalp Exec Panel (Optional; Permission-Locked) — 5 Features
52.SCALP ENTRY — PERMITTED / BLOCKED (strict permission lock).
53.ENTRY QUALITY — A / B / C (execution quality classification).
54.MGMT — HOLD / PROTECT / PARTIAL / EXIT (fast management instruction).
55.SL CONTEXT — VALID / AT-RISK / COMPROMISED / — (stop safety context).
56.PARTICIPATION — STRONG / NEUTRAL / WEAK / OFF (context-only quality).
7.8 Visual & On-Chart Execution Tools (Built-In)
Risk Planning (optional): Auto SL + TP1 + TP2 + TP3 (ATR-based)
PDH/PDL reference lines
Swing liquidity points (pivot highs/lows)
Optional state markers (LONG/SHORT)
Candle coloring by bias
7.9 Professional Clarity (What MARAL Is / Is Not)
MARAL supports traders by:
enforcing permission-based execution (ENTER / WAIT / SKIP)
reducing overtrading through gating + scoring
standardizing post-entry management via Trade Status + Action State
showing risk early (exit pressure, obstacles, deterioration)
enabling disciplined scalping via permission locks (optional)
MARAL does not:
predict the future
guarantee outcomes
execute trades
replace learning, discipline, or risk management
full maral panel togather for USD/GOLD
Permission first. Risk always. Discipline forever.
This script and the content in this chapter are provided strictly for educational and informational purposes.
Note : Discretionary decision-support only: MARAL is a chart-analysis workflow designed to help traders structure their decision process (context → qualification → management).
Not financial advice: Nothing here is investment advice, trading advice, or a recommendation to buy/sell any asset.No automation / no execution: The script does not place trades, execute orders, or provide any guaranteed “signal service.”No guarantees: Trading involves significant risk. Past performance does not predict future results. Any examples shown are for learning only.User responsibility: You are solely responsible for your own decisions, risk management, position sizing, and compliance with your local regulations and broker rules.
Use at your own risk. Trade responsibly.
#TradingView #PineScript #TradingEducation #Execution #TradingPsychology #RiskManagement #Discipline #NoTradeZone #Overtrading #GreedControl #TradeManagement #MarketStructure #Liquidity #SmartMoney #ICT #MTFAnalysis #PriceAction #ATR #ADX #RSI #Scalping #IntradayTrading #Forex #Crypto #Stocks
Risk Management: The Art of Long-Term Survival
Risk Management
Imagine a hero standing at a crossroads with three paths.
If he takes the road to the right, he will face a serious challenge with a difficulty level of 100. At the end of this path, however, he will be rewarded with five gold bars.
The middle road leads to ten gold bars, but the hero will encounter not one, but three challenges along the way. Each of them is no less difficult than the one on the right-hand road. Taken together, their total difficulty amounts to 300.
The left road involves a less demanding challenge with a difficulty of 60, but the reward is modest — only one gold bar.
Which path would you choose if you were in the hero’s place?
Now suppose the hero chose a balanced level of risk, but along the way he was bitten by a snake and never even reached the challenge.
This is exactly what risk-taking in financial markets looks like.
In the real world, risk is first and foremost the probability of loss.
Risk is an inevitable consequence of the fact that the future is unknown. At any given moment, there are far more possible outcomes than those that ultimately materialize. It is precisely this gap — between the range of potential outcomes and the single realized result — that gives rise to risk. The future cannot be viewed as a predetermined or predictable script; it is a spectrum of possibilities that includes both favorable and unfavorable outcomes.
An investor may estimate the range of the most likely scenarios and base their expectations of the future on them. However, even the most probable event offers no guarantee that it will actually occur.
Risk comes in many forms, and the probability of loss is only one of them. Another important type is the risk of missed opportunities — the risk of taking too little risk. Staying on the sidelines can cause an investor to miss a recovery or a growth phase and ultimately drop out of the investment process altogether.
Particularly destructive is the risk of selling at the bottom. In this case, the investor not only locks in losses but also forfeits the chance to participate in the subsequent recovery, which often leads to a permanent exit from the market.
There are also risks associated with rare but catastrophic events. These risks may remain hidden for a long time, creating the illusion that a strategy is safe — until they suddenly materialize with severe consequences, as in the example of the hero and the snake.
Risk has a contradictory and deceptive nature. It depends not only on the asset or the market itself, but also on the behavior of market participants. When people feel safe and confident, they tend to act less cautiously, and actual risk increases.
Conversely, when risk is recognized and perceived as high, behavior becomes more restrained, and risk may decrease.
Paradoxically, rising prices often increase risk, while falling prices can make an asset safer — even though most people intuitively perceive the opposite.
Risk management is not a one-time action or a reaction to a crisis; it is a continuous process.
Since it is impossible to know in advance when adverse events will occur, risk control must be present at all times, not only during periods of obvious threat.
The essence of a sound approach is not the complete avoidance of risk, but its conscious acceptance, analysis, and limitation. An investor takes on risks they understand, can diversify, and are adequately compensated for.
Ultimately, the investor’s task is to build an asymmetric outcome profile: to participate in upside when events unfold favorably, and to lose less when negative scenarios materialize.
Such asymmetry is a hallmark of true skill and reflects a deep understanding of probability distributions, hidden risks, and acceptable loss limits.
How to Form Your Own Risk Assessment in a Specific Situation
To address this question, it is useful to turn to the work of Ed Seykota. One of his core ideas can be summarized as follows:
Risk is not the size of a potential loss in itself, but the probability of that loss occurring given the current market structure.
An important implication follows from this:
The profit-to-loss ratio (risk/reward) is not an independent criterion of trade quality.
The risk of a specific trade is determined by two key factors:
the market environment,
the distribution of profits and losses.
However, the decisive element is not the absolute size of the potential profit, but the probability of achieving it, as defined by the market context
Consider a situation where the potential profit is relatively small compared to the possible loss. From a formal risk/reward perspective, such a trade appears unattractive. But if the market conditions suggest that the probability of a positive outcome is high — for example, around 90% — the risk no longer appears unreasonable. In this case, the trade is justified not by the magnitude of the payoff, but by the stability of the probabilistic edge.
An individual trade, taken in isolation, is meaningless. What matters is how similar situations play out over a large sample size.
Even with a very high probability of success, risk becomes unjustified if:
a negative scenario is capable of destroying a significant portion of the capital;
or a single rare loss outweighs the cumulative result of many successful trades.
This is why, within any robust system, probability and loss control must always go hand in hand. High probability without loss limitation is not trading — it is gambling.
Unjustified Risk
Suppose a trader manages to earn 5% on their account over the course of a month , while the benchmark — for example, the Nasdaq — delivers a return of 8% over the same period. What does this imply?
To answer this, we turn to the concept of alpha .
Alpha is a metric that measures how much a strategy’s or trader’s performance deviates from the benchmark return, after accounting for the level of market risk taken.
If a trader engages in active intraday trading — assuming operational, market, behavioral, and tail risks — yet achieves a return lower than that of the benchmark, this indicates that risk was taken without adequate compensation . The critical issue is not the mere presence of risk, but the relationship between risk and outcome.
By its nature, intraday trading involves high engagement, frequent decision-making, exposure to market noise, commissions, slippage, and psychological pressure. All of these factors increase the strategy’s total risk profile. If, despite this, the final result underperforms a passive benchmark, alpha becomes negative. This means that each unit of risk taken was not only unrewarded, but actually worsened the overall financial outcome.
In such a case, alpha does more than simply indicate “underperformance relative to the market.” It highlights the inefficiency of the risk taken . The trader is effectively performing a more complex and uncertain task while achieving a result that could have been matched — or exceeded — through passive exposure, without active trading and its associated risks.
This is precisely what constitutes unjustified risk: risk that does not increase expected returns and does not improve the distribution of outcomes.
Thus, intraday trading with returns below the benchmark is an example of risk-taking without economic rationale. Alpha here serves not as a goal, but as a diagnostic tool. If alpha is negative, it indicates that the trading risk is not merely unjustified — it is value-destructive relative to a passive alternative.
Integration into Trading
1. Market Context Comes Before the Trade
In real trading, the first object of analysis is not the entry, not the stop, and not the take-profit — it is the state of the market itself.
The key question you must answer is:
Is there a recurring market situation here that historically shifts the probability in my favor?
If the situation is not repeatable and lacks a clear internal logic, the trade is not considered at all — regardless of how attractive the risk/reward ratio may look.
2. Probability Matters More Than Potential Profit
Once the situation has been identified, the focus shifts not to profit, but to the probability of the scenario playing out.
In practical terms, this means:
You must understand why the market is more likely to continue the move rather than reverse.
The reason for entry should explain why continuation is more probable, based on the logic of market participants’ behavior — not merely be the result of a formal signal.
Even if the potential profit is relatively small, a trade may still be justified if:
The probability of success is consistently above random;
The situation is reproducible over a large sample size.
3. Loss Is Defined in Advance — and Rigidly
A loss is not something to “figure out along the way.”
It is defined before entering the trade and is not revised in the hope that the market will “come back.”
The core integration rule is simple:
No single loss should be capable of damaging the integrity of the system
This implies:
Strictly limited risk per trade;
No scenarios in which one unfavorable outcome wipes out the results of many successful trades.
4. Serial Thinking Instead of Evaluating Individual Trades
True integration happens at the mental level. You stop evaluating trades in terms of “profit or loss.”
Each trade is viewed as:
One element within a series;
One roll of the dice with a known probability bias.
In practice, this leads to:
No emotional reaction to a single loss;
No euphoria from a single winning trade.
5. Trade Selection Instead of Increased Activity
Integrating this approach almost always reduces the number of trades.
You enter the market only when:
The market provides a readable context;
The scenario has a statistical edge;
The risk is clearly defined in advance.
If the market does not offer these conditions, you do not “look for trades” — you wait.
6. Evaluating Results by Process, Not by Money
In real trading, success is not measured by daily PnL, but by:
Adherence to the logic of situation selection;
Discipline in loss limitation;
Consistency of execution.
A losing day can be a perfect day if all decisions were made within the framework of the system.
Risk Management Framework in Investing
Risk should be distributed not only across trading instruments, but also across sources of returns.
A portfolio composed of assets dependent on a single growth scenario creates an illusion of diversification while remaining structurally fragile. True diversification implies exposure to different sectors, asset classes, and underlying economic processes.
An important element of risk management is time diversification. Entering positions in stages reduces the risk of poor timing and mitigates the impact of short-term market fluctuations. Investing the full amount at a single price point turns an investment into a timing bet rather than a conviction in the underlying idea.
Liquidity risk must also be taken into account. An asset that cannot be sold without a significant discount carries hidden danger. Liquidity matters not during calm periods, but during times of stress, when exiting a position may become critically important.
Diversification also means being willing to keep part of the capital out of the market. Holding free liquidity reduces decision-making pressure and allows the investor to respond to opportunities that arise during periods of panic. Full capital deployment increases the risk of forced actions.
Risk reduction becomes necessary when uncertainty rises. Increasing correlations between assets, changes in macroeconomic conditions, growing leverage, or excessive market optimism are signals to reassess portfolio structure. In such periods, capital preservation takes precedence over returns.
An increase in investment risk is acceptable only when there is a sufficient margin of safety. Expanding exposure to higher-risk assets is justified when capital is growing, the investment horizon is long, and acceptable losses are clearly defined. An investor does not increase risk in an attempt to “catch up with the market.”
Portfolio structure should reflect not only the investor’s expectations, but also their ability to withstand unfavorable periods. There is no universal allocation; however, practical guidelines help keep risk within manageable limits.
Portfolio Structure Guidelines
Low-risk allocation serves as the foundation and stabilizer of the portfolio.
Typically, it represents 50–70% of total capital . This segment includes highly liquid assets with relatively predictable behavior. Its purpose is not to maximize returns, but to preserve capital and reduce overall portfolio volatility.
Moderate-risk allocation usually accounts for 20–40% of the portfolio. These are assets with growth potential but without critical dependence on a single scenario. They generate the core long-term returns and absorb part of the market’s fluctuations.
High-risk allocation is limited to 5–15% of capital. This segment includes assets with high volatility, asymmetric payoff potential, and an elevated probability of deep drawdowns. Losses in this zone must never threaten the integrity of the entire portfolio. If an asset can go to zero, its position size must be small enough for that outcome to be non-critical.
Rebalancing and Capital Discipline
Rebalancing is a mandatory component of risk management. As high-risk assets appreciate, their weight increases automatically, and part of the gains should be reallocated toward more stable segments. During market declines, the portfolio structure is reviewed based on changing conditions rather than emotional reactions.
Increasing exposure to high-risk assets is appropriate only when capital is growing, the investment horizon is long, and potential losses are clearly understood. Reducing exposure becomes necessary during periods of heightened uncertainty, macroeconomic shifts, or declining personal risk tolerance.
A portion of the portfolio should be held in cash. Cash is not inactivity or a missed opportunity — it is an asset that serves both defensive and strategic functions.
Typically, cash represents 10–30% of the portfolio , depending on market conditions and uncertainty. During stable growth phases, it may sit near the lower end of this range. In periods of elevated volatility, uncertainty, or after prolonged market rallies, increasing the cash allocation becomes prudent.
A cash position reduces overall portfolio risk and alleviates psychological pressure.
Free liquidity allows decisions to be made calmly, without the need to sell assets under unfavorable conditions.
The key principle lies not in finding the perfect percentage, but in maintaining the chosen structure . Discipline in risk allocation is more important than precision in initial calculations.
A Risk Management Framework in Trading
Risk management in trading does not begin with entering a trade; it begins with accepting the fact that any trade can end in a loss. A trader who is not internally aligned with this reality will inevitably violate their own rules. Accepting losses as a legitimate outcome is a fundamental condition for survival in the market.
Position sizing is more important than the entry point. Even a strong idea loses its value if its size is disproportionate to potential adverse scenarios. A trader is not required to predict direction perfectly, but they are obligated to control the consequences of being wrong.
Every trade must be “paid for” in advance. The potential loss must be known and psychologically accepted before entry. For one trader, an acceptable risk may be one percent of capital; for another, five percent. These figures are not universal truths — they reflect individual tolerance for uncertainty, trading style, and time horizon. What matters is not the number itself, but strict adherence to it.
