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My home-made bar replay for MT4

I made a home-made bar replay for MT4 as an alternative to the tradingview bar replay. You can change timeframes and use objects easily. It just uses vertical lines to block the future candles. Then it adjusts the vertical lines when you change zoom or time frames to keep the "future" bars hidden.
I am not a professional coder so this is not as robust as something like Soft4fx or Forex Tester. But for me it gets the job done and is very convenient. Maybe you will find some benefit from it.

Here are the steps to use it:
1) copy the text from the code block
2) go to MT4 terminal and open Meta Editor (click icon or press F4)
3) go to File -> New -> Expert Advisor
4) put in a title and click Next, Next, Finish
5) Delete all text from new file and paste in text from code block
6) go back to MT4
7) Bring up Navigator (Ctrl+N if it's not already up)
8) go to expert advisors section and find what you titled it
9) open up a chart of the symbol you want to test
10) add the EA to this chart
11) specify colors and start time in inputs then press OK
12) use "S" key on your keyboard to advance 1 bar of current time frame
13) use tool bar buttons to change zoom and time frames, do objects, etc.
14) don't turn on auto scroll. if you do by accident, press "S" to return to simulation time.
15) click "buy" and "sell" buttons (white text, top center) to generate entry, TP and SL lines to track your trade
16) to cancel or close a trade, press "close order" then click the white entry line
17) drag and drop TP/SL lines to modify RR
18) click "End" to delete all objects and remove simulation from chart
19) to change simulation time, click "End", then add the simulator EA to your chart with a new start time
20) When you click "End", your own objects will be deleted too, so make sure you are done with them
21) keep track of your own trade results manually
22) use Tools-> History center to download new data if you need it. the simulator won't work on time frames if you don't have historical data going back that far, but it will work on time frames that you have the data for. If you have data but its not appearing, you might also need to increase max bars in chart in Tools->Options->Charts.
23) don't look at status bar if you are moused over hidden candles, or to avoid this you can hide the status bar.