For a beginner trader, an acceptable risk per trade is typically a loss of no more than one to two percent of the account. This level of risk allows the trader to endure a series of losing trades without causing critical damage to capital and, just as importantly, to psychological stability. Under these conditions, the risk-to-reward ratio should be no less than 1:2 and, in more favorable setups, should approach 1:3. This means that the potential profit of a trade should be at least twice, and preferably three times, greater than the potential loss. With such an approach, a trader maintains a positive mathematical expectancy even when a portion of trades ends in losses.
No single trade is decisive. The market is a sequence of attempts, not a single trial. Focusing on the outcome of an individual trade undermines discipline and distorts risk perception.
Refusing to exit is also a decision — and it carries risk. Holding a losing position in the hope of a reversal is not a neutral action; it is an active choice to increase uncertainty.
Periods of growth require no less caution than periods of decline. Confidence reinforced by a streak of successful trades often becomes the source of the largest losses. Growth in capital is a reason to reduce risk, not to increase it.
The best kind of risk is one that allows for error. A strategy that leaves no room for mistakes is doomed in the long run. Resilience matters more than precision.
The goal of risk management is not to eliminate losses, but to preserve the ability to continue trading. A trader wins not when losses are avoided, but when losses do not deprive them of the ability to take the next step.
This post is based on our own experiences and research we've gathered from books and various platforms.
Enjoy!
The Only Stop Loss and Take Profit Strategy You Need
This stop loss and take profit strategy is unique: being very efficient, safe and accurate , it can be applied for day trading, swing trading and scalping.
In this article, I will teach you how to easily place stop loss and target, applying just one basic technical tool.
Imagine that you are planning to open a trading position. You may decide to open a swing trade on a daily, a day trade on an hourly time frame, or a scalping trade on 15 minutes time frame.
For the sake of the example,
we will take a short position on GBPUSD on a daily,
a short position on NZDUSD on an hourly time frame,
and a long position on USDCHf on 15 minutes time frame.
In order to identify safe levels for TP and SL on GBPUSD, identify the closest key horizontal support and resistance on a daily time frame.
When you underline key structures, make sure that you consider the candle closes and the wicks , so that the key structure would represent the area .
Your safe stop loss will be strictly above the closest horizontal resistance,
while your target will be the upper boundary of a key horizontal support.
Selling NZDUSD on an hourly time frame, identify the closest key horizontal support and resistance on an hourly time frame.
Your safe stop loss will lie above a key resistance,
and your take profit will be the upper boundary of a key support.
Buying USDCHF on 15 minutes time frame, you do the same thing.
You identify the closest support and resistance.
Your safe stop loss will be below a key support, while your take profit will be a lower boundary of a key resistance.
Planning your trade, always remember to assess th e reward to risk ratio of your trade.
If the risk is bigger than the reward, such reward to risk ratio will be called negative .
Such a trade is better not to take.
While, the trade where reward exceeds risk will have a positive r/r ratio.
Such a trade we can take.
This stop loss and take profit placement technique is not perfect.
With experience, you will learn to set even safe stop loss and take profits, but for beginners, that is one of the safest strategies to follow.
❤️Please, support my work with like, thank you!❤️
I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis.
How High Risk-to-Reward Trades Are Actually BuiltIf you want to understand how I achieve risk-to-reward ratios of 5, 10, or higher — something you’ve probably seen in many of my analyses — stay with me until the end. 🍵
I’ll explain this step by step, directly, and without unnecessary complexity.
Step 1: Fix Your Expectations First 🔧🧠
Before anything else, your expectations must be corrected.
If you expect to trade with an average R:R of 5 and maintain an 80% win rate, you should stop reading right now. That mindset is fundamentally flawed.
Profitability does not require a high win rate. ❌
For example:
With an R:R of 2, a win rate of around 40% can already be profitable.
With an R:R of 5, profitability requires only about a 20% win rate.
That means out of 100 trades, you only need 20 winners — and you must be mentally prepared for 80 stop losses.
This brings us to a concept that is rarely discussed properly: losing streaks. 📉
A losing streak means taking multiple stop losses in a row during a trading period With a 40% win rate (R:R 2), a losing streak of 4 trades is statistically normal.
With a 20% win rate (R:R 5), losing streaks of 9 trades are expected.
If your numbers are significantly different from this, your journal needs review — not your strategy.
Once expectations are realistic, we can move forward.
Step 2: The Art of the Range 🎨
Personally, I love ranges :)
Why?
Because ranges accumulate liquidity — and liquidity eventually fuels strong, impulsive moves.
Do we trade inside the range?
Absolutely not.
You don’t want to become liquidity for others.
We trade the break — either the top or the bottom of the range.
Is it that simple? No.
Let’s walk through a real example.
On December 9, in my XAGUSD analysis on the 1H timeframe, price entered a range after a clear impulsive leg up.
Both HWC and MWC structures remained bullish, with higher lows forming inside the range.
Momentum favored buyers.
Candles were stronger on the upside.
Even fundamentals supported silver.
In this case, I waited for a break of the range high, not the low — because all parameters aligned with continuation.
When the breakout candle appeared, the position was opened.
At this point, execution paths may differ:
Some place stops below the previous range low.
Others place them behind the breakout candle.
In my case, I placed the stop below the lower wick of the breakout candle.
Why?
Because seller presence was visible, yet buyers absorbed it aggressively.
The liquidity below that wick made it statistically difficult for price to return there.
This is where trade quality increases — and high R:R becomes possible.
Step 3: Where the Real Edge Is Built 🧙♂️
This is where most traders struggle: when to exit.
I almost always use partial profit-taking — especially when HWC, MWC, and even LWC structures remain bullish.
Closing too early is a mistake.
Instead:
Close 35% at R:R 2 → the trade becomes risk-free.
Close 40% at R:R 5 → another R:R 2 is secured.
Close 20% at R:R 10 → yet another layer of edge.
Beyond R:R 10, exits depend on:
Momentum weakness
Candle rejection
Structural changes based on Dow Theory
At that point, experience and market reading matter more than rules.
(Reviewing your Personal Trading journal after 100 trades or more, helps you clearly understand how to read the market better.)
If you have questions, leave them in the comments — I’ll respond.
By the way, I’m Skeptic , founder of Skeptic Lab.
I focus on long-term performance through psychology, data-driven thinking, and tested processes.
If this was useful, feel free to support it. 🩵
More education continues tomorrow.
Peace out.
— Skeptic
What to Fix in Your Trading Process Before 2026I’ve been stopped out more than 300 times.
After years of trial, error, and reflection, I realized there was one thing missing from my process.
If I had understood and fixed it earlier, I would have become profitable much sooner.
It’s probably very simple to you.
And I’m confident that around 90% of traders either don’t do it at all — or do it incorrectly.
That skill is scenario writing.
It doesn’t matter whether you’ve been in the market for one year or three.
Writing scenarios before the trade can significantly improve your win rate.
Let me explain how.
Imagine your trading day has started.
You’re analyzing the market when suddenly a symbol begins to move with strong momentum.
Your mind says: “This fits my strategy. I should enter.”
Your emotions say: “Wait for a pullback — you’ll get a better price.”
Then another thought appears: “What if this trade covers a year’s profit?”
Logic, FOMO, and dozens of parameters start competing.
The result is usually a position with much lower quality than what your strategy actually requires.
Now imagine a second trader.
The day before, they wrote down all possible scenarios and the exact actions required for each one.
When momentum appears and resistance breaks, they enter immediately — without hesitation.
Not because they are emotionless, but because the plan already exists.
Nothing is surprising.
Nothing feels urgent.
Emotions play a minimal role because the decisions were made in advance.
I believe many of you have experienced the first situation.
So let’s look at how scenario writing should actually be done.
I’ve prepared a simple template you can copy directly into Notion.
Duplicate it daily and use it for every pair you analyze.
At first, it may feel difficult.
It might even take hours.
But after one month of consistent use, analyzing a symbol will often take less than seven minutes.
If you’ve followed my daily analyses, this structure may already feel familiar — because all my analysis is built around clear triggers, scenarios, and defined risk.
⚙️ Trading Scenario Journal Template
🧩 Structure Overview
Each position should have six sections:
Start (Setup & Entry Logic)
End (Exit & Contingency)
Actors (Market Elements)
Storyline (Expected Path)
Mid-Scenarios (Adjustments)
Goal (Purpose & Awareness)
1. Start – Setup & Entry Logic
Market Direction: Uptrend / Downtrend / Range (Weekly / Daily / 4H)
Entry Trigger: What confirms the entry (e.g., break & retest, candle pattern, volume)
Alternative Conditions: Valid setups if the main trigger fails
🗝 Only trade within the defined structure — no guessing.
2. End – Exit Logic & Contingencies
Exit Trigger: Where and why the position will be closed
If the Trigger Never Happens: Wait, cancel, or partially close
Profit Management: When to secure profit or move stop to break-even
🗝 Every “if–then” must be decided before the market forces you to act.
3. Actors – Key Market Factors
Each element either supports or weakens the setup:
Candles (strength, volatility, dominance)
Volume (confirmation or rejection)
RSI (momentum or exhaustion)
DMI / ADX (trend and volatility strength)
Support & Resistance (decision zones)
Trendlines / Channels (structural bias)
P.S: These are my personal trading plan confirmations; you need to define your own
🗝 Align at least three confirmations.
4. Storyline – Expected Path
Write the ideal “movie” of price behavior.
Example:
“Pullback to 66.4k → rejection → retest → continuation to 68k.”
If the story does not unfold, there is no trade.
🗝 You don’t predict. You prepare.
5. Mid-Scenarios – Management Adjustments
All reactions are pre-defined, not emotional:
Add if structure breaks with volume
Reduce if momentum fades or divergence appears
Exit early if volatility disappears or major news hits
Avoid adding if RSI is already overextended
🗝 Responses are designed mathematically, not emotionally.
6. Goal – Purpose of the Trade
Core Goal: Why this trade exists (continuation, test, reversal)
Expected Learning: What this trade should teach you — even if it loses
“If something unexpected happens, it means the scenario wasn’t fully planned.
Next time, it gets written down.”
If you stop writing scenarios by day seven, understand this clearly:
You are not supposed to be profitable yet.
Trading is not a comfort skill.
And this environment is not designed for comfort.
It requires structure, discipline, and emotional control.
If journaling, scenario writing, and structured analysis feel unbearable, quitting early is actually more honest than pretending.
By the way — I’m Skeptic , founder of Skeptic Lab .
I focus on long-term performance through psychology, data-driven thinking, and tested processes.
If this was useful, feel free to support it.
If you know a trader struggling with the same issue, share it — growing together is one of the most human experiences we have.
And if you have your own insights, leave them in the comments.
Let’s learn from each other.
— Skeptic
How to Break Out of the Cycle of Blowing Your Trading AccountIf you’ve been trading for over two years and still keep blowing your account, unless you do what I outline below, you’ll stay trapped in the same cycle.
Why You’re Still Blowing Your Account
There are two main reasons:
You’re overrisking or overleveraging.
You’re in a sustained losing streak caused by a bad trading plan or not following one at all.
But the real reason runs much deeper than that.
The Root of the Problem
Most people get into trading because they have a rebellious streak. you want to break away from social norms and create a life that gives you freedom. You want freedom to travel, to provide for your family your way, to buy what you want, when you want.
Somewhere along the way, you tied freedom to rebellion. You believe that to be free, you must resist rules and do things your own way.
That mindset is the same one that leads you to:
Overtrade or revenge trade.
Ignore your trading rules.
Blow your account, again and again.
Break commitments in other areas of life too.(relationship, debt, laws)
Freedom vs. Rebellion
Here’s the truth: freedom and rebellion are opposites.
Think about it.
When has a rebellious nation ever enjoyed the kind of freedom that comes with security, access, prosperity, and opportunity?
Compare Sudan, a country in constant conflict, with Switzerland, which has enjoyed peace and stability for decades.
Where do people live better, freer lives?
So, if you want true freedom, you must break the paradigm by seeing the contradiction between what you believe and what actually works.
What True Freedom Requires
To be free, you need the very things you’ve been avoiding:
Structure
Rules
Regulation
Obedience
That’s where discipline, respect for authority, and consistency begin.
In trading:
The market is the authority.
Your trading plan is the law.
Only through obedience to both will you ever achieve real trading freedom.
Build Discipline from the Ground Up
How you do anything is how you do everything.
Start with the small things:
Make your bed every morning.
Keep your home tidy.
Follow a schedule.
Track your income and expenses like a business owner.
When you build discipline in everyday habits, it naturally extends to how you trade.
Eventually, you’ll see how ridiculous it is to trade without a solid plan or to keep breaking your own rules.
That’s the moment your paradigm shifts and you finally break the cycle of blowing your account.
God bless and wishing you profitability in 2026
How to Stop Guessing and Start Trading with IntentThe Psychology Behind Trading Decisions
Estimates suggest that only about 5% of human brain activity is conscious . The remaining 95% operates at a subconscious level — outside our direct control and awareness. If this is true, then in trading, most decisions are also made unconsciously.
As Somerset Maugham once said:
“ Money is a sixth sense — without it, you cannot fully use the other five. ”
Money goes far beyond being a simple medium of exchange. It becomes an emotional and psychological factor that directly affects our sense of security, freedom, and control .
Investing and trading are among the few fields where participants work directly with money for the purpose of increasing it . And this is exactly where the trap lies — one that almost all beginners, and even experienced traders, fall into.
Why Trading Is Psychologically Different from Business
When the object of activity is not a product, not a service, and not a process, but money itself , the psyche begins to respond differently.
Consider a motherboard manufacturer. Their activity generates income only after the product is sold. There is always distance between the action and the money :
development
production
logistics
marketing
distribution
time
Profit in such a business is the result of a well-built system , not the outcome of each individual action.
In trading and investing, this distance disappears.
Money is no longer the result — it becomes the direct object of work.
Every decision is instantly reflected in the account balance
Every mistake becomes an immediate loss
Every winning trade delivers instant emotional reward
At this point, money ceases to be a neutral tool and turns into a psychological trigger .
How the Market Hijacks Decision-Making
Fear of loss intensifies.
Greed increases.
Decision-making accelerates.
Choices are no longer driven by logic, but by automatic reactions :
fear of loss
greed
the need to be right
the urge to quickly recover losses
The market constantly provokes these reactions. Without structure, a trader begins to act impulsively — even while believing that everything has been “ carefully thought through .”
The Illusion of Rationality
A sense of rational process emerges:
the chart is analyzed
arguments for entry are found
exit levels are reconsidered
Yet without pre-defined rules , these actions are not logic. They are attempts to justify a decision made under the influence of the moment.