Here is the code block.
//+------------------------------------------------------------------+ //| Bar Replay V2.mq4 | //| Copyright 2020, MetaQuotes Software Corp. | //| https://www.mql5.com | //+------------------------------------------------------------------+ #property copyright "Copyright 2020, MetaQuotes Software Corp." #property link "https://www.mql5.com" #property version "1.00" #property strict #define VK_A 0x41 #define VK_S 0x53 #define VK_X 0x58 #define VK_Z 0x5A #define VK_V 0x56 #define VK_C 0x43 #define VK_W 0x57 #define VK_E 0x45 double balance; string balance_as_string; int filehandle; int trade_ticket = 1; string objectname; string entry_line_name; string tp_line_name; string sl_line_name; string one_R_line_name; double distance; double entry_price; double tp_price; double sl_price; double one_R; double TP_distance; double gain_in_R; string direction; bool balance_file_exist; double new_balance; double sl_distance; string trade_number; double risk; double reward; string RR_string; int is_tp_or_sl_line=0; int click_to_cancel=0; input color foreground_color = clrWhite; input color background_color = clrBlack; input color bear_candle_color = clrRed; input color bull_candle_color = clrSpringGreen; input color current_price_line_color = clrGray; input string start_time = "2020.10.27 12:00"; input int vertical_margin = 100; //+------------------------------------------------------------------+ //| Expert initialization function | //+------------------------------------------------------------------+ int OnInit() { Comment(""); ChartNavigate(0,CHART_BEGIN,0); BlankChart(); ChartSetInteger(0,CHART_SHIFT,true); ChartSetInteger(0,CHART_FOREGROUND,false); ChartSetInteger(0,CHART_AUTOSCROLL,false); ChartSetInteger(0,CHART_SCALEFIX,false); ChartSetInteger(0,CHART_SHOW_OBJECT_DESCR,true); if (ObjectFind(0,"First OnInit")<0){ CreateStorageHLine("First OnInit",1);} if (ObjectFind(0,"Simulation Time")<0){ CreateTestVLine("Simulation Time",StringToTime(start_time));} string vlinename; for (int i=0; i<=1000000; i++){ vlinename="VLine"+IntegerToString(i); ObjectDelete(vlinename); } HideBars(SimulationBarTime(),0); //HideBar(SimulationBarTime()); UnBlankChart(); LabelCreate("New Buy Button","Buy",0,38,foreground_color); LabelCreate("New Sell Button","Sell",0,41,foreground_color); LabelCreate("Cancel Order","Close Order",0,44,foreground_color); LabelCreate("Risk To Reward","RR",0,52,foreground_color); LabelCreate("End","End",0,35,foreground_color); ObjectMove(0,"First OnInit",0,0,0); //--- create timer EventSetTimer(60); return(INIT_SUCCEEDED); } //+------------------------------------------------------------------+ //| Expert deinitialization function | //+------------------------------------------------------------------+ void OnDeinit(const int reason) { //--- destroy timer EventKillTimer(); } //+------------------------------------------------------------------+ //| Expert tick function | //+------------------------------------------------------------------+ void OnTick() { //--- } //+------------------------------------------------------------------+ //| ChartEvent function | //+------------------------------------------------------------------+ void OnChartEvent(const int id, const long &lparam, const double &dparam, const string &sparam) { if (id==CHARTEVENT_CHART_CHANGE){ int chartscale = ChartGetInteger(0,CHART_SCALE,0); int lastchartscale = ObjectGetDouble(0,"Last Chart Scale",OBJPROP_PRICE,0); if (chartscale!=lastchartscale){ int chartscale = ChartGetInteger(0,CHART_SCALE,0); ObjectMove(0,"Last Chart Scale",0,0,chartscale); OnInit(); }} if (id==CHARTEVENT_KEYDOWN){ if (lparam==VK_S){ IncreaseSimulationTime(); UnHideBar(SimulationPosition()); NavigateToSimulationPosition(); CreateHLine(0,"Current Price",Close[SimulationPosition()+1],current_price_line_color,1,0,true,false,false,"price"); SetChartMinMax(); }} if(id==CHARTEVENT_OBJECT_CLICK) { if(sparam=="New Sell Button") { distance = iATR(_Symbol,_Period,20,SimulationPosition()+1)/2; objectname = "Trade # "+IntegerToString(trade_ticket); CreateHLine(0,objectname,Close[SimulationPosition()+1],foreground_color,2,5,false,true,true,"Sell"); objectname = "TP for Trade # "+IntegerToString(trade_ticket); CreateHLine(0,objectname,Close[SimulationPosition()+1]-distance*2,clrAqua,2,5,false,true,true,"TP"); objectname = "SL for Trade # "+IntegerToString(trade_ticket); CreateHLine(0,objectname,Close[SimulationPosition()+1]+distance,clrRed,2,5,false,true,true,"SL"); trade_ticket+=1; } } if(id==CHARTEVENT_OBJECT_CLICK) { if(sparam=="New Buy Button") { distance = iATR(_Symbol,_Period,20,SimulationPosition()+1)/2; objectname = "Trade # "+IntegerToString(trade_ticket); CreateHLine(0,objectname,Close[SimulationPosition()+1],foreground_color,2,5,false,true,true,"Buy"); objectname = "TP for Trade # "+IntegerToString(trade_ticket); CreateHLine(0,objectname,Close[SimulationPosition()+1]+distance*2,clrAqua,2,5,false,true,true,"TP"); objectname = "SL for Trade # "+IntegerToString(trade_ticket); CreateHLine(0,objectname,Close[SimulationPosition()+1]-distance,clrRed,2,5,false,true,true,"SL"); trade_ticket+=1; } } if(id==CHARTEVENT_OBJECT_DRAG) { if(StringFind(sparam,"TP",0)==0) { is_tp_or_sl_line=1; } if(StringFind(sparam,"SL",0)==0) { is_tp_or_sl_line=1; } Comment(is_tp_or_sl_line); if(is_tp_or_sl_line==1) { trade_number = StringSubstr(sparam,7,9); entry_line_name = trade_number; tp_line_name = "TP for "+entry_line_name; sl_line_name = "SL for "+entry_line_name; entry_price = ObjectGetDouble(0,entry_line_name,OBJPROP_PRICE,0); tp_price = ObjectGetDouble(0,tp_line_name,OBJPROP_PRICE,0); sl_price = ObjectGetDouble(0,sl_line_name,OBJPROP_PRICE,0); sl_distance = MathAbs(entry_price-sl_price); TP_distance = MathAbs(entry_price-tp_price); reward = TP_distance/sl_distance; RR_string = "RR = 1 : "+DoubleToString(reward,2); ObjectSetString(0,"Risk To Reward",OBJPROP_TEXT,RR_string); is_tp_or_sl_line=0; } } if(id==CHARTEVENT_OBJECT_CLICK) { if(sparam=="Cancel Order") { click_to_cancel=1; Comment("please click the entry line of the order you wish to cancel."); } } if(id==CHARTEVENT_OBJECT_CLICK) { if(sparam!="Cancel Order") { if(click_to_cancel==1) { if(ObjectGetInteger(0,sparam,OBJPROP_TYPE,0)==OBJ_HLINE) { entry_line_name = sparam; tp_line_name = "TP for "+sparam; sl_line_name = "SL for "+sparam; ObjectDelete(0,entry_line_name); ObjectDelete(0,tp_line_name); ObjectDelete(0,sl_line_name); click_to_cancel=0; ObjectSetString(0,"Risk To Reward",OBJPROP_TEXT,"RR"); } } } } if (id==CHARTEVENT_OBJECT_CLICK){ if (sparam=="End"){ ObjectsDeleteAll(0,-1,-1); ExpertRemove(); }} } //+------------------------------------------------------------------+ void CreateStorageHLine(string name, double value){ ObjectDelete(name); ObjectCreate(0,name,OBJ_HLINE,0,0,value); ObjectSetInteger(0,name,OBJPROP_SELECTED,false); ObjectSetInteger(0,name,OBJPROP_SELECTABLE,false); ObjectSetInteger(0,name,OBJPROP_COLOR,clrNONE); ObjectSetInteger(0,name,OBJPROP_BACK,true); ObjectSetInteger(0,name,OBJPROP_ZORDER,0); } void CreateTestHLine(string name, double value){ ObjectDelete(name); ObjectCreate(0,name,OBJ_HLINE,0,0,value); ObjectSetInteger(0,name,OBJPROP_SELECTED,false); ObjectSetInteger(0,name,OBJPROP_SELECTABLE,false); ObjectSetInteger(0,name,OBJPROP_COLOR,clrWhite); ObjectSetInteger(0,name,OBJPROP_BACK,true); ObjectSetInteger(0,name,OBJPROP_ZORDER,0); } bool IsFirstOnInit(){ bool bbb=false; if (ObjectGetDouble(0,"First OnInit",OBJPROP_PRICE,0)==1){return true;} return bbb; } void CreateTestVLine(string name, datetime timevalue){ ObjectDelete(name); ObjectCreate(0,name,OBJ_VLINE,0,timevalue,0); ObjectSetInteger(0,name,OBJPROP_SELECTED,false); ObjectSetInteger(0,name,OBJPROP_SELECTABLE,false); ObjectSetInteger(0,name,OBJPROP_COLOR,clrNONE); ObjectSetInteger(0,name,OBJPROP_BACK,false); ObjectSetInteger(0,name,OBJPROP_ZORDER,3); } datetime SimulationTime(){ return ObjectGetInteger(0,"Simulation Time",OBJPROP_TIME,0); } int SimulationPosition(){ return iBarShift(_Symbol,_Period,SimulationTime(),false); } datetime SimulationBarTime(){ return Time[SimulationPosition()]; } void IncreaseSimulationTime(){ ObjectMove(0,"Simulation Time",0,Time[SimulationPosition()-1],0); } void NavigateToSimulationPosition(){ ChartNavigate(0,CHART_END,-1*SimulationPosition()+15); } void NotifyNotEnoughHistoricalData(){ BlankChart(); Comment("Sorry, but there is not enough historical data to load this time frame."+"\n"+ "Please load more historical data or use a higher time frame. Thank you :)");} void UnHideBar(int barindex){ ObjectDelete(0,"VLine"+IntegerToString(barindex+1)); } void BlankChart(){ ChartSetInteger(0,CHART_COLOR_FOREGROUND,clrNONE); ChartSetInteger(0,CHART_COLOR_CANDLE_BEAR,clrNONE); ChartSetInteger(0,CHART_COLOR_CANDLE_BULL,clrNONE); ChartSetInteger(0,CHART_COLOR_CHART_DOWN,clrNONE); ChartSetInteger(0,CHART_COLOR_CHART_UP,clrNONE); ChartSetInteger(0,CHART_COLOR_CHART_LINE,clrNONE); ChartSetInteger(0,CHART_COLOR_GRID,clrNONE); ChartSetInteger(0,CHART_COLOR_ASK,clrNONE); ChartSetInteger(0,CHART_COLOR_BID,clrNONE);} void UnBlankChart(){ ChartSetInteger(0,CHART_COLOR_FOREGROUND,foreground_color); ChartSetInteger(0,CHART_COLOR_CANDLE_BEAR,bear_candle_color); ChartSetInteger(0,CHART_COLOR_CANDLE_BULL,bull_candle_color); ChartSetInteger(0,CHART_COLOR_BACKGROUND,background_color); ChartSetInteger(0,CHART_COLOR_CHART_DOWN,foreground_color); ChartSetInteger(0,CHART_COLOR_CHART_UP,foreground_color); ChartSetInteger(0,CHART_COLOR_CHART_LINE,foreground_color); ChartSetInteger(0,CHART_COLOR_GRID,clrNONE); ChartSetInteger(0,CHART_COLOR_ASK,clrNONE); ChartSetInteger(0,CHART_COLOR_BID,clrNONE);} void HideBars(datetime starttime, int shift){ int startbarindex = iBarShift(_Symbol,_Period,starttime,false); ChartNavigate(0,CHART_BEGIN,0); if (Time[WindowFirstVisibleBar()]>SimulationTime()){NotifyNotEnoughHistoricalData();} if (Time[WindowFirstVisibleBar()]=0; i--){ vlinename="VLine"+IntegerToString(i); ObjectCreate(0,vlinename,OBJ_VLINE,0,Time[i],0); ObjectSetInteger(0,vlinename,OBJPROP_COLOR,background_color); ObjectSetInteger(0,vlinename,OBJPROP_BACK,false); ObjectSetInteger(0,vlinename,OBJPROP_WIDTH,vlinewidth); ObjectSetInteger(0,vlinename,OBJPROP_ZORDER,10); ObjectSetInteger(0,vlinename,OBJPROP_FILL,true); ObjectSetInteger(0,vlinename,OBJPROP_STYLE,STYLE_SOLID); ObjectSetInteger(0,vlinename,OBJPROP_SELECTED,false); ObjectSetInteger(0,vlinename,OBJPROP_SELECTABLE,false); } NavigateToSimulationPosition(); SetChartMinMax();} }//end of HideBars function void SetChartMinMax(){ int firstbar = WindowFirstVisibleBar(); int lastbar = SimulationPosition(); int lastbarwhenscrolled = WindowFirstVisibleBar()-WindowBarsPerChart(); if (lastbarwhenscrolled>lastbar){lastbar=lastbarwhenscrolled;} double highest = High[iHighest(_Symbol,_Period,MODE_HIGH,firstbar-lastbar,lastbar)]; double lowest = Low[iLowest(_Symbol,_Period,MODE_LOW,firstbar-lastbar,lastbar)]; ChartSetInteger(0,CHART_SCALEFIX,true); ChartSetDouble(0,CHART_FIXED_MAX,highest+vertical_margin*_Point); ChartSetDouble(0,CHART_FIXED_MIN,lowest-vertical_margin*_Point); } void LabelCreate(string labelname, string labeltext, int row, int column, color labelcolor){ int ylocation = row*18; int xlocation = column*10; ObjectCreate(0,labelname,OBJ_LABEL,0,0,0); ObjectSetString(0,labelname,OBJPROP_TEXT,labeltext); ObjectSetInteger(0,labelname,OBJPROP_COLOR,labelcolor); ObjectSetInteger(0,labelname,OBJPROP_FONTSIZE,10); ObjectSetInteger(0,labelname,OBJPROP_ZORDER,10); ObjectSetInteger(0,labelname,OBJPROP_BACK,false); ObjectSetInteger(0,labelname,OBJPROP_CORNER,CORNER_LEFT_UPPER); ObjectSetInteger(0,labelname,OBJPROP_ANCHOR,ANCHOR_LEFT_UPPER); ObjectSetInteger(0,labelname,OBJPROP_XDISTANCE,xlocation); ObjectSetInteger(0,labelname,OBJPROP_YDISTANCE,ylocation);} double GetHLinePrice(string name){ return ObjectGetDouble(0,name,OBJPROP_PRICE,0); } void CreateHLine(int chartid, string objectnamey, double objectprice, color linecolor, int width, int zorder, bool back, bool selected, bool selectable, string descriptionn) { ObjectDelete(chartid,objectnamey); ObjectCreate(chartid,objectnamey,OBJ_HLINE,0,0,objectprice); ObjectSetString(chartid,objectnamey,OBJPROP_TEXT,objectprice); ObjectSetInteger(chartid,objectnamey,OBJPROP_COLOR,linecolor); ObjectSetInteger(chartid,objectnamey,OBJPROP_WIDTH,width); ObjectSetInteger(chartid,objectnamey,OBJPROP_ZORDER,zorder); ObjectSetInteger(chartid,objectnamey,OBJPROP_BACK,back); ObjectSetInteger(chartid,objectnamey,OBJPROP_SELECTED,selected); ObjectSetInteger(chartid,objectnamey,OBJPROP_SELECTABLE,selectable); ObjectSetString(0,objectnamey,OBJPROP_TEXT,descriptionn); } //end of code 
submitted by Learning_2 to Forex [link] [comments]