Trading turns into a sequence of chaotic market decisions:
mental pressure builds
motivation fades
fatigue sets in
internal tension accumulates
Each new trade begins to feel like a way to “ fix ” the previous one.
In such an environment, the trader stops managing risk and starts being managed by emotions .
An illusion of control appears:
just a bit more analysis, one more argument — and the market has to respond correctly.
If this sounds familiar, you know the feeling.
Why Most Losses Actually Happen
Most losses occur not because of poor analysis, but because the plan was not fixed before entry .
When trade management is no longer handled by a strategy, it is taken over by the psyche.
And the psyche cannot work with probabilities — it can only:
avoid pain
seek pleasure
Where Logical Trading Begins
Logical trading begins where the subconscious has nothing left to decide .
All key questions are answered in advance:
What is a valid trigger and confirmation for entry?
When and how will I exit?
How do I interpret mistakes?
Under what conditions do I not trade?
How is risk managed?
At the moment of execution, the trader does not think — he executes .
And the fewer decisions that must be made while in a position, the lower the chance that those decisions will be driven by fear or hope .
The Role of a Trading Strategy
So how can this be achieved?
The answer is a trading strategy.
A trading strategy is not :
a set of indicators
a “favorite setup”
A trading strategy is a formalized logic of actions that exists before entering the market.
It answers all key questions in advance and leaves no room for improvisation at the moment when pressure is highest.
Crucially, the strategy must be documented — not only in your head, but on paper or in digital form — so the market has no chance to confuse you.
What a Solid Trading Strategy Defines
A complete strategy clearly specifies:
which method of analysis is used
under what market conditions trading makes sense
how a trade idea is formed
what time of day trading is conducted
which analytical tools are used and how they are interpreted
where the trade idea is proven wrong
specifics of trading different assets
how risk and position size are calculated
how the trade is managed after entry
how mistakes are reviewed and analyzed
A strategy is not something you “feel”
If it can be changed during the trade — it is not a strategy
Strategy vs. Losses
It is important to understand:
A strategy does not eliminate losses. It eliminates chaos.
A loss within a strategy is a planned expense , not a mistake.
A mistake is a rule violation driven by emotion .
When a strategy is clearly defined and tested, the trader’s role is reduced to execution .
At this point:
you stop “feeling the market”
you start working with probabilities
A single trade no longer matters.
What matters is the series , the statistics , the long run .
That is why professionals think not in terms of profit or loss, but in terms of process .
Final Thought
A trading strategy takes over the 95% of decisions that were previously made subconsciously.
The trader is left with only one task:
Follow the system..
Enjoy!
Gold vs Real Estate: Which Is Safer?Gold vs Real Estate: Which One Truly Keeps Your Money Safe in Uncertain Times?
When markets turn unstable, the first question that always comes up is: “ How do I keep my money safe ?”
Almost immediately, two familiar names are put on the scale: gold and real estate .
One is a globally recognized defensive asset.
The other is a tangible asset tied to land and long-term growth cycles.
But safety does not lie in the name of the asset — it lies in how you use it .
Safety does not mean “never going down”
Many people mistakenly believe that a safe asset is one that never declines in price. In reality, every asset goes through corrections .
True safety means:
When you need cash, can you actually convert it?
When markets deteriorate, can you withstand the psychological and cash-flow pressure?
When the cycle shifts, does that asset help you survive?
And this is exactly where gold and real estate begin to diverge.
Gold — safety through liquidity and defense
Gold is considered safe because it does not depend on a single economy . When inflation rises, crises emerge, or confidence in fiat currencies weakens, gold is often chosen as a safe haven.
Gold’s greatest strength is liquidity . It can be converted into cash almost instantly, nearly anywhere in the world. This makes gold an effective defensive tool during periods of strong market volatility.
However, gold does not generate cash flow . Its price can also move sideways for long periods, requiring patience and a capital-preservation mindset rather than a get-rich-quick mentality.
Real estate — safety through tangibility and long-term value
Real estate feels safe because it is tangible and familiar . The land remains. The property remains. Over the long term, real estate tends to appreciate alongside economic growth and urbanization.
In addition, real estate can generate rental income , something gold cannot offer. For investors with stable capital and no pressure to rotate funds quickly, this is a major advantage.
The trade-off, however, is low liquidity . When markets weaken or credit conditions tighten, selling property can take a long time. If leverage is involved, this so-called “safe asset” can quickly become a financial burden.
The core difference: time horizon and flexibility
Gold suits investors who value flexibility and fast response .
Real estate suits those with long-term vision, substantial capital, and the ability to endure cycles .
Gold helps you defend in the short to medium term .
Real estate helps you build wealth over the long term .
No asset replaces the other.
They differ only in their role within your financial strategy .
What Is the Bull Side – and What Is the Bear Side?In trading, there are concepts that everyone has heard of , but not everyone truly understands correctly . “ Bull side ” and “ Bear side ” are two such terms. Many traders use them every day, yet often assign them overly simplistic meanings: bulls mean buying, bears mean selling.
In reality, behind these two concepts lies how the market operates , how capital flows think , and how traders choose which side to stand on .
What Is the Bull Side?
The Bull side (bulls) represents those who expect prices to rise . However, bulls are not simply about buying .
The true essence of the bull side is the belief that the current price is lower than its future value , and that the market has enough momentum to continue moving upward .
The bull side typically appears when:
Price structure shows that an uptrend is being maintained
Active buying pressure controls pullbacks
The market reacts positively to news or fresh capital inflows
More importantly, strong bulls do not need price to rise quickly . What they need is a structured advance , with healthy pauses and clear support levels to continue higher.
What Is the Bear Side?
The Bear side (bears) represents those who expect prices to fall . Like bulls, bears are not merely about selling .
The core of the bear side is the belief that the current price is higher than its true value , and that selling pressure will gradually take control .
The bear side tends to strengthen when:
An uptrend begins to weaken or breaks down
Price no longer responds positively to good news
Every rally is met with clear selling pressure
A market dominated by bears does not always collapse sharply . Sometimes, it shows up as weak rebounds , slow and extended , but unable to travel far .
When Does the Market Lean Toward Bulls or Bears?
The market is never fixed to one side . It is constantly shifting .
There are periods when bulls are in control , times when bears dominate , and moments when neither side is truly strong .
Professional traders do not try to predict which side is right . Instead, they observe:
Which side controls the main move
Which side is reacting more weakly over time
What price is respecting more: support or resistance
These price reactions reveal who is in control , not personal opinions or emotions.
Common Mistakes When Talking About Bulls and Bears
Many traders believe they must “ choose a side ” and remain loyal to it . In reality, the market does not require loyalty .
The market only demands adaptation .
Today’s bulls can become tomorrow’s bears .
A skilled trader is someone who is willing to change perspective when the data changes , rather than defending an outdated view .
Why Reducing Trading Fees Is The Foundation of Risk ManagementMost traders obsess over entries, exits, indicators, and leverage.
Very few obsess over fees .
That’s odd; because unlike your strategy, your psychology, or the market itself, trading fees are guaranteed . They apply to every trade, in every market condition, whether you win or lose.
If risk management is about controlling what you can, trading fees should be the first place to start.
Trading Fees Are a Permanent Tax on Activity
Maker vs taker fees, VIP tiers, and exchange comparisons are well-known topics.
What’s often missed is the cumulative effect:
High-frequency trading multiplies fees rapidly
Lower timeframes amplify churn
Leverage magnifies fee impact on ROI
You can make correct directional calls and still watch profits evaporate simply due to volume-based costs.
Fees don’t care if your trade was “good”.
Why Traders Mentally Ignore Fees
Fees are usually framed as:
“The cost of doing business”
“Small enough not to matter”
“Something I’ll optimize later”
But later rarely comes.
Most traders optimize strategy first and infrastructure las t, even though infrastructure compounds quietly over time.
This is the same reason many traders focus on win rate instead of expectancy.
Referral Codes Aren’t Just Marketing Gimmicks
Here’s an under-discussed mechanic:
Exchanges pay affiliates a share of the trading fees generated by referred users.
Structurally, nothing forces affiliates to keep that commission.
Some setups return a portion of those fees back to the trader as ongoing rebates , effectively lowering trading costs indefinitely ; not as a one-time bonus, but as a permanent modifier.
That makes referral mechanics less about marketing and more about cost structure.
Fee Reduction Is Risk Management, Not Optimization
Reducing fees:
Improves expectancy without changing strategy
Reduces drawdowns during choppy conditions
Increases survivability during high-volume phases
Compounds positively over time
Unlike indicators, it doesn’t introduce noise.
Unlike leverage, it doesn’t increase risk.
It simply removes friction.
Why This Matters More for Active Traders
If you:
Trade frequently
Use algorithmic or semi-automated strategies
Operate on lower timeframes
Manage multiple positions
…then fee drag is one of the largest silent variables in your system.
Ignoring it is equivalent to ignoring slippage or execution quality.
Making Fee Reduction Part of Your Setup
Some traders handle this by:
Reaching higher VIP tiers
Negotiating institutional rates
Using rebate or cashback mechanisms
The key shift is treating fee reduction as infrastructure , not an afterthought.
If you already track risk, exposure, and performance metrics, fees deserve the same level of attention.
Final Thought
You can’t control the market.
You can’t guarantee execution.
But you can control how much friction you accept per trade.
If risk management is about stacking small, permanent edges, then reducing trading fees isn’t optional; it’s foundational.
For those curious about how traders automate fee rebates and make this part of their infrastructure, educational resources exist that break down the mechanics step by step (for example, how Bybit referral rebates work and how they can be applied even after account creation).
THE PSYCHOLOGY OF TRADING: WHY MOST TRADERS LOSE?You have probably heard that most people who attempt trading end up losing money. There’s a
good reason for this, and the reason is primarily that most people think about trading in the
wrong light.
Most people come into the markets with unrealistic expectations, such as thinking they are
going to quit their jobs after a month of trading or thinking they are going to turn $1,000 into
$100,000 in a few months. These unrealistic expectations work to foster an account-destroying
trading mindset because traders feel too much pressure or “need” to make money.
When you begin trading with this pressure, you inevitably end up trading emotionally—which is
the fastest way to lose your money.
To be specific, let’s break down the 4 Main Emotional Factors that destroy portfolios: FOMO,
Fear, Revenge, and Greed.
__________________________________________________________________________________
1. FOMO (Fear of Missing Out)
FOMO is an emotional state experienced by almost everyone. For traders, it is accelerated by
feelings of jealousy, envy, and impatience. The depth of these emotions is intensified by the
fast-acting environment of the Crypto and Forex markets.
How to Avoid FOMO:
● Develop a Routine: Trading is often a singular, lonesome pursuit. Eliminate distractions
and focus on identifying key market spots to tune out external chatter. Avoid social
media outlets and ungrateful attitudes.
● Be Present Minded, Future Thinking: Just because a trade is lost does not mean the
following transactions will follow suit. There are always more trading opportunities. Stay
present-minded yet have your scope set upon the future goals of your trading.
● Employ a Trading Plan: No plan is perfect, but a well-developed plan covers most
eventualities, helping you invest with lower risk exposure and more consistency.
Establish short-term, medium, and long-term trading goals.
● Take Joy from Trading: FOMO stems from insecurity and greed. Once a trader grasps
this truth, they can cast out this reckless state and trade with maximum potential.
__________________________________________________________________________________
2. GREED (The Account Destroyer)
There’s an old saying regarding markets: “Bulls make money, bears make money, and pigs
get slaughtered.”
This means if you are a "greedy pig" in the markets, you are almost certainly going to lose.
Greed acts as a trader’s kryptonite. When the desire for wealth clouds logic, traders make fatal
mistakes such as:
● Not taking profits because they think a trade will go on forever.
● Adding to a position simply because the market moved slightly in their favor (without
logical price action reasons).
● Using excessive leverage to maximize potential gains.
● Doubling down on losing trades (The Martingale Strategy).
Advice for Avoiding Greed:
Think of greed as the counterpart to discipline. Traders who are well-poised and consistent are
less likely to fall victim to greed. It is critical that every trader consistently follow trading plans;
otherwise, the likelihood of slipping into destructive habits is far greater.
__________________________________________________________________________________
3. FEAR
Fear often arises after a trader hits a series of losing trades or suffers a loss larger than what
they are emotionally capable of absorbing.
When fear takes over, you hesitate. You might see a perfect setup that aligns with your strategy, but you freeze because you are afraid of losing again. Or, you might cut a winning trade too early because you are terrified the market will turn against you. Fear paralyzes your ability to execute your edge.
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4. REVENGE TRADING
Revenge trading is a natural emotional response when a trader suffers a significant loss. The
idea is to recover the money immediately. The thinking is: "If I put on another trade right now, I can win it back."
Usually, this "expected" winning trade turns into a losing trade—often bigger than the first one.
5 Effective Ways to Fight Revenge Trading:
1. Step Back Temporarily: Take a day or two off. If you must be in the markets, trade
incredibly small, but the best course is to walk away.
2. Make a Self-Assessment: Once you are emotion-free, analyze what led to the loss.
Was it a bad strategy, or bad execution?
3. Assess Market Conditions: Is the market too volatile? Are there no solid trends?
Sometimes the best trade is no trade.
4. Assess Your Strategy: Check your entry and exit criteria. Did you actually see a setup,
or did you force a trade out of anger?
5. Make Necessary Adjustments: Note the feedback, learn the lesson, and mentally
"throw" the bad trade away. Affirm to yourself: "That is how I will do it next time."
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SUMMARY
Trading is simple, but it is not easy. The charts are the easy part; managing your own mind is
where the real work begins. Identify these four emotions— FOMO, Fear, Greed, and
Revenge —and suppress them the moment they arise.
Are you controlling your emotions, or are they controlling your portfolio? Let me know in
the comments below.
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Disclaimer: This content is for educational purposes only. Trading involves significant risk.
The Retail Trend-Following MythThe Illusion of Simple Profits: A Quantitative Analysis of Moving Average Trend Following Strategies and the Gap Between Retail Mythology and Institutional Reality
The proliferation of retail trading education has created a widespread belief that trend following through moving average crossover systems represents a reliable path to consistent profits. This study challenges that assumption through empirical analysis of over 50,000 backtested strategy configurations across multiple asset classes. Our findings reveal that the simplified trend following approaches promoted in retail trading circles fail to generate statistically significant risk-adjusted returns after accounting for realistic transaction costs.
More critically, we demonstrate that what retail traders understand as trend following bears little resemblance to the sophisticated quantitative approaches employed by institutional trend followers who have historically captured crisis alpha. This paper bridges the gap between retail mythology and institutional reality, providing both a cautionary analysis and a roadmap toward more rigorous trend following methodologies.
1. Introduction
Every year, millions of aspiring traders encounter some variation of the same promise: draw two lines on a chart, wait for them to cross, and watch the profits roll in. The golden cross strategy, where a 50-day moving average crosses above a 200-day moving average to signal a buy, has achieved almost mythological status in retail trading education. YouTube tutorials, trading courses, and social media influencers present these systems as the democratization of Wall Street wisdom, finally making the secrets of the wealthy accessible to ordinary people.
But here is an uncomfortable question that rarely gets asked: if these strategies are so effective and so simple, why do professional trend followers employ entirely different methods? Why do firms like AQR Capital Management, Man AHL, and Winton Group invest millions in research infrastructure when a few moving averages would apparently suffice?
This study was designed to answer that question empirically. We constructed a comprehensive testing framework spanning eight major asset classes, six moving average calculation methods, and multiple strategy configurations including both long-only and long-short implementations. The results paint a sobering picture for anyone who believed that profitable trading could be reduced to watching two lines cross.
Figure 1 displays the distribution of Sharpe ratios across all tested strategy configurations, separated by asset class. The box plots show the median performance (horizontal line), interquartile range (box), and outliers (individual points).
What immediately strikes the eye is how many configurations cluster around or below zero. A Sharpe ratio of zero means the strategy performed no better than holding cash. The wide spread of outcomes, particularly visible in the currency pairs, suggests that any apparent success in trend following may be attributable to luck rather than skill. Notice how even the best performing asset, SPY, shows a median Sharpe ratio barely above 0.3, which institutional investors would consider inadequate for a standalone strategy.
2. Methodology and Data
Our analysis employed daily price data from 2010 through 2024 for the following instruments: SPY representing US equities, GLD for gold, USO for crude oil, SLV for silver, and currency ETFs FXE, FXB, FXY, and FXA representing EUR/USD, GBP/USD, USD/JPY, and AUD/USD respectively. This fourteen-year period encompasses multiple market regimes including the post-financial crisis bull market, the 2015-2016 commodity crash, the COVID-19 volatility event, and the 2022 inflation-driven correction.
We tested six moving average types: Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA), Hull Moving Average (HMA), Double Exponential Moving Average (DEMA), and Triple Exponential Moving Average (TEMA). Fast period parameters ranged from 5 to 50 days while slow period parameters ranged from 20 to 200 days, constrained such that the fast period was always shorter than the slow period.
Critically, each configuration was tested in two modes. The long-only mode, which is what most retail traders employ, takes a long position when the trend signal is bullish and exits to cash when bearish. The long-short mode, more common among professional trend followers, takes a long position when bullish and a short position when bearish, maintaining constant market exposure in one direction or the other.
Transaction costs were set at 10 basis points per trade, which is generous compared to what many retail brokers actually charge when accounting for bid-ask spreads, particularly in less liquid instruments. Position changes from long to short incur double the transaction cost since both a sale and a purchase occur.
Figure 2 compares the performance distributions of different strategy modes. Each box represents thousands of backtested configurations. The striking finding here is that long-short strategies, which are theoretically capable of profiting in both rising and falling markets, show worse average performance than their long-only counterparts in most cases. This contradicts the intuition that being able to profit from downtrends should improve overall returns. The explanation lies in the persistence of the equity risk premium during our sample period, combined with the whipsaw costs incurred when strategies repeatedly flip between long and short positions during trendless markets.
3. The Retail Trader Illusion
Before presenting our quantitative findings in detail, it is worth examining what retail traders typically believe about trend following and why those beliefs are so persistent despite limited evidence.
The standard retail narrative goes something like this: markets trend because of herding behavior among participants. Once a trend begins, it tends to continue because traders observe price movement and pile in, creating self-fulfilling momentum. Moving averages smooth out noise and reveal the underlying trend direction. When a faster moving average crosses above a slower one, it confirms that recent price action is stronger than historical price action, signaling the beginning of a new uptrend. The reverse signals a downtrend.
This narrative contains elements of truth but dangerously oversimplifies the challenge. What it omits is far more important than what it includes.
First, it ignores the distinction between trending and mean-reverting market regimes. Research by Hurst, Ooi, and Pedersen (2017) demonstrates that trend following strategies have historically made most of their returns during relatively brief crisis periods while suffering extended drawdowns during calm markets. The 2008 financial crisis was extremely profitable for trend followers. The 2009 to 2019 period was largely a grind. Retail traders who expect consistent monthly returns from trend following will be disappointed and likely abandon the approach precisely when they should be persisting.
Second, the simple crossover story ignores the profound impact of parameter selection. Our analysis tested thousands of parameter combinations. The difference between the best and worst performing parameter sets within the same asset class often exceeded 2 Sharpe ratio points. This creates a severe multiple testing problem. When you test enough combinations, some will appear profitable by chance alone. The probability that the specific combination you choose going forward will perform as well as the historical backtest suggests is remarkably low.
Figure 3 presents a heatmap showing average Sharpe ratios for each combination of moving average type and asset class. Darker blue colors indicate better performance while red indicates worse performance. The pattern is immediately revealing. There is no single moving average type that dominates across all assets. EMA works reasonably for SPY but poorly for currencies. HMA shows promise in gold but disappoints in crude oil. This inconsistency suggests that any apparent edge from a particular MA type may be spurious, resulting from data mining rather than a genuine economic effect. A truly robust strategy should show more consistency across markets.
Third and most importantly, the retail narrative treats trend following as a complete strategy when it is actually just a signal generation method. Professional trend followers embed their signals within comprehensive systems that include volatility scaling, correlation-based position sizing, portfolio construction optimization, and dynamic leverage management. The signal is perhaps ten percent of the system. The retail trader who implements only that ten percent is like someone who buys a car engine and wonders why it does not drive.
4. What Professionals Actually Do
To understand the gap between retail and institutional trend following, we must examine what professional systematic traders actually implement. The following section introduces several key concepts with their mathematical foundations.
4.1 Volatility-Adjusted Position Sizing
Retail traders typically allocate fixed percentages of capital to each trade. Professional trend followers normalize position sizes by volatility so that each position contributes approximately equal risk to the portfolio. The standard approach uses the formula:
Position Size = (Target Risk) / (Instrument Volatility x Price)
Where target risk is often expressed as a fraction of portfolio equity and volatility is typically measured as the annualized standard deviation of returns over a recent lookback period, commonly 20 to 60 days. This approach, documented extensively by Carver (2015), ensures that a position in a highly volatile instrument like crude oil does not dominate the portfolio simply because it moves more.
The mathematical expression for the number of contracts or shares to hold becomes:
N = (k x E) / (sigma x P x M)
Where N is the number of contracts, k is the target risk as a percentage of equity, E is total equity, sigma is the annualized volatility, P is the price, and M is the contract multiplier. This seemingly simple formula has profound implications. It means position sizes change daily as volatility evolves, automatically reducing exposure during turbulent periods and increasing it during calm periods.
4.2 The Time Series Momentum Factor
Academic research by Moskowitz, Ooi, and Pedersen (2012) formalized trend following as time series momentum, distinct from the cross-sectional momentum studied in equity markets. The signal for instrument i at time t is calculated as:
Signal(i,t) = r(i,t-12,t) / sigma(i,t)
Where r(i,t-12,t) is the cumulative return over the past 12 months and sigma(i,t) is the annualized volatility. This creates a standardized momentum measure that can be compared across instruments with very different volatility characteristics.
The position in each instrument is then:
Position(i,t) = Signal(i,t) x (Target Volatility / sigma(i,t))
This double normalization by volatility, once in the signal and once in the position size, is crucial. It prevents the strategy from making large bets simply because an instrument has been moving a lot recently.
4.3 Exponentially Weighted Moving Average Crossover with Trend Strength
A more sophisticated approach to moving average signals incorporates trend strength rather than simple direction. The trend strength measure advocated by Baz et al. (2015) is:
TSMOM = (EWMA_fast - EWMA_slow) / sigma
Where EWMA represents the exponentially weighted moving average with different half-lives and sigma is recent volatility. Rather than generating binary signals, this approach creates a continuous signal that ranges from strongly negative to strongly positive. Positions are scaled proportionally:
Position = sign(TSMOM) x min(|TSMOM|, cap) x base_position
The cap parameter prevents extreme positions when the signal is exceptionally strong, which often occurs during bubbles or crashes when trend followers are most vulnerable to reversals.
4.4 Correlation-Based Portfolio Construction
Perhaps the most significant difference between retail and institutional trend following is portfolio construction. Retail traders typically divide capital equally among instruments or allocate based on conviction. Professionals optimize allocations to account for correlations between positions.
The mean-variance optimization framework determines weights w to maximize:
w'mu - (lambda/2) x w'Sigma w
Subject to constraints on total exposure, sector concentration, and other risk limits. Here mu is the vector of expected returns based on trend signals, Sigma is the covariance matrix of instrument returns, and lambda is a risk aversion parameter.
More advanced implementations use hierarchical risk parity as developed by Lopez de Prado (2016), which clusters instruments by correlation structure and allocates risk equally across clusters rather than instruments. This prevents highly correlated positions from dominating the portfolio.
4.5 Regression-Based Trend Detection: The Statistical Foundation
The most sophisticated trend following approaches employed by quantitative hedge funds move beyond simple price averaging entirely. Instead, they treat trend detection as a statistical inference problem, asking not merely whether prices are rising or falling, but whether the observed price movement represents a statistically significant trend or merely random walk behavior.
The regression-based trend model, implemented by firms such as Winton Group and Man AHL, represents the gold standard in this domain. Rather than smoothing prices through moving averages, this approach fits a linear regression model to price data over a rolling window, extracting both the slope coefficient and its statistical significance.
The mathematical foundation begins with the standard linear regression model:
P(t) = alpha + beta x t + epsilon(t)
Where P(t) represents the price at time t, alpha is the intercept term, beta is the slope coefficient representing the trend strength, t is the time index, and epsilon(t) is the error term assumed to be independently and identically distributed with mean zero and variance sigma squared.
For a rolling window of length L ending at time T, we observe prices P(T-L+1), P(T-L+2), ..., P(T). The ordinary least squares estimator for the slope coefficient is:
beta_hat = sum((t - t_bar) x (P(t) - P_bar)) / sum((t - t_bar)^2)
Where t_bar = (1/L) x sum(t) and P_bar = (1/L) x sum(P(t)) represent the sample means of the time index and prices respectively, with both summations running from t = T-L+1 to t = T.
The numerator represents the covariance between time and price, while the denominator is the variance of the time index. This formulation makes intuitive sense: if prices consistently increase over time, the covariance will be positive, producing a positive slope estimate.
However, extracting the slope alone is insufficient. A positive slope could arise from random walk behavior with an upward drift, or it could represent a genuine trend. To distinguish between these cases, we must assess the statistical significance of the slope coefficient.
The standard error of the slope estimator is:
SE(beta_hat) = sqrt(MSE / sum((t - t_bar)^2))
Where MSE, the mean squared error, is calculated as:
MSE = (1/(L-2)) x sum((P(t) - alpha_hat - beta_hat x t)^2)
The t-statistic for testing the null hypothesis that beta equals zero is:
t_stat = beta_hat / SE(beta_hat)
Under the null hypothesis of no trend, this statistic follows a t-distribution with L-2 degrees of freedom. A large absolute t-statistic indicates that the observed slope is unlikely to have occurred by chance, providing evidence for a genuine trend.
The signal generation mechanism then becomes:
Signal(t) = sign(beta_hat) x min(|t_stat| / t_critical, 1)
Where t_critical is the critical value from the t-distribution at the desired significance level, typically 1.96 for a two-tailed test at the five percent level. This formulation creates a continuous signal that ranges from -1 to +1, with magnitude proportional to both trend strength and statistical confidence.
The position sizing formula incorporates both the slope and its significance:
Position(t) = (beta_hat / sigma_returns) x (|t_stat| / t_critical) x (Target_Volatility / sigma_instrument)
This triple normalization is crucial. The first term, beta_hat / sigma_returns, standardizes the slope by recent return volatility, preventing the strategy from taking large positions simply because prices have been moving rapidly. The second term, |t_stat| / t_critical, scales the position by statistical confidence, reducing exposure when trends are weak or statistically insignificant. The third term, Target_Volatility / sigma_instrument, ensures that each position contributes equal risk to the portfolio regardless of the instrument's inherent volatility.
The multi-horizon ensemble extension, which significantly improves robustness, runs parallel regressions across multiple lookback windows. Common choices include 20, 60, 120, and 252 trading days, corresponding roughly to one month, one quarter, six months, and one year. The final signal becomes a weighted average:
Signal_ensemble(t) = sum(w_i x Signal_i(t))
Where w_i represents the weight assigned to horizon i, typically determined through out-of-sample optimization or equal weighting. Research by Hurst, Ooi, and Pedersen (2017) demonstrates that ensemble approaches reduce the variance of returns by approximately 30 percent compared to single-horizon implementations while maintaining similar mean returns.
The computational efficiency of this approach in modern trading platforms stems from the recursive updating property of linear regression. When moving from window ending at time T to time T+1, we can update the regression statistics without recalculating from scratch:
beta_hat_new = beta_hat_old + delta_beta
Where delta_beta can be computed efficiently using only the new data point and the previous regression statistics. This makes the approach computationally tractable even when applied to hundreds of instruments with multiple lookback windows.
The superiority of regression-based trend detection over moving averages becomes apparent when examining performance during regime transitions. Moving averages, being backward-looking by construction, always lag price movements. A regression model, by explicitly modeling the relationship between time and price, can detect trend changes more rapidly, particularly when combined with significance testing that filters out noise.
Empirical evidence from institutional implementations suggests Sharpe ratio improvements of 0.2 to 0.4 points compared to equivalent moving average systems. However, this improvement comes at the cost of increased complexity and the requirement for statistical software infrastructure that most retail traders lack.
Figure 4 plots Sharpe ratios against Sortino ratios for all strategy configurations. The Sortino ratio, which measures risk-adjusted returns using only downside deviation rather than total volatility, provides insight into whether strategies achieve returns through consistent positive performance or through occasional large gains offset by frequent small losses. Points clustering along the diagonal indicate balanced risk profiles, while points above the diagonal suggest strategies with favorable upside capture relative to downside exposure. The wide scatter in this plot further reinforces the lack of a robust edge in simple moving average systems.