No, the British did not steal $45 trillion from India

This is an updated copy of the version on BadHistory. I plan to update it in accordance with the feedback I got.
I'd like to thank two people who will remain anonymous for helping me greatly with this post (you know who you are)
Three years ago a festschrift for Binay Bhushan Chaudhuri was published by Shubhra Chakrabarti, a history teacher at the University of Delhi and Utsa Patnaik, a Marxist economist who taught at JNU until 2010.
One of the essays in the festschirt by Utsa Patnaik was an attempt to quantify the "drain" undergone by India during British Rule. Her conclusion? Britain robbed India of $45 trillion (or £9.2 trillion) during their 200 or so years of rule. This figure was immensely popular, and got republished in several major news outlets (here, here, here, here (they get the number wrong) and more recently here), got a mention from the Minister of External Affairs & returns 29,100 results on Google. There's also plenty of references to it here on Reddit.
Patnaik is not the first to calculate such a figure. Angus Maddison thought it was £100 million, Simon Digby said £1 billion, Javier Estaban said £40 million see Roy (2019). The huge range of figures should set off some alarm bells.
So how did Patnaik calculate this (shockingly large) figure? Well, even though I don't have access to the festschrift, she conveniently has written an article detailing her methodology here. Let's have a look.
How exactly did the British manage to diddle us and drain our wealth’ ? was the question that Basudev Chatterjee (later editor of a volume in the Towards Freedom project) had posed to me 50 years ago when we were fellow-students abroad.
This is begging the question.
After decades of research I find that using India’s commodity export surplus as the measure and applying an interest rate of 5%, the total drain from 1765 to 1938, compounded up to 2016, comes to £9.2 trillion; since $4.86 exchanged for £1 those days, this sum equals about $45 trillion.
This is completely meaningless. To understand why it's meaningless consider India's annual coconut exports. These are almost certainly a surplus but the surplus in trade is countered by the other country buying the product (indeed, by definition, trade surpluses contribute to the GDP of a nation which hardly plays into intuitive conceptualisations of drain).
Furthermore, Dewey (2019) critiques the 5% interest rate.
She [Patnaik] consistently adopts statistical assumptions (such as compound interest at a rate of 5% per annum over centuries) that exaggerate the magnitude of the drain
Moving on:
The exact mechanism of drain, or transfers from India to Britain was quite simple.
Convenient.
Drain theory possessed the political merit of being easily grasped by a nation of peasants. [...] No other idea could arouse people than the thought that they were being taxed so that others in far off lands might live in comfort. [...] It was, therefore, inevitable that the drain theory became the main staple of nationalist political agitation during the Gandhian era.
- Chandra et al. (1989)
The key factor was Britain’s control over our taxation revenues combined with control over India’s financial gold and forex earnings from its booming commodity export surplus with the world. Simply put, Britain used locally raised rupee tax revenues to pay for its net import of goods, a highly abnormal use of budgetary funds not seen in any sovereign country.
The issue with figures like these is they all make certain methodological assumptions that are impossible to prove. From Roy in Frankema et al. (2019):
the "drain theory" of Indian poverty cannot be tested with evidence, for several reasons. First, it rests on the counterfactual that any money saved on account of factor payments abroad would translate into domestic investment, which can never be proved. Second, it rests on "the primitive notion that all payments to foreigners are "drain"", that is, on the assumption that these payments did not contribute to domestic national income to the equivalent extent (Kumar 1985, 384; see also Chaudhuri 1968). Again, this cannot be tested. [...] Fourth, while British officers serving India did receive salaries that were many times that of the average income in India, a paper using cross-country data shows that colonies with better paid officers were governed better (Jones 2013).
Indeed, drain theory rests on some very weak foundations. This, in of itself, should be enough to dismiss any of the other figures that get thrown out. Nonetheless, I felt it would be a useful exercise to continue exploring Patnaik's take on drain theory.
The East India Company from 1765 onwards allocated every year up to one-third of Indian budgetary revenues net of collection costs, to buy a large volume of goods for direct import into Britain, far in excess of that country’s own needs.
So what's going on here? Well Roy (2019) explains it better:
Colonial India ran an export surplus, which, together with foreign investment, was used to pay for services purchased from Britain. These payments included interest on public debt, salaries, and pensions paid to government offcers who had come from Britain, salaries of managers and engineers, guaranteed profts paid to railway companies, and repatriated business profts. How do we know that any of these payments involved paying too much? The answer is we do not.
So what was really happening is the government was paying its workers for services (as well as guaranteeing profits - to promote investment - something the GoI does today Dalal (2019), and promoting business in India), and those workers were remitting some of that money to Britain. This is hardly a drain (unless, of course, Indian diaspora around the world today are "draining" it). In some cases, the remittances would take the form of goods (as described) see Chaudhuri (1983):
It is obvious that these debit items were financed through the export surplus on merchandise account, and later, when railway construction started on a large scale in India, through capital import. Until 1833 the East India Company followed a cumbersome method in remitting the annual home charges. This was to purchase export commodities in India out of revenue, which were then shipped to London and the proceeds from their sale handed over to the home treasury.
While Roy's earlier point argues better paid officers governed better, it is honestly impossible to say what part of the repatriated export surplus was a drain, and what was not. However calling all of it a drain is definitely misguided.
It's worth noting that Patnaik seems to make no attempt to quantify the benefits of the Raj either, Dewey (2019)'s 2nd criticism:
she [Patnaik] consistently ignores research that would tend to cut the economic impact of the drain down to size, such as the work on the sources of investment during the industrial revolution (which shows that industrialisation was financed by the ploughed-back profits of industrialists) or the costs of empire school (which stresses the high price of imperial defence)