Figures 5a through 5i present heatmaps showing average Sharpe ratios for each combination of fast and slow moving average types, separately for each asset class. These visualizations reveal the extreme parameter sensitivity that plagues retail trend following. Notice how performance varies dramatically across MA type combinations even within the same asset. For SPY, EMA paired with SMA shows reasonable performance, but EMA paired with HMA produces substantially worse results. This inconsistency across what should be similar smoothing methods suggests that any apparent edges are fragile and unlikely to persist out of sample.
Figure 6 shows average Sharpe ratios for different combinations of fast and slow moving average periods. The horizontal axis shows the fast period in days while the vertical axis shows the slow period. Each cell represents the average performance across all assets and MA types for that specific period combination. Notice the inconsistent pattern. There is no clear sweet spot where performance is reliably strong. Some period combinations that work well in certain market conditions fail completely in others. This lack of a robust optimal parameter region is a warning sign that the apparent edges we observe may be artifacts of our specific sample period rather than persistent market inefficiencies.
5. Empirical Results
Our research produced sobering results for the retail trend following thesis. Across 51,840 unique strategy configurations, the mean Sharpe ratio was 0.18 with a standard deviation of 0.42. Only 23 percent of configurations produced Sharpe ratios above 0.5, which is generally considered the minimum threshold for a viable strategy. A mere 8 percent exceeded 1.0.
Figure 7 presents the optimal parameter combination identified for each asset class through our grid search optimization. While these numbers may appear attractive in isolation, they must be interpreted with extreme caution. These are in-sample optimized results, meaning we selected the best performing parameters after observing all the data. The probability that these exact parameters will produce similar results going forward is low. Academic research consistently shows that out-of-sample performance degrades by 50 percent or more compared to in-sample optimization (Moskowitz, Ooi, and Pedersen, 2012).
The asset class breakdown reveals further challenges. Equity index trend following in SPY produced the most consistent results, with a best Sharpe ratio of 0.87 for the dual moving average long-only strategy using EMA with 10 and 75 day periods. Currency pairs performed substantially worse, with best Sharpe ratios ranging from 0.31 to 0.52. Commodities fell in between, with gold showing 0.68 and crude oil at 0.54.
These results align with the academic literature. Moskowitz, Ooi, and Pedersen (2012) document significant time series momentum profits in equity index futures but weaker effects in currencies. The explanation likely relates to central bank intervention in currency markets, which can abruptly reverse trends, and the generally higher efficiency of currency markets where large institutional participants dominate.
Figure 8 compares the performance distributions of different moving average calculation methods. Each box plot represents thousands of configurations using that specific MA type. The most striking finding is the absence of a clearly superior method. Simple Moving Average, the most basic calculation, performs comparably to sophisticated alternatives like Hull Moving Average or Triple Exponential Moving Average. This undermines the popular belief that exotic MA types provide meaningful edges. In fact, more complex calculations introduce additional parameters that create more opportunities for overfitting.
The long-short versus long-only comparison yielded counterintuitive results. Conventional wisdom suggests that long-short strategies should outperform because they can profit in both directions. Our data shows the opposite in most cases. The long-short configurations produced mean Sharpe ratios of 0.12 compared to 0.24 for long-only. This approximately fifty percent reduction reflects two factors: the persistent upward drift in equity markets during our sample period, and the transaction costs incurred when strategies flip between long and short positions during trendless periods.
Figure 9 plots each strategy configuration by its maximum drawdown on the horizontal axis and its compound annual growth rate on the vertical axis. Each dot represents one backtested configuration, color-coded by asset class. The ideal positions would be in the upper right, showing high returns with shallow drawdowns. Instead, we observe a cloud of points with no clear relationship between risk and return at the strategy level. Many configurations that achieved high returns also suffered devastating drawdowns exceeding fifty percent. Conversely, strategies with modest drawdowns rarely exceeded single-digit annual returns. This lack of a favorable risk-return tradeoff suggests that trend following, as implemented in these simple forms, does not offer a free lunch.
6. Statistical Significance Testing
To address the multiple testing problem inherent in evaluating thousands of strategy configurations, we applied rigorous statistical tests. One-way ANOVA comparing Sharpe ratios across MA types produced an F-statistic of 2.34 with a p-value of 0.038. While technically significant at the five percent level, the effect size is tiny, explaining less than one percent of variance in outcomes. This suggests that MA type selection, despite the emphasis it receives in retail education, contributes almost nothing to strategy performance.
The non-parametric Kruskal-Wallis test, which makes no assumptions about the distribution of returns, confirmed this finding with an H-statistic of 11.2 and p-value of 0.047. Pairwise t-tests with Bonferroni correction for multiple comparisons found no statistically significant differences between any specific pair of MA types after adjustment.
Figures 10a through 10f break down performance by both strategy mode and asset class, allowing us to examine whether long-short strategies outperform long-only in any specific market. The answer is predominantly negative. Only in crude oil does the long-short approach show a meaningful advantage, likely reflecting the extended downtrend in oil prices during 2014-2016 and the COVID crash in 2020. For equities and currencies, long-only strategies dominate. This finding should give pause to retail traders who believe that adding short selling capability automatically improves their systems.
Figure 11 displays the twenty best-performing parameter combinations for the SPY equity index, ranked by Sharpe ratio. What immediately stands out is the diversity of configurations that achieved similar performance levels. The top entry uses EMA with periods 10 and 75, but configurations using SMA with periods 15 and 100, or WMA with periods 20 and 150, also appear in the top tier. This parameter space flatness, where many different combinations produce comparable results, is actually a positive sign. It suggests that the strategy may be somewhat robust to parameter selection, at least within certain ranges. However, the fact that the best Sharpe ratio barely exceeds 0.9, and that this represents in-sample optimization, means that out-of-sample performance will likely degrade substantially.
Figures 12a through 12e compare strategy performance across the four currency pairs tested: EUR/USD, GBP/USD, USD/JPY, and AUD/USD. The results are uniformly disappointing. No currency pair produced a best Sharpe ratio above 0.6, and the median performance across all configurations hovers near zero. This aligns with academic research showing that currency markets, being highly efficient and dominated by large institutional participants, offer fewer exploitable trends than equity or commodity markets (Moskowitz, Ooi, and Pedersen, 2012). The frequent intervention by central banks, which can abruptly reverse currency trends, further complicates trend following in this asset class. Retail traders who attempt to apply equity market trend following techniques directly to currencies without understanding these structural differences are likely to experience frustration.
Figures 13a through 13c examine performance in the three commodity instruments: gold, crude oil, and silver. Gold shows the strongest results, with a best Sharpe ratio of 0.68, while crude oil and silver both cluster around 0.5. The superior performance in gold may relate to its dual role as both a commodity and a monetary asset, creating more persistent trends than pure industrial commodities. However, even gold's best configuration falls short of what institutional investors would consider acceptable for a standalone strategy. The wide dispersion of outcomes within each commodity, visible in the heatmaps, further emphasizes the parameter sensitivity problem that plagues these approaches.
Figure 14 presents a detailed sensitivity analysis showing how strategy performance varies with the choice of fast and slow moving average periods for the SPY equity index. The subplots display the mean Sharpe ratio, with error bars showing one standard deviation, for different period choices. The fast period sensitivity shows performance peaking around 10 to 15 days, then declining as the period increases. The slow period sensitivity reveals a more complex pattern, with local optima around 75 and 150 days. However, the error bars are substantial, indicating high variance in outcomes. This uncertainty in optimal parameter selection is precisely why institutional traders employ ensemble methods rather than attempting to identify a single best configuration.
Figures 15a through 15c display histograms showing the distribution of key performance metrics across all strategy configurations. The Sharpe ratio distribution reveals a roughly normal shape centered slightly above zero, with a long tail extending to positive values. The maximum drawdown distribution shows that a substantial fraction of configurations experienced drawdowns exceeding 30 percent, with some exceeding 50 percent. The win rate distribution clusters around 45 to 55 percent, indicating that most configurations are only slightly better than random. These distributions collectively paint a picture of strategies that occasionally produce attractive risk-adjusted returns but more often produce mediocre or negative results, with significant tail risk in the form of large drawdowns.
7. Alternative Professional Trend Following Methodologies
Beyond regression-based approaches, institutional trend followers employ several other sophisticated techniques that bear little resemblance to retail moving average systems. Understanding these methods provides insight into the true complexity of professional trend following.
The Hodrick-Prescott filter, originally developed for macroeconomic time series analysis (Hodrick and Prescott, 1997), decomposes price series into trend and cyclical components through a penalized least squares optimization. The trend component T(t) minimizes:
sum((P(t) - T(t))^2) + lambda x sum((T(t+1) - T(t)) - (T(t) - T(t-1)))^2
Where lambda is a smoothing parameter, typically set to 129,600 for daily data. The first term penalizes deviations from the observed price, while the second term penalizes changes in the trend's growth rate, creating a smooth trend estimate. Trend following signals are generated when the filtered trend changes direction, with position sizes scaled by the magnitude of the trend acceleration. This approach, while computationally intensive, produces smoother signals than moving averages and reduces false breakouts during choppy markets.
Donchian channel breakouts, while conceptually simple, become sophisticated when implemented as multi-horizon ensembles with volatility scaling. Rather than using fixed 20-day or 55-day channels as retail traders do, professional implementations simultaneously monitor breakouts across 20, 50, 100, and 200-day channels. Signals are weighted by the channel width relative to recent volatility, with wider channels relative to volatility producing stronger signals. The ensemble signal becomes:
Signal = sum(w_i x (P(t) - Channel_Low_i) / (Channel_High_i - Channel_Low_i))
Where w_i are horizon-specific weights optimized through walk-forward analysis. This multi-timeframe approach captures trends operating at different scales simultaneously, a crucial advantage over single-horizon methods.
Ehlers filters, developed specifically for trading applications (Ehlers, 2001), use advanced digital signal processing techniques to extract trends while minimizing lag. The Super Smoother filter, for example, applies a two-pole Butterworth filter with adaptive cutoff frequency based on market volatility. The mathematical formulation involves complex frequency domain transformations that are beyond the scope of this paper, but the key insight is that these filters are designed to respond quickly to genuine trend changes while filtering out noise, achieving a better trade-off between responsiveness and stability than traditional moving averages.
The CUSUM drift detector provides a statistical framework for identifying regime changes (Page, 1954). The cumulative sum statistic is calculated as:
S(t) = max(0, S(t-1) + (r(t) - k))
Where r(t) is the return at time t and k is a drift parameter, typically set to half the expected return during a trend. When S(t) exceeds a threshold h, a trend is declared. This approach has the advantage of providing explicit statistical control over false positive rates, unlike moving average crossovers which have no such theoretical foundation.
Each of these methods addresses specific weaknesses in simple moving average approaches. Regression-based methods provide statistical significance testing. HP filters produce smoother trends. Donchian ensembles capture multi-scale trends. Ehlers filters minimize lag. CUSUM detectors provide statistical rigor. Professional implementations typically combine multiple methods, weighting their signals based on recent performance and market regime indicators.
Figure 16 conceptually illustrates the difference between retail and professional trend following. The retail approach, represented by a simple moving average crossover, produces binary signals with no statistical foundation and consists of merely four steps: price data, MA calculation, crossover detection, and trade execution. The professional approach incorporates seven distinct processing stages: multi-asset data ingestion, multiple parallel signal generators (regression-based, multi-horizon ensemble, and DSP filters), statistical significance testing and signal aggregation, volatility scaling and dynamic position sizing, correlation-based portfolio construction, risk limits and drawdown controls, and finally trade execution. The key insight is that professional trend following is not merely a more sophisticated version of retail trend following, but an entirely different approach that happens to share the same name.
8. The Path Forward
If simple moving average strategies fail to deliver consistent risk-adjusted returns, what alternatives exist for traders seeking systematic trend following approaches?
The first step is accepting that profitable trend following requires substantially more infrastructure than drawing two lines on a chart. The successful systematic trading firms operate research teams, maintain massive databases of historical prices, and continuously refine their models. They accept that any given strategy may underperform for years while maintaining confidence in the long-term statistical edge.
For individual traders without institutional resources, several paths remain viable. The first is specialization. Rather than attempting to trade multiple asset classes with a single methodology, focus on deep understanding of one market. The inefficiencies that persist today are subtle and require expertise to exploit.
The second is ensemble approaches. Rather than selecting one MA type and one parameter combination, implement multiple variations and combine their signals. This diversification across methodologies reduces the variance of outcomes and the dependence on any single backtest.
The third is incorporation of additional factors. Pure price trend is just one source of potential edge. Professional trend followers combine momentum signals with carry, the interest rate differential across currencies, with value measures, and with volatility signals. Academic research by Hurst, Ooi, and Pedersen (2017) demonstrates that multi-factor approaches produce more stable returns than any single factor in isolation.
The fourth and perhaps most important path is realistic expectation setting. Even the most successful trend following funds experience extended drawdowns and periods of underperformance. The AQR Managed Futures Strategy Fund, one of the largest trend following vehicles available to retail investors, lost money in 2009, 2010, 2011, 2012, 2016, 2017, 2018, and 2021. Seven losing years out of thirteen. Yet the strategy remains viable because the winning years, particularly 2008 and 2022, produced exceptional returns that more than compensated.
9. Conclusion
This study systematically evaluated over fifty thousand configurations of moving average trend following strategies across multiple asset classes, MA types, and trading modes. The results conclusively demonstrate that the simple approaches promoted in retail trading education fail to produce reliable risk-adjusted returns after accounting for transaction costs and multiple testing biases.
The gap between what retail traders believe about trend following and what professional systematic traders actually implement is vast. Retail approaches treat the entry signal as the complete system. Professional approaches treat the signal as merely one component within a sophisticated framework encompassing position sizing, portfolio construction, risk management, and execution optimization.
This does not mean that trend following is without merit. Academic research documents persistent time series momentum across asset classes over multi-decade periods. Crisis alpha, the tendency of trend followers to profit during market dislocations, provides genuine diversification benefits for portfolios otherwise exposed to equity risk. The strategy has a legitimate economic basis in the behavioral tendencies of market participants to underreact to information initially and overreact subsequently.
However, capturing this edge requires moving beyond the oversimplified frameworks that dominate retail education. It requires accepting that profitable trading is difficult, that edges are small and unstable, and that consistent success demands continuous adaptation and rigorous analysis.
The trader who approaches markets with humility, armed with statistical tools rather than certainty, stands a far better chance than one who believes two moving average lines hold the secret to wealth. No evidence, no trade. That principle, applied ruthlessly to every strategy and every assumption, separates the survivors from the casualties in the long game of systematic trading.
References
Baz, J., Granger, N., Harvey, C.R., Le Roux, N. and Rattray, S. (2015) 'Dissecting Investment Strategies in the Cross Section and Time Series', Working Paper, Man AHL.
Carver, R. (2015) Systematic Trading: A Unique New Method for Designing Trading and Investing Systems. Petersfield: Harriman House.
Ehlers, J.F. (2001) Rocket Science for Traders: Digital Signal Processing Applications. New York: John Wiley and Sons.
Hodrick, R.J. and Prescott, E.C. (1997) 'Postwar U.S. Business Cycles: An Empirical Investigation', Journal of Money, Credit and Banking, 29(1), pp. 1-16.
Hurst, B., Ooi, Y.H. and Pedersen, L.H. (2017) 'A Century of Evidence on Trend-Following Investing', Journal of Portfolio Management, 44(1), pp. 15-29.
Lopez de Prado, M. (2016) 'Building Diversified Portfolios that Outperform Out of Sample', Journal of Portfolio Management, 42(4), pp. 59-69.
Moskowitz, T.J., Ooi, Y.H. and Pedersen, L.H. (2012) 'Time Series Momentum', Journal of Financial Economics, 104(2), pp. 228-250.
Page, E.S. (1954) 'Continuous Inspection Schemes', Biometrika, 41(1/2), pp. 100-115.
MASTERING RISK MANAGEMENT: THE SURVIVAL SYSTEM FOR TRADERSRisk management is not just a safety net; it is the specific system used to control losses and protect your trading capital. Without a strict risk plan, even a highly profitable strategy will eventually fail. A few bad trades should never have the power to wipe out your account.
WHY IT IS CRUCIAL
Markets are inherently unpredictable. No matter how good the analysis is, probabilities dictate that losses will occur. Risk management:
1. Protects against emotional trading (fear and greed).
2. Ensures long-term survival so you can stay in the game long enough to be profitable.
3. Stabilizes your equity curve, avoiding massive drawdowns.
OUR CORE RISK RULES
1. PER TRADE RISK LIMIT
Never risk more than 0.7% to 2% of your total account balance on a single trade. This ensures that a losing streak does not destroy your capital.
Example:
If you have a $10,000 account, your maximum risk per trade should be between $70 and $200.
2. DAILY LOSS LIMIT
Do not open too many positions simultaneously. You must have a hard stop for the day. Your total daily loss limit should be a maximum of 15% of your portfolio. If you hit this limit, stop trading immediately for the day to prevent emotional revenge trading.
KEY TOOLS FOR RISK CONTROL
Use a Risk Calculator to automate your position sizing. Do not guess your lot size.
Stop Loss (SL): An order that automatically exits a losing trade at a specific price. This is your insurance policy. Never trade without it.
Take Profit (TP): An order that locks in gains at predefined levels.
Risk-to-Reward Ratio (RRR):
Always aim for 1:2 or better. This means if you are risking 50 pips/5%, your target should be at least 100 pips/10%. With a 1:2 ratio, you can be wrong 50% of the time and still be profitable.
ADVANCED TACTIC: MOVING STOP-LOSS TO ENTRY (BREAK-EVEN)
Moving the Stop-Loss to the Entry price is a technique used to eliminate risk exposure in an active trade. It involves adjusting your stop loss level to the exact price where you entered the market.
Why do this?
If the trade reverses against you after moving to entry, you lose $0. You have eliminated the risk while keeping the potential for profit open.
ADVANCED TACTIC: CLOSING PART OF A TRADE (PARTIALS)
You do not have to close 100% of a trade at once. Closing a portion (partial closing) is vital for managing psychology and banking revenue.
By taking profits on 50% or 75% of a position, you lock in gains immediately. You can then leave the remaining portion of the trade running to catch a larger trend with zero stress, as you have already banked profit.
COMING UP NEXT
In the next article, we will be diving into Types of Traders & Their Risk Management Styles
Disclaimer: This content is for educational purposes only and does not constitute financial advice. Trading involves significant risk.
- Tuffy (Team Mubite)
#RiskManagement #CapitalProtection #TradingSurvival #RiskReward
5 Must-Know Tips for Trading Gold. XAUUSD Must Know Secrets
After more than 9 years of Gold trading, I decided to reveal 5 essential trading tips , that will save you a lot of money, time and effort.
Of course, these trading recommendations won't make you rich, but they will certainly help you to avoid a lot of losing trades.
Whether you are new to Gold trading or an experienced trader, these insights will dramatically improve your trading.
Don't trade gold with a small account
I always repeat to my students that in gold trading, the risk per trade should not exceed 1% of a trading account.
It means that if your trades close with stop loss, you should lose maximum 1% of your deposit.
For the majority of the day trading and swing strategies, you will require at least 2000$ deposit to risk 1% per trade. Trading with a smaller account size, it will be challenging to follow this risk management principle of not exceeding 1%
Here is a day trade on Gold.
With a stop loss of 619 pips and a trading account of 10000$,
a lot size for this trade will be 0.02.
If the trade closes on stop loss, total risk will be 100$ or 1% of a trading account.
With a 100$ account, trading with a minimal lot 0.01, your potential risk will be 50$ or half of your trading account.
Check spreads
Spread may dramatically fluctuate on Gold.
High spreads can make it difficult for day traders to catch small price movements, reducing the profit potential of their trades.
Wide spreads can lead to slippage , where day traders may end up buying at a higher price and selling at a lower price than expected, increasing the risk of losses.
Gold has the lowest spreads during London and New York sessions,
while trading the Asian session is not recommended.
Personally, I don't trade Gold if the spread exceeds 100 pips.
In the picture above, you can see a current spread on Gold.
It is 30 pips. It is a relatively low spread, so we can trade.
Don't trade on US holidays
When US banks are closed, liquidity drops substantially on Gold.
It leads to increased spreads and higher probabilities of manipulations,
reduced volatility and very slow market.
For that reason, it is better not to trade Gold during US holidays.
You can easily find the calendar of US banking holidays on Google.
Simply take a break during these trading days.
Don't trade ahead of important US news
US news may dramatically affect Gold prices.
Such events as FOMC or FED Interest rate decision may trigger a high volatility and very impulsive movements.
My recommendations to you is to stay away from trading Gold one hour ahead of the important news releases.
You can find important US news in the economic calendar .
Just sort out the calendar in a way that it would display only significant news and pay attention to them.
Above, you can see the important US news for the coming days in the economic calendar.
Do not open multiple orders
Here is what many Gold traders do wrong:
once they place an order, instead of patiently waiting for a stop loss or take profit being reached, they start opening more orders.
Please, open one single trade per your prediction.
Open a new trade if only you see a new trading setup or your initial trade is already risk-free with a stop loss move to entry level.
Here is the example, a newbie trader decides to buy Gold and opens a long positions.
The market moves in the projected direction, and a trader opens one more trade.
The one can open even dozens of positions like that.
However, the problem is that the market can always suddenly reverse and all these trades will be closed in a loss.
It can lead to a substantial account drawdown.
Open a one single trading position instead.
I truly believe that these trading tips will help you improve your gold trading. Carefully embed these rules in your trading plan and watch how your trading performance improves.
❤️Please, support my work with like, thank you!❤️
I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis.
A Honest Annual Trading Review: Losses, Lessons, and 2026It’s December 11th, and there are maybe ten real trading days left in the year. At this point, there isn’t much more to do. The market won’t change my year, and I won’t change the market.
So it’s the right moment for an annual review.
I’m not the kind of trader who does weekly or even monthly “performance summaries” that don’t actually mean anything. For me, the only question that matters is this:
With how much did I start the year—and with how much am I ending it?
And after fourteen consecutive positive years, this is the year I end in the red.
So the question becomes: Why?
Why did I lose this year?
Before I dive into the lessons, the mistakes, and the changes I’ll implement starting in 2026, I need to give you some context—because no trading journey exists in isolation.
From 2002 to Today: A Long Road Filled With Luck, Lessons, and Reality
I began trading in 2002, investing in stocks right after the dot-com bubble. And things went incredibly well— not because I was smart, not because I understood markets, but because I had one of the greatest advantages a trader can have:
Perfect timing after a major market collapse.
In other words: pure luck.
In 2004 I discovered Forex, and by 2007 I had shifted entirely to Forex trading.
Until 2009, everything worked almost effortlessly. Every year was green. Even the 2008 crisis was profitable for me—I happened to hold some exceptional short positions.
And then came 2009.
The market didn’t humble me. My own arrogance did.
“ I can’t be wrong. I predicted the 2008 crash. I see the market clearly. I’ve got this.”
That mindset cost me 50% of everything I had accumulated.
That was my first real wake-up call.
It forced me to understand a truth that every long-term trader eventually learns, one way or another:
Humility in front of the market is not optional. It is survival.
That realization became the first major shift in how I approach trading.
What Changed After 2009: A Short Summary of a Long Transformation
As a brief summary of what shifted after 2009—beyond drastically reducing my appetite for risk—the biggest change was my transition toward pure price action and swing trading as the foundation of my approach.
Before that, the market felt almost binary, almost predictable.
- If NFP came in above expectations, the USD strengthened—and it stayed strong, not just for a few intraday spikes.
- When Hurricane Katrina hit, the narrative was straightforward: weak USD.
- Carry trade on JPY was the play all the way until 2008, so buy every substantial dip
- Breakouts were real breakouts—not whatever we have today, with fakeouts layered on fakeouts.
It was a different environment.
Cleaner. More directional. More narrative-driven.
And I traded it exactly as it was.
But markets evolve, and if you don’t evolve with them, you get left behind.
So I adapted.
I shifted from being a trader who reacted to news flows and macro momentum to a trader who reads structure, context, and price behavior first.
I shifted from chasing moves to waiting for high-probability rotations.
I shifted from assuming I understand the market to accepting that the market owes me nothing and can invalidate my ideas at any moment.
There’s much more to say about that transition—how painful it was, how long it took, and how it changed the way I think not just about trading, but about myself. But that’s a story for another time.
For now, it’s enough to say this:
2009 forced me to mature as a trader.
What followed shaped the next decade and a half.
It’s Not About Trump, and It’s Not About Excuses
This isn’t about Trump coming to the White House.
This isn’t about macro narratives or politics.
Yes, the markets did shift around that period — but this article is not about searching for excuses.
Because when it comes to Forex and XAUUSD, I managed the environment just fine.
I adjusted. I adapted. I traded often from instinct shaped by experience, and overall, that part of my trading year held up.
What dragged my year down — completely and undeniably — were my crypto investments.
I Was Never a “To-the-Moon” Guy — And Still Lost Substantially
I’ve never been a moonboy.
I’ve always been realistic with my targets: soft, achievable gains in the 30–50% range.
I never believed in the mythical “altcoin season.” I said repeatedly that it was wishful thinking and that the glory of past cycles would not repeat.
I didn’t gamble on new projects, I didn’t throw money at memes, and I didn’t YOLO into narratives.
And yet — I still lost.
So why?
Because I allocated too much capital, even within my fixed conservative approach.
Not because I believed in altcoin season, but because I believed we would see a meaningful recovery in the autumn.
I sized like someone expecting a bounce.
When the bounce didn’t come, instead, the flash crush from October, the weighting crushed the year( BTW, I wasn't leveraged)
Simple as that.
What I Will Change in 2026 (Crypto Edition)
The fix is straightforward:
- No more long-term investing in crypto, regardless of narrative.
- Maximum time exposure: a few days, maybe a few weeks.
- Stick strictly to major, established projects.
- Trade only what behaves cleanly from a technical perspective.
In other words, crypto will no longer be a long-term play in my portfolio.
It will be treated exactly as I should've be treated it from the beginning:
a short-term speculative instrument — nothing more, nothing less.
Forex and XAU/USD / XAG/USD: The Adjustments Going Into 2026
On the Forex and metals side, the changes are more nuanced — and in some ways, more strategic.
The core shift is this: shorter-term focus, smaller targets on Forex, larger targets on Gold, and a more active approach on Silver.
Here’s the breakdown:
1. Smaller Targets in Forex (EUR/USD as the Example)
In previous years, a 200–250 pip target on EUR/USD was perfectly reasonable.
The volatility allowed it, the market structure supported it, and the flow followed through.
But today, that kind of moves — consistently — is simply not realistic (look at it in the past 6 months).
So the adjustment is straightforward:
From 200–250 pip targets → to sub-100 pip targets.
It’s not about aiming lower.
It’s about aligning targets with actual market behavior, not nostalgia for a volatility regime that no longer exists.
2. Larger Targets on Gold (Because the Volatility Demands It)
Gold is the opposite story.
Volatility has exploded, rotations are massive, liquidity pockets run deep, and intraday swings are two or three times what they used to be.
So the shift here is:
From 300–400 → to 500+ being the new standard.
You can’t trade for 50-100 pips an instrument that behaves like a hurricane.
You adapt to its nature — or it eats you alive.
3. A More Active Approach on Silver (XAG/USD)
Silver has become a much more attractive instrument for me:
- Cleaner technical behavior
- Larger relative percentage moves
- Alignment with Gold, but with more exploitable inefficiencies
So 2026 will include more active trading on XAGUSD, treating it as a strategic middle ground between Forex and Gold volatility.
4. Integrating More ICT/SMC Into My Framework
Another important change is methodological:
I’ll incorporate more ICT/Smart Money Concepts into my analysis and execution.
Not as a religious shift — I’m not replacing classical TA and price action — but as an enhancement.
SMC concepts:
- map exceptionally well onto today’s liquidity-driven markets
- clarify sweeps, inducement, fakeouts
- explain displacement and rebalancing
- blend naturally with the price action approach I already use
In other words, this is not a stylistic change — it’s an upgrade of the internal framework.
Price action stays.
Classical TA stays.
But SMC becomes a bigger part of the decision-making process.
What This All Means for 2026: A Cleaner, Tighter, More Adapted System
When you put all these adjustments together — the crypto restructuring, the refined Forex targets, the larger Gold plays, the increased activity on Silver, and the deeper integration of SMC — the message becomes clear:
2026 won’t be about reinventing myself.
It will be about refining myself.
This year wasn’t a catastrophe ( around 15% loss overall)
It wasn’t an identity crisis.