Since tropical goods were highly prized in other cold temperate countries which could never produce them, in effect these free goods represented international purchasing power for Britain which kept a part for its own use and re-exported the balance to other countries in Europe and North America against import of food grains, iron and other goods in which it was deficient.
Re-exports necessarily adds value to goods when the goods are processed and when the goods are transported. The country with the largest navy at the time would presumably be in very good stead to do the latter.
The British historians Phyllis Deane and WA Cole presented an incorrect estimate of Britain’s 18th-19th century trade volume, by leaving out re-exports completely. I found that by 1800 Britain’s total trade was 62% higher than their estimate, on applying the correct definition of trade including re-exports, that is used by the United Nations and by all other international organisations.
While interesting, and certainly expected for such an old book, re-exporting necessarily adds value to goods.
When the Crown took over from the Company, from 1861 a clever system was developed under which all of India’s financial gold and forex earnings from its fast-rising commodity export surplus with the world, was intercepted and appropriated by Britain. As before up to a third of India’s rising budgetary revenues was not spent domestically but was set aside as ‘expenditure abroad’.
So, what does this mean? Britain appropriated all of India's earnings, and then spent a third of it aboard? Not exactly. She is describing home charges see Roy (2019) again:
Some of the expenditures on defense and administration were made in sterling and went out of the country. This payment by the government was known as the Home Charges. For example, interest payment on loans raised to finance construction of railways and irrigation works, pensions paid to retired officers, and purchase of stores, were payments in sterling. [...] almost all money that the government paid abroad corresponded to the purchase of a service from abroad. [...] The balance of payments system that emerged after 1800 was based on standard business principles. India bought something and paid for it. State revenues were used to pay for wages of people hired abroad, pay for interest on loans raised abroad, and repatriation of profits on foreign investments coming into India. These were legitimate market transactions.
Indeed, if paying for what you buy is drain, then several billions of us are drained every day.
The Secretary of State for India in Council, based in London, invited foreign importers to deposit with him the payment (in gold, sterling and their own currencies) for their net imports from India, and these gold and forex payments disappeared into the yawning maw of the SoS’s account in the Bank of England.
It should be noted that India having two heads was beneficial, and encouraged investment per Roy (2019):
The fact that the India Office in London managed a part of the monetary system made India creditworthy, stabilized its currency, and encouraged foreign savers to put money into railways and private enterprise in India. Current research on the history of public debt shows that stable and large colonies found it easier to borrow abroad than independent economies because the investors trusted the guarantee of the colonist powers.

Against India’s net foreign earnings he issued bills, termed Council bills (CBs), to an equivalent rupee value. The rate (between gold-linked sterling and silver rupee) at which the bills were issued, was carefully adjusted to the last farthing, so that foreigners would never find it more profitable to ship financial gold as payment directly to Indians, compared to using the CB route. Foreign importers then sent the CBs by post or by telegraph to the export houses in India, that via the exchange banks were paid out of the budgeted provision of sums under ‘expenditure abroad’, and the exporters in turn paid the producers (peasants and artisans) from whom they sourced the goods.
Sunderland (2013) argues CBs had two main roles (and neither were part of a grand plot to keep gold out of India):
Council bills had two roles. They firstly promoted trade by handing the IO some control of the rate of exchange and allowing the exchange banks to remit funds to India and to hedge currency transaction risks. They also enabled the Indian government to transfer cash to England for the payment of its UK commitments.

The United Nations (1962) historical data for 1900 to 1960, show that for three decades up to 1928 (and very likely earlier too) India posted the second highest merchandise export surplus in the world, with USA in the first position. Not only were Indians deprived of every bit of the enormous international purchasing power they had earned over 175 years, even its rupee equivalent was not issued to them since not even the colonial government was credited with any part of India’s net gold and forex earnings against which it could issue rupees. The sleight-of-hand employed, namely ‘paying’ producers out of their own taxes, made India’s export surplus unrequited and constituted a tax-financed drain to the metropolis, as had been correctly pointed out by those highly insightful classical writers, Dadabhai Naoroji and RCDutt.
It doesn't appear that others appreciate their insight Roy (2019):
K. N. Chaudhuri rightly calls such practice ‘confused’ economics ‘coloured by political feelings’.