It was a recalibration — a reminder that longevity in trading is not about perfection, but adaptation.
I didn’t lose because I became worse.
I lost because my allocation in one corner of my portfolio didn’t match the reality of the market.
And the only unforgivable mistake in trading is refusing to learn from the forgivable ones.
The markets haven’t betrayed me.
Crypto hasn’t betrayed me.
Forex and metals haven’t betrayed me.
The responsibility is mine — and so is the path forward.
In 2026, my system becomes:
- Simpler — fewer narratives, more structure.
- Tighter — smaller Forex targets.
- More opportunistic — bigger Gold moves, active Silver plays, short-term crypto speculation.
More aligned with how markets actually behave, not how past versions of me used to trade them.
And that’s the real conclusion of this year:
After almost 25 years in the markets, the only edge that never expires is the willingness to evolve.
Some years, you win because you’re right.
Some years, because you're lucky.
Some years you lose because you’re human.
But the trader who survives is the trader who adapts — again and again, without ego, without excuses.
And that’s exactly what 2026 will be about.
P.S:
And One More Thing… I Kind of Expected This After 14 Years
If I’m being completely honest, part of me always knew this moment would come.
You don’t go fourteen consecutive years without a losing one and expect the streak to last forever.
Statistically, psychologically, realistically — a red year was inevitable at some point.
So no, this wasn’t a shock.
It wasn’t a dramatic fall from grace.
It was simply… the year that was eventually going to arrive.
And that’s actually liberating!:)
Because once you accept that even long-term consistency includes the occasional step backward, you also see the bigger picture clearly:
This year doesn’t define me — the next one will.
Radio Yerevan: Is Crypto the Biggest Wealth Transfer in History?Answer: Yes. But not in the direction people hope.
In the last decade, crypto marketing has repeated one grand promise:
“This is the biggest wealth transfer in human history!”
And in classic Radio Yerevan fashion, this statement is both true and misleading.
Yes — a historic wealth transfer took place.
No — it did not empower the average investor.
Instead, it efficiently moved wealth from retail… back to the very entities retail thought it was escaping from.
Let’s break it down: structured, clear, and with just the right amount of irony.
1. The Myth: A Decentralized Financial Uprising
The early crypto narrative was simple and beautiful:
- The people would reclaim financial independence.
- The system would decentralize power.
- Wealth would flow from institutions to individuals.
The idea was inspiring — almost revolutionary.
Reality check: Revolutions are expensive.
And someone has to pay the bill.
In crypto’s case, the average investor volunteered enthusiastically.
2. The Mechanism: How the Transfer Actually Happened
To call crypto a wealth transfer is not an exaggeration.
The numbers speak loudly:
Total market cap peaked above $3+ trillion.
Most of the profit was extracted by:
- VCs who bought early,
- teams with massive token allocations,
- exchanges capturing fees on every trade,
- and whales who mastered liquidity cycles.
Retail investors, meanwhile, contributed:
- capital,
- liquidity,
- hope,
- hype
- and a remarkable tolerance for drawdowns.
It was, in essence, the perfect economic loop:
money flowed from millions → to a concentrated few → exactly like in traditional finance, only faster and with better memes.
3. The Irony: A Centralized Outcome From a Decentralized Dream
Here lies the great contradiction:
Crypto promised decentralization. Tokenomics delivered centralization.
When 5 wallets hold 60% of a token’s supply, you don’t need conspiracy theories — you need a calculator.
The “revolution” looked more like:
- Decentralized marketing
- Centralized ownership
- Retail-funded exits
- And a financial system where “freedom” was defined by unlock schedules and vesting cliffs
But packaged correctly, even a dump can look like innovation.
4. Why Retail Was Doomed From the Start
Not because people are unintelligent, but because:
- No one reads tokenomics.
- Unlock calendars sound boring.
- Supply distribution charts kill the romance.
- Liquidity mechanics are not as exciting as „next 100x gem”.
- And hype travels faster than math.
In a speculative market, psychology beats fundamentals until the moment fundamentals matter again — usually when it's too late.
5. The Real Wealth Transfer: From “Us” to “Them”
The slogan said:
“Crypto will redistribute wealth to the people!”
The chart said:
“Thank you for your liquidity, dear people.”
The actual transfer looked like this:
- Retail bought the story.
- Institutions created the tokens.
- Retail bought the bags.
- Institutions sold the bags.
- Retail called it a correction.
- Institutions called it a cycle.
Everyone had a term for it.
Only one group had consistent profits from it.
6. So, Was It the Biggest Wealth Transfer in History?
Yes.
But not because it made the average investor rich.
It was the biggest because:
- no previous financial system mobilized so many people
- so quickly
- with so little due diligence
- to transfer so much capital
- to so few beneficiaries
- under the banner of liberation.
It wasn’t a scam.
It wasn’t a conspiracy.
It was simply financial physics meeting human psychology.
7. The Lesson: Crypto Isn’t the Problem — Expectations Are
- Blockchain remains a brilliant invention.
- Tokenization has real use cases.
- DeFi is a groundbreaking paradigm.
- And so on
The issue wasn’t the technology.
It was the narrative that convinced people that buying a token was equivalent to buying financial freedom.
Real freedom comes from:
- understanding liquidity,
- reading tokenomics,
- respecting supply dynamics,
- and asking the only question that matters:
“If I’m buying… who is selling?”
In markets — especially crypto — this question is worth more than any airdrop.
8. Final Radio Yerevan Clarification
Question: Will the next crypto cycle finally deliver the wealth transfer to the masses?
Answer: In principle, yes.
In practice… only if the masses stop donating liquidity.
How to Use ATR in TradingViewMaster ATR using TradingView's powerful charting tools in this step-by-step tutorial from Optimus Futures.
ATR, or Average True Range, is a volatility indicator that helps traders measure market movement, set appropriate stop losses, and adjust position sizing based on current market conditions.
What You'll Learn:
Understanding ATR as a volatility measurement tool that tracks price movement regardless of direction
How ATR calculates the average range between highs and lows over a specified period — typically 14
Why rising ATR signals increasing volatility and larger price swings
Why falling ATR indicates decreasing volatility and quieter market conditions
Using ATR to set dynamic stop losses that adjust to current volatility rather than arbitrary dollar amounts
How to calculate stop distances by multiplying ATR by factors like 2x or 3x
Applying ATR for position sizing to maintain consistent risk across different volatility environments
Setting profit targets based on ATR multiples to align with actual market movement
Filtering trade setups using ATR levels to avoid low-volatility periods or confirm breakout momentum
How to add ATR on TradingView via the Indicators menu
Understanding the default 14-period setting and how shorter or longer periods affect responsiveness
Practical examples using the E-mini S&P 500 futures chart
Applying ATR across daily, weekly, and intraday timeframes for risk management and trade planning
This tutorial is designed for futures traders, swing traders, and risk-focused analysts who want to integrate volatility-based risk management into their trading approach.
The methods discussed may help you set smarter stops, size positions appropriately, and adapt your trading strategy to changing market conditions across multiple markets and timeframes.
Learn more about futures trading with TradingView: optimusfutures.com
Disclaimer
There is a substantial risk of loss in futures trading. Past performance is not indicative of future results. Please trade only with risk capital.
We are not responsible for any third-party links, comments, or content shared on TradingView. Any opinions, links, or messages posted by users on TradingView do not represent our views or recommendations.
Please exercise your own judgment and due diligence when engaging with any external content or user commentary.
This video represents the opinion of Optimus Futures and is intended for educational purposes only. Chart interpretations are presented solely to illustrate objective technical concepts and should not be viewed as predictive of future market behavior.
In our opinion, charts are analytical tools, not forecasting instruments.
Risk Management Basics 95% of Traders IgnoreWhen traders try to improve their results, they often jump straight to indicators, new setups, or refined entries.
But here’s the uncomfortable truth:
Most traders don’t fail because of their strategy — they fail because they don’t control their risk.
Let’s break down the two fundamentals that separate professionals from the 95%:
1️⃣ The 1% Rule: Your Built-In Survival System
Most beginners risk 5–20% per trade.
Professionals risk a maximum of 1%. Why?
Because the goal isn’t to win every trade — the goal is to stay in the game long enough for your edge to play out.
Risking only 1% means:
✔ A losing streak won’t destroy your account
✔ Your emotions stay stable and rational
✔ Your system has room to unfold statistically
✔ You avoid the #1 account killer: overexposure
Here’s the key mindset shift:
Risk management is not about fear — it’s about increasing your probability of long-term profitability.
2️⃣ Positive Expectancy: The Math Behind Winning Traders
Most traders judge a setup based on the last one or two trades.
Professionals evaluate it based on expectancy — the average profit per trade across a large sample.
Here’s a simple example:
Win rate: 40%
Average win: +60 pips
Average loss: –30 pips
Expectancy =
(0.4 × 60) – (0.6 × 30) = +6 pips per trade
Meaning:
You can lose more trades than you win — and still be profitable.
This is the principle beginners never understand.
A system with positive expectancy + 1% risk per trade becomes extremely powerful.
You stop caring about individual losses and start thinking in probabilities, not emotions.
The Truth Most Traders Miss
➡️ Risk management is the strategy.
➡️ Expectancy matters more than your win rate.
➡️ Risking 1% won’t make you rich fast — but it will prevent you from blowing up.
➡️ Trading becomes easier when you remove the illusion of certainty.
If traders spent more time understanding expectancy and risk instead of chasing “perfect setups,” half of their frustration would disappear overnight.
Thanks for reading — and have a disciplined start to your trading week!
If you found this post valuable, let me know in the comments.
I might create a full series on applied risk management and expectancy modeling.
Jonas Lumpp
Speechless Trading
Disclaimer: This tutorial is for educational purposes only and does not constitute financial advice. Its goal is to help traders develop a professional mindset, improve risk management, and make more structured trading decisions.
AI Revolution: How the Retail Trader Can Finally WinA step-by-step guide for traders who want to stop staring at charts and start letting AI do the heavy lifting.
For years, trading meant one thing:
Sit at your desk.
Stare at charts.
Wait.
Hope.
React.
Repeat.
But in 2025, that’s ancient history.
AI has changed everything.
Now any retail trader — even a complete beginner — can create a TradingView strategy, test it, refine it, and fully automate execution to MT5 or cTrader using webhooks… without writing a single line of code.
If you can type instructions, you can build an automated trading system.
Here’s the full blueprint — updated with the crucial Step 0 that most people don’t even know exists.
⭐ STEP 0 — Build Your Master AI Prompt (The Secret Weapon)
Before you write a single strategy rule…
Before you ask AI to code…
Before you try to automate anything…
You MUST build a Master Prompt.
This is the “operating system” for the AI — it tells the model:
how to write the Pine Script
how to structure entries & exits
how to format alerts
how to avoid compile errors
how to respond when you paste broken code
how to preserve your logic perfectly
Without a Master Prompt, AI guesses.
With a Master Prompt, AI produces clean, professional, error-free trading systems consistently.
Here’s the master prompt you’ll use:
🔥 MASTER PROMPT (Copy + Paste Into ChatGPT Before Giving Your Strategy Rules)
You are now my expert TradingView Pine Script v5 strategist, quant developer, and compiler-level debugging assistant.
Your job is to:
1. Build a complete TradingView strategy() script based on the rules I give you.
2. Ensure the script compiles with ZERO errors.
3. Write clean, structured, commented code using professional conventions.
4. Include:
– strategy.entry()
– strategy.exit() with SL & TP
– Input parameters
– alertcondition() for webhook automation
5. Structure alerts so they work with strategy.order.action.
6. NEVER change my trading logic. Follow it EXACTLY.
7. If the code fails to compile:
– Identify the REAL root cause
– Fix only what’s necessary
– Return a fully corrected script
8. When I ask for improvements, optimize the code without altering the core idea.
After loading this master prompt, wait for my rules before generating the strategy.
Now your AI assistant is fully “trained” before it begins coding.
Once Step 0 is done?
The real fun begins.
🚀 STEP 1 — Decide What You Want Your Strategy To Do
Define the basics:
What triggers your entry?
What ends the trade?
What confirms the setup?
How much risk?
Example simple idea:
Buy when price closes above the 20 EMA after RSI oversold.
Sell when price closes below the 20 EMA after RSI overbought.
Stop = 1 ATR.
Take profit = 2 ATR.
Once you define this?
You're ready for the AI to code it.
🤖 STEP 2 — Use AI to Turn Your Idea Into a TradingView Strategy
Paste your Master Prompt.
Then paste your rules.
Example instruction:
“Build the strategy using my Master Prompt.
Here are the rules…”
AI outputs a ready-to-paste Pine Script.
If it errors?
You tell it:
“Fix all compile errors without changing my trading logic.”
This is the magic of Step 0 — the AI already understands exactly how to fix your code properly.
📊 STEP 3 — Backtest Directly on TradingView
Paste the script.
Add to chart.
Open Strategy Tester.
Check:
Win rate
Drawdown
Profit factor
Stability
Number of trades
If it sucks?
Ask AI:
“Improve this strategy’s performance. Keep the overall concept but add filters.”
AI gives you Version 2.
⚙️ STEP 4 — Turn Your Strategy Into Webhook Alerts
Click Alerts → Condition → Your Strategy Name
Choose:
Strategy Entry Long
Strategy Exit Long
Strategy Entry Short
Strategy Exit Short
Turn on Webhook URL.
Use structured JSON:
{
"signal": "{{strategy.order.action}}",
"symbol": "{{ticker}}",
"price": "{{close}}",
"position_size": "0.10"
}
Now TradingView is alert-ready.
🌐 STEP 5 — Send Alerts to MT5 or cTrader Using Webhooks
You need a bridge.
Best options:
PineConnector
TradeConnector
cTrader Open API bot
Make/Zapier → Python Server → MT5 EA
Example webhook:
{
"action": "BUY",
"symbol": "XAUUSD",
"lot": 0.10,
"sl": 50,
"tp": 100
}
🧠 STEP 6 — Use AI to Build the MT5 or cTrader Execution Robot
If you want a custom bot instead of PineConnector:
Ask:
“Write an MT5 EA that receives webhook commands in JSON format and executes market orders with SL and TP.”
Or:
“Write a cTrader cBot that listens for webhook signals and places trades automatically.”
AI builds your execution engine.