Surplus budgets to effect such heavy tax-financed transfers had a severe employment–reducing and income-deflating effect: mass consumption was squeezed in order to release export goods. Per capita annual foodgrains absorption in British India declined from 210 kg. during the period 1904-09, to 157 kg. during 1937-41, and to only 137 kg by 1946.
Dewey (1978) points out reliability issues with Indian agriculutural statistics, however this calorie decline persists to this day. Some of it is attributed to less food being consumed at home Smith (2015), a lower infectious disease burden Duh & Spears (2016) and diversified diets Vankatesh et al. (2016).
If even a part of its enormous foreign earnings had been credited to it and not entirely siphoned off, India could have imported modern technology to build up an industrial structure as Japan was doing.
This is, unfortunately, impossible to prove. Had the British not arrived in India, there is no clear indication that India would've united (this is arguably more plausible than the given counterfactual1). Had the British not arrived in India, there is no clear indication India would not have been nuked in WW2, much like Japan. Had the British not arrived in India, there is no clear indication India would not have been invaded by lizard people, much like Japan. The list continues eternally.
Nevertheless, I will charitably examine the given counterfactual anyway. Did pre-colonial India have industrial potential? The answer is a resounding no.
From Gupta (1980):
This article starts from the premise that while economic categories - the extent of commodity production, wage labour, monetarisation of the economy, etc - should be the basis for any analysis of the production relations of pre-British India, it is the nature of class struggles arising out of particular class alignments that finally gives the decisive twist to social change. Arguing on this premise, and analysing the available evidence, this article concludes that there was little potential for industrial revolution before the British arrived in India because, whatever might have been the character of economic categories of that period, the class relations had not sufficiently matured to develop productive forces and the required class struggle for a 'revolution' to take place.
A view echoed in Raychaudhuri (1983):
Yet all of this did not amount to an economic situation comparable to that of western Europe on the eve of the industrial revolution. Her technology - in agriculture as well as manufacturers - had by and large been stagnant for centuries. [...] The weakness of the Indian economy in the mid-eighteenth century, as compared to pre-industrial Europe was not simply a matter of technology and commercial and industrial organization. No scientific or geographical revolution formed part of the eighteenth-century Indian's historical experience. [...] Spontaneous movement towards industrialisation is unlikely in such a situation.
So now we've established India did not have industrial potential, was India similar to Japan just before the Meiji era? The answer, yet again, unsurprisingly, is no. Japan's economic situation was not comparable to India's, which allowed for Japan to finance its revolution. From Yasuba (1986):
All in all, the Japanese standard of living may not have been much below the English standard of living before industrialization, and both of them may have been considerably higher than the Indian standard of living. We can no longer say that Japan started from a pathetically low economic level and achieved a rapid or even "miraculous" economic growth. Japan's per capita income was almost as high as in Western Europe before industrialization, and it was possible for Japan to produce surplus in the Meiji Period to finance private and public capital formation.
The circumstances that led to Meiji Japan were extremely unique. See Tomlinson (1985):
Most modern comparisons between India and Japan, written by either Indianists or Japanese specialists, stress instead that industrial growth in Meiji Japan was the product of unique features that were not reproducible elsewhere. [...] it is undoubtably true that Japan's progress to industrialization has been unique and unrepeatable
So there you have it. Unsubstantiated statistical assumptions, calling any number you can a drain & assuming a counterfactual for no good reason gets you this $45 trillion number. Hopefully that's enough to bury it in the ground.
1. Several authors have affirmed that Indian identity is a colonial artefact. For example see Rajan 1969:
Perhaps the single greatest and most enduring impact of British rule over India is that it created an Indian nation, in the modern political sense. After centuries of rule by different dynasties overparts of the Indian sub-continent, and after about 100 years of British rule, Indians ceased to be merely Bengalis, Maharashtrians,or Tamils, linguistically and culturally.
or see Bryant 2000:
But then, it would be anachronistic to condemn eighteenth-century Indians, who served the British, as collaborators, when the notion of 'democratic' nationalism or of an Indian 'nation' did not then exist. [...] Indians who fought for them, differed from the Europeans in having a primary attachment to a non-belligerent religion, family and local chief, which was stronger than any identity they might have with a more remote prince or 'nation'.

Bibliography

Chakrabarti, Shubra & Patnaik, Utsa (2018). Agrarian and other histories: Essays for Binay Bhushan Chaudhuri. Colombia University Press
Hickel, Jason (2018). How the British stole $45 trillion from India. The Guardian
Bhuyan, Aroonim & Sharma, Krishan (2019). The Great Loot: How the British stole $45 trillion from India. Indiapost
Monbiot, George (2020). English Landowners have stolen our rights. It is time to reclaim them. The Guardian
Tsjeng, Zing (2020). How Britain Stole $45 trillion from India with trains | Empires of Dirt. Vice
Chaudhury, Dipanjan (2019). British looted $45 trillion from India in today’s value: Jaishankar. The Economic Times
Roy, Tirthankar (2019). How British rule changed India's economy: The Paradox of the Raj. Palgrave Macmillan
Patnaik, Utsa (2018). How the British impoverished India. Hindustan Times
Tuovila, Alicia (2019). Expenditure method. Investopedia
Dewey, Clive (2019). Changing the guard: The dissolution of the nationalist–Marxist orthodoxy in the agrarian and agricultural history of India. The Indian Economic & Social History Review
Chandra, Bipan et al. (1989). India's Struggle for Independence, 1857-1947. Penguin Books
Frankema, Ewout & Booth, Anne (2019). Fiscal Capacity and the Colonial State in Asia and Africa, c. 1850-1960. Cambridge University Press
Dalal, Sucheta (2019). IL&FS Controversy: Centre is Paying Up on Sovereign Guarantees to ADB, KfW for Group's Loan. TheWire
Chaudhuri, K.N. (1983). X - Foreign Trade and Balance of Payments (1757–1947). Cambridge University Press
Sunderland, David (2013). Financing the Raj: The City of London and Colonial India, 1858-1940. Boydell Press
Dewey, Clive (1978). Patwari and Chaukidar: Subordinate officials and the reliability of India’s agricultural statistics. Athlone Press
Smith, Lisa (2015). The great Indian calorie debate: Explaining rising undernourishment during India’s rapid economic growth. Food Policy
Duh, Josephine & Spears, Dean (2016). Health and Hunger: Disease, Energy Needs, and the Indian Calorie Consumption Puzzle. The Economic Journal
Vankatesh, P. et al. (2016). Relationship between Food Production and Consumption Diversity in India – Empirical Evidences from Cross Section Analysis. Agricultural Economics Research Review
Gupta, Shaibal (1980). Potential of Industrial Revolution in Pre-British India. Economic and Political Weekly
Raychaudhuri, Tapan (1983). I - The mid-eighteenth-century background. Cambridge University Press
Yasuba, Yasukichi (1986). Standard of Living in Japan Before Industrialization: From what Level did Japan Begin? A Comment. The Journal of Economic History
Tomblinson, B.R. (1985). Writing History Sideways: Lessons for Indian Economic Historians from Meiji Japan. Cambridge University Press
Rajan, M.S. (1969). The Impact of British Rule in India. Journal of Contemporary History
Bryant, G.J. (2000). Indigenous Mercenaries in the Service of European Imperialists: The Case of the Sepoys in the Early British Indian Army, 1750-1800. War in History
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Reduce Output And Price, Now Is The End Of OPEC?