🔁 STEP 7 — Your Fully Automated Trading Pipeline
STEP 0 — Build your Master AI Prompt
STEP 1 — Define your strategy
STEP 2 — AI generates TradingView strategy
STEP 3 — Backtest & refine
STEP 4 — Create alert webhooks
STEP 5 — Bridge → MT5/cTrader
STEP 6 — AI builds execution bot
STEP 7 — Enjoy hands-free AI-powered trading
🎯 Final Thoughts — This Is the New Era
The trader who wins is the one who:
uses AI
automates everything
removes emotion
builds systems, not guesses
executes consistently
Tools like TradingView + AI + MT5/cTrader automation are the biggest level-up in retail history.
And it all starts with:
STEP 0 — Build your Master Prompt.
Let the fun begin
Get Funded and make $20 000 Monthly. Complete plan for 2026.Hey traders let's have a look at prop trading again. It's a great opportunity for the skilled traders who has good strategy, discipline and mastered risk management. Let's start with the numbers which many traders and misunderstood.
📌 Prop firm facts
- $100K account with 10% max drawdown means you got $10K account, not $100K
- Goal of 10% to pass phase 1 while you can risk 10% means 100% gain
- Goal of 5% to pass Phase 2 while you can risk 10% adds another 50% gain.
- You will literally be funded after making 150% not 10% and 5%
⁉️ Does it mean it's impossible to get funded ?
Yes it's possible, next to good strategy you need, discipline and mainly you just need to adjust your risk management. If you make 150% in year as a Hedge fund manager you will be a superstar trader. Yet people still want to pass prop challenge in a less than week or in a few trades which means not sticking to the risk management.
🔗 Click to the picture below to Learn more about Prop Risk management 📌 How to make $20 000 a month ? Magic of 3%
Yes, you actually need to make only a 3% a month. Is it difficult ? No, It's not. You need 3 wins with 1:2 RR while risking 0.5% Risk.
1️⃣Your Ultimate goal - -$100K Funded account - 3% Gain - 80% Profit split = $2400 Payout
2️⃣Let's take it to $20 000 a Month
Don't try to increase your % gains per month, increase your capital under management
- Get another 4 x $ 100K Challenges pass them - You will have $500K AUM:
- $ 500 000 - 3% Gain - 80% Profit split = $12 000
3️⃣Reinvest buy another 3 - 5 challenges aim for $ 1000 000 funded across few solid props firms. 🎯 $ 1000 000 - 3% gain - 80% Profit Split = $24 000 Payout
📌 Have a long term plan
this is not gonna happen in few months. It's a year plan - But you got this... 💪
With approximate cost of $500 - $600 per $100K challenge you will need to spend apron. $5500 to get $1000 000 funding. You will fail some, its unavoidable, so let's count with more might $10K. But still , you can start with first $100K an then reinvest to another challenges. You dont need $10K investment right now. But later this $10K and 3% gain and 80% profit split is $24 000, even more then $20K.
📌 Difficulty is not technical, but in patience
I speak from experiences that my biggest mistakes was trying to pass quickly or when I was in drawdown I started to gamble. Be patient and stick to the rules. If we stick to 3% a month without progressive risk management it would be 4 months to get funded. If you do progressive risk management you can do it faster, and once you are confident you can run multiple challenges at the same time.
📌 Long term plan requires perfect planning
Find 60 minutes just for yourself and this about these questions below, write the answers to to the paper, think about the execution of your project. I know you didn't do it now, but come back to this and do it again. You need to visualize your future successful yourself and remind that visualization every day. I recommend a book - Psycho-cybernetics from Maxwell Maltz it will help you define your self-image of successful trader in the fact this book will change your life.
📌 Essential Rules for Prop Trading
-Its not a straight forward game
-Reduce number of trades - Only A+ Setups
- Grow Your Capital Under management in multiple firms not % gains
- 3% is a golden profit in prop space to live from trading
❌ Dont do this
If you don't trade well on small account, getting prop firm will not change it.
Don't expect it to be a solution to bad financial situation. It's extension. 🧪 Trading is not hard we often overcomplicate it
I believe you already few great trades in a month, but you also have many unnecessary ones, look at your last few month results and check if would be able to make 3% if you excluded those unnecessary trades. I sure you could ant thats what you have to do
Switch from machine gunner to a Sniper.
Write this on a paper and put it somewhere so you see it every day.
🎯 $ 1000 000 - 3% gain - 80% Profit Split = $24 000 Payout
🎯 $ 1000 000 - 3% gain - 80% Profit Split = $24 000 Payout
🎯 $ 1000 000 - 3% gain - 80% Profit Split = $24 000 Payout
$1000 000 Funding !! - Your ultimate goal for 2026 💪
I promised myself I’d become the person I once needed the most as a beginner. Below are links to a powerful lessons I shared on Tradingview. Hope it can help you avoid years of trial and error I went thru.
📊 Sharpen your trading Strategy
⚙️ 100% Mechanical System - Complete Strategy
🔁 Daily Bias – Continuation
🔄 Daily Bias – Reversal
🧱 Key Level – Order Block
📉 How to Buy Lows and Sell Highs
🎯 Dealing Range – Enter on pullbacks
💧 Liquidity – Basics to understand
🕒 Timeframe Alignments
🚫 Market Narratives – Avoid traps
🐢 Turtle Soup Master – High reward method
🧘 How to stop overcomplicating trading
🕰️ Day Trading Cheat Code – Sessions
🇬🇧 London Session Trading
🔍 SMT Divergence – Secret Smart Money signal
📐 Standard Deviations – Predict future targets
🎣 Stop Hunt Trading
🧠 Level Up your Mindset
🛕 Monk Mode – Transition from 9–5 to full-time trading
⚠️ Trading Enemies – Habits that destroy success
🔄 Trader’s Routine – Build discipline daily
🛡️ Risk Management
🏦 Risk Management for Prop Trading
📏 Risk in % or Fixed Position Size
🔐 Risk Per Trade – Keep consistency
When to Trade — When to Stay OutWhen to Trade — When to Stay Out: A Deep, Practical Guide for Traders
Timing is a core edge. Not every hour, session, or chart condition is trade-worthy. The difference between a profitable trader and an active losing trader is not how many trades they take — it’s which trades they take and when. This article gives you a detailed, systematic framework to decide when to trade and when to stay out, with concrete rules, time windows, checklists and worked examples.
Big-picture logic
Markets are driven by liquidity (where orders sit), volatility (how fast price moves) and participants (who is trading). Good timing aligns these three:
Liquidity concentration (institutions, marketmakers) produces cleaner, higher-probability moves.
Right volatility means enough movement to reach targets but not so much that stop losses are random.
Recognizable market structure (trends, ranges, breaks) allows rules to be applied consistently.
If any of the three is missing, edge declines and risk of random losses rises.
Session windows — when the market is most tradable
Below are standard session definitions in UTC+00:00. Adjust for daylight savings if required (noted where relevant).
Tokyo / Asian Session
⏵ UTC+00:00: 23:00 – 08:00 ( main liquidity often 23:00–02:00 UTC )
⏵ Characteristic: lower liquidity for major FX pairs, choppier price action. Exceptions: JPY crosses, pairs with Asia-led liquidity, and crypto (24/7).
London Session
⏵ UTC+00:00: 07:00 – 16:00 (most active 08:00–11:00 UTC)
⏵ Characteristic: heavy institutional flow, high liquidity. Many clear directional moves begin here.
New York Session
⏵ UTC+00:00: 12:00 – 21:00 (most active 13:00–16:00 UTC)
⏵ Characteristic: continuation or reversal of London moves; major news releases occur here.
Key overlap (best single window)
⏵ London–New York overlap: UTC+00:00 ~12:00–16:00. Highest combined liquidity and volatility; most “clean” trends and reliable breakouts occur here.
Rule of thumb: Prefer intraday trades during the London session and the London–New York overlap. Be selective in Asia unless trading JPY pairs or range-break strategies designed for low liquidity.
Concrete: Best times to trade (prioritized)
Session open impulse — first 60–120 minutes of London or New York sessions.
Overlap window — London + New York overlap (UTC+00:00 ~12:00–16:00).
Post-news verified moves — 10–30 minutes after high-impact macro prints, if market structure becomes clear and isn’t just noise.
Clear breakouts after consolidation during active sessions (volume confirmation, sweep of liquidity, not just a one-bar spike).
When to avoid trading (and why)
Low-volume Asian hours for majors — price tends to chop and give false signals.
Right before major macro releases (NFP, CPI, FOMC) — price can gap or spike unpredictably. Exceptions: defined volatility playbook with strict hedges.
Midday lulls after initial session impulse — often flat ranges and low edge.
On unclear structure / messy price action — wide, overlapping candles, no clear swing highs/lows.
During market holidays or early close days — liquidity is thin; spreads widen.
Pre-trade checklist
Time window OK? (London / NY open or high liquidity event)
Major news? (No significant release within ±30 mins)
Higher timeframe structure clear? (H4 or Daily trend / range)
Trade idea defined (entry, stop, target) — use price levels, not indicators only.
Risk per trade ≤ planned % of account (see position sizing).
Reward : Risk ≥ your minimum (e.g., 1.5–3:1 depending on edge).
Catastrophic stop capability confirmed (can you absorb worst-case slippage?)
Exit rules set (profit-taking scale or full exit)
Trade logged in journal immediately after (reason, setup, time, bias)
Position sizing — exact worked example (step-by-step)
Use a fixed % of equity for risk per trade (commonly 0.5%–2%). Example uses 1% risk.
Assume:
Account size = $10,000.
Risk per trade = 1% of account = $10,000 × 0.01.
We compute digit-by-digit: 10,000 × 0.01 = 100. So maximum $100 risk on this trade.
Generic position-size formula:
Position size (units) = (Account Size × Risk%) ÷ (Stop Distance in price units × Value per price unit per 1 unit)
Always recalc pip/value for cross rates and for instruments (stocks, futures, crypto) — adapt the “value per price unit” accordingly.
Money Management is much more important than a strategy. You should learn Money Management before trying any strategy.
Order types & execution rules
Limit entries at confluence levels (support/resistance + liquidity sweep zone) — better price and less slippage.
Stop orders for breakout entries — use when you want to enter only after momentum confirms.
OCO (One Cancels Other) for scaling / invalidation management — reduces manual errors.
Avoid market entries during major news due to slippage/gap risk, unless your plan accounts for it.
Trade management & exits
Initial target: defined by structure (previous swing, ATR multiples, measured moves).
Scale out: consider taking partial profits at the first reasonable target, let the rest run with a trailing stop.
Stop relocation: only move stop to breakeven after a predefined profit multiple reached (e.g., after +1R or after price clears a new structure). Don’t move stops based on emotion.
If price returns and breaks your entry zone invalidating the setup, exit — the market changed.
Strategy-specific timing tweaks
Trend-following: prefer strong sessions (London/NY) and avoid Asian low-liquidity hours. Enter on retracements that align with higher timeframe trend.
Range / mean-reversion: worst during session opens; best during mid-session lulls, but only if volatility is low and boundaries are clear.
Breakout strategies: require confirmation — e.g., breakout during overlap or accompanied by increased volume / volatility. Avoid breakouts in thin Asian hours.
News scalping: high risk; only for experienced traders with defined entry, strict spread/latency controls, and capital to absorb spikes.
Common mistakes (and how to fix them)
Trading outside your chosen time windows — fix: enforce a trading clock.
Overtrading in chop — fix: increase minimum R:R and wait for clear structure.
Ignoring spreads and liquidity — fix: include spread in stop/target math and avoid thin sessions.
Moving stops prematurely — fix: use rules (e.g., only move after +1R).
Trading news impulsively — fix: have a news plan: either avoid or have a predefined volatility playbook.
Emotional trading (e.g. not closing the position when the price hits stop-loss)
Psychological & routine rules
Trade only when rested and focused.
Limit screen time to your pre-set sessions.
Keep a journal: reason for trade, outcome, lessons. Review weekly.
Daily routine: pre-market scan 30–60 minutes before your active session, post-session journal entry.
FAQ
Q: Can I trade during Asian hours?
A: Yes — but selectively. Prefer JPY pairs, Asia-centric instruments, or strategies built for low volatility.
Q: What if my timeframe and session disagree?
A: Give priority to higher timeframe structure. If H4 / Daily shows trend, trade during active sessions for better fills.
Q: How much should I risk per trade?
A: Conservative traders use 0.5%–1% per trade. More aggressive ones use up to 2%. The key is consistency and drawdown planning.
Focus your trading during high-liquidity windows (London, New York, and their overlap), avoid low-volume and pre-news periods, always validate trades with liquidity + volatility + clear market structure, use strict risk management (e.g., 1% per trade with position sizing), and follow a pre-trade checklist to avoid low-quality setups. Better timing = better edge.
Enjoy!
Gold Forex Trading During Major Economic Events & News Releases
I guess you already noticed how impulsively the markets may react to economic events and news.
In this article, I will teach you a simple strategy to follow during important news release s and how to trade news.
1. Sort out the economic calendar
There are a lot of news in the economic calendar.
They are not equal in their impact.
Most of the economic calendars indicate the potential significance of each event: while some news have low importance, some have medium importance and some are considered to be extremely important.
For example, above is the list of coming UK fundamental news.
You can see that these news have different degree of importance.
My recommendation to you is to sort out the economic calendar in a way, so it would display only the most important news.
Among the news that we discussed above, only one release has high importance.
2. Know on what trading instruments does the news have an effect
While some of the news in the economic calendar may impact many financial markets and trading instruments, some news may affect very particular instruments.
For example, a FED Interest Rate decision may have a very broad effect on financial markets.
At the same time, Interest Rate Decision in Australia may affect only Australia - related instruments.
3. Don't trade one hour before the news and one hour after the release
Once you see the important fundamental news coming, don't trade the trading instruments that can be affected by the new s 1 hour before and after the release.
For example, in 5 minutes we are expecting important UK news - CPI data.
I stopped trading GBP pairs 1 hour before the release of the news, and will resume trading them one hour after the release.
4. Protect your trading positions 5 minutes ahead of the news
If you have an active trading position and related important news are expected, move your stop loss to entry 5 minutes ahead of the release of the news.
For example, I have a short trade on GBPAUD. I see that in 5 minutes important UK data is coming. I will move stop loss to entry 5 minutes ahead of the news and make a position risk-free.
I always say to my students, that news trading is very complicated. Due to a high volatility, it is very hard to make wise decision during the news releases.
The approach that I suggest will help you to avoid all that and trade the markets when they are calm.
❤️Please, support my work with like, thank you!❤️
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