Reduce Output And Price, Now Is The End Of OPEC?

Photo: Internet
Since the outbreak of COVID-19 in 2020, the global economy has entered a recession, with gold soaring, stock markets tumbling, and oil prices plummeting.
Saudi Arabia cut pricing for oil sales to Asia and the U.S. for October shipments, and the reduction exceeded last month.
Global daily oil consumption (total liquid volume) broke the "100 million barrels" mark for the first time in 2019, reaching 10.96 million barrels. It means the global daily consumption is more than 100 million barrels, and the annual consumption is more than 5 billion tons.
Since the outbreak of COVID-19, fuel demand has decreased significantly, while global oil supply has continued to increase.
Global oil consumption has decreased by nearly a quarter due to COVID-19. The global daily oil consumption level in the second quarter of this year was less than 77 million barrels, which is almost 20 years ago.
20th April saw WTI oil prices plunge from $17.85 to -$37.63, more than a 300% drop, the largest one day drop for U.S. crude in history.
The oil prices up and down in history, and various factors impact the oil prices. One of the most critical factors is OPEC.
The Birth of OPEC
The Organization of the Petroleum Exporting Countries (OPEC) is a permanent, intergovernmental Organization created at the Baghdad Conference on September 10–14, 1960, by Iran, Iraq, Kuwait, Saudi Arabia, Venezuela.
Before the OPEC, the Seven Sisters (E Anglo-Iranian Oil Company, Gulf Oil, Royal Dutch Shell, Chevron, ExxonMobil, Socony, Standard Oil Company of New York, and Texaco) controlled the world's oil markets.
In the 1950s, coal was the most critical fuel globally, but oil consumption increased rapidly, and demand continued to grow. In 1959, the United States' Seven Sisters lowered the price of oil produced in Venezuela and the Middle East by 10% to reduce the United States' price.
To counter the U.S. oil monopoly, OPEC was born.
OPEC's 13 members control approximately 30% of global oil supplies and 79.4% of proven reserves. OPEC member nations produce about 42% of the world's crude oil, and OPEC's oil exports account for roughly 60% of the total petroleum traded worldwide.

Photo: OPEC
Impact of OPEC on Oil Prices
Within the OPEC group, Saudi Arabia is the largest crude oil producer in the world and remains the most dominant member of OPEC, with each instance of a cut in oil production by them, resulting in a sharp rise in oil prices, and vice versa.
Additionally, the 'kingdom of Saud' is also the leading exporter of crude oil globally. Since 2000, all historical instances since the 1973 Arab oil embargo indicate that Saudi Arabia has maintained its upper hand in the oil market. It calls the shots in determining crude oil prices by controlling supply.
All major oil price fluctuations in recent history can be clearly attributed to production levels from Saudi Arabia, along with other OPEC nations.
Is it now the end of OPEC?
The success of shale oil and the plunge in oil prices in 2014 are signs that OPEC has declined.
Since 2014, U.S. shale oil has created a boom in domestic crude oil production. Shale oil comprises more than a third of the onshore production of crude oil in the lower 48 states. It drove U.S. oil output from 8.8 million barrels per day in 2014 to a record 12.2 million barrels a day in 2019.
As a result, the United States became the world's largest crude-oil producer.

Photo: EIA

Photo: EIA
Today the U.S., Saudi Arabia, and Russia rank among the top three in world oil production.

Photo: EIA

Photo: EIA

In November 2014, despite the appeals of other OPEC members to cut production, Saudi Arabia suddenly increased production sharply, trying to defeat U.S. shale oil companies through the competitive increase in OPEC member states. But American shale oil survived strongly by borrowing, and it became more efficient, and production costs were greatly reduced.
During this time, Saudi Arabia's economy is declining rapidly. Saudi Arabia had the highest government deficit in history-98 billion U.S. dollars, accounting for 15% of GDP in 2015.
In 2016, Saudi Arabia led OPEC and Russia to reach an OPEC+ production reduction agreement. Since then, oil prices have steadily rebounded. At the same time, Saudi Arabia has begun to consider taking advantage of high oil prices to list Saudi Aramco to ease domestic financial difficulties.
During this period, OPEC +'s reduction in production has rescued U.S. shale oil again. The production capacity of shale oil has increased sharply by 4 million barrels per day, surpassing Saudi Arabia, and Russia.
So far, the OPEC structure and cohesion continues to divide and elude.
On 8th March 2020, Saudi Arabia initiated a price war with Russia, facilitating a 65% quarterly fall in the price of oil. The price war was triggered by a break-up in dialogue between the Organization of the Petroleum Exporting Countries (OPEC) and Russia over proposed oil-production cuts in the midst of the COVID-19 pandemic. Russia walked out of the agreement, leading to the fall of the OPEC+ alliance.
While past oil shocks have been driven by either supply or demand, the price collapse of 2020 is highly unusual in oil market history: It results from a massive demand shock and a huge supply overhang at the same time.


https://preview.redd.it/2smucmlke1n51.png?width=686&format=png&auto=webp&s=63932270640be1913c8d41214418073a57b1646a

https://preview.redd.it/34m0nn3me1n51.png?width=686&format=png&auto=webp&s=6519e7614205955da1fe6b02437048ec461249dc
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on the fakeness of the internet

funny to see that subject pop up again. it was what drove me insane enough to find this sub in the first place.
at any rate, the problem is not the bots. I thought it was, but those are just part of the parasitic ecosystem.
but to get that, first we need to take a few steps back on web history, ad serving, UX, tracking technology and media advertising.
too lazy to gather links, but you know, do your googlin'.
I assume that most of you are fairly web literate here, but I'll try to go down into the bare bones as much as possible for those who aren't.
so let's start with a basic question - what is a web visitor anyway?
from the standpoint of a normal person, that would be a person browsing a given website or piece of content. from the standpoint of technology however all you know is that some device has downloaded content from your server using the http protocol. thanks to the wonderful technology of web browsers, you can plant browser cookies on a visitor - stuff that's used to remember if they logged in, what their preferences are, stuff that your service can read from the device. it also serves usually very basic telemetry like last visit time, session time, and so on.
this, over time has evolved in what we call browser fingerprinting, a convoluted bunch of technology that allows websites and web services to uniquely identify you.
it still doesn't know if you're a human or not, but from the standpoint of the web technology, you're a visitor.
now back in ye old days of the web, when the first banner ads were springing up, these were important questions. most consumers were still to be reached on traditional media channels, and ad spend would have to be justified somehow on the risky ventures of online business. so beyond traditional polls that would infer the value of visitors, websites would start tracking number of visitors, time on page and so on. these were used to milk the advertising cow so to speak, and it gave in to some funny developments like the creation of the popup ad - if I recon correctly on geocities, where they would just but the ads everywhere until some big auto company noticed that they're appearing on porn sites. so - put the ad in the popup, and you can claim it's not in the context of porn!
around this point in time the online ad business is still pretty low tech. you actually have to call a physical human being, they send you ppts and pdfs, you send back image files and excel sheets, you wire money, the ads run, and so on. this is called direct sales, and it's tracked again by counting a bunch of visitors, and telling you how much impressions and clicks your marvelous creatives and ad budget generated.
now enter google - or more precisely, a technology firm called doubleclick that was to be acquired by google. they developed a tool for automatic ad serving, later to be called programmatic advertising, that keeps the pesky sales dude out of the loop and achieves reasonable amounts of scale for a more hefty price - after all, if the sales are automated, you get a bidding war for attention between different advertisers, and you're paying for clicks.
so you can see how this was a strategic move for google - they already had the most valuable data available in this situation. they were seeing in real time what people were searching for, and using the programmatic ad serving system, you could effectively bid not just for general attention - but for attention with an intent to buy.
...and the way that google got this data is because they indexed the web, using bots. at least GoogleBot would identify itself as a site visitor, but in the meantime they developed a service for websites to comprehensively track their own visitors and where they were coming from and what they were doing on your website. incidentally, you could also put on google's ads on your webpage to earn quite a bit of money, as content relevant ads would be shown through the doubleclick system.
this kicked off two things:
one, the ability to classify your website visitors into different clusters and segments allowed businesses to start tailoring the appearance of the website or service to fit that specific audience segment, starting off the great fracture - segmentation of the web (in the sense that two people viewing the same website at the same time were not seeing the same thing)
two, it created a very strong financial incentive for people to trick google into thinking they were having actual human visitors that would click on ads, when in fact they were bots. in an even funnier twist, some of them were from browser hijackers, commonly known as malware at the time, which google cross-financed. look up download valley and crossrider.
at the cross section of the above two, you had one interesting twist: websites that would appear differently to the security bots or the compliance officers of Google as they would to fake visitors or malware jacked human beings. the former would get a benign looking website, while the latter would get bombarded with auto clicking ads.
this kicked off the billion dollar arms race called online advertising fraud.
I'm not here to shed a tear for big money corps bleeding money. the real fallout lay somewhere else, but for that you have to understand that you never really saw the real internet, you only saw your corner and the one that was personalized for you.
but if you ever had the pleasure of watching daytime TVs or off channels and witnessing the ads, you could kind of infer what kind of audience must be watching these shows generally. from quite clear rip offs to magic number lotteries and television fortune telling, these sorts of programming was aimed at the most gullible, bought for pennies, where the smallest audience portion had to be converted into a money making operation.
...and with audience segmentation and data gathering, that was now possible at unprecedented scale, automatically. so big was the scale in fact, that it gave birth to an entire new beast of an industry called affiliate marketing, where instead of a regular payroll, you'd get a cut of the sale should you figure out an angle on where to push whatever fucking bullshit the vendors were offering to whoever the fuck would be dumb enough to click on an ad and buy. (the funniest story I recall was someone pulling five figures a month because he figured out that if you buy ads on anime-hentai pages and sell PUA shit courses and e-books you'd make a killing)
at any rate, affiliate marketing brought with it the killer landing page, the thing that's supposed to hammer the nail in the coffin once you get through the banner ad. the earliest form of deceptiveness in memory comes from various pirate sites, that had fake download buttons as banner ads and virus alerts as the landing pages. but then at some point, some schmuck realized that for certain type of products, like diet pills or forex trading or whatever, the best lander is in fact a fake news page that comes packed with comments and all. that would convert like crazy, because it had the appearance of social proof.
until at least the lawsuits came raining down, and these sorts of landing pages and campaigns for being banned left right and centre on all platforms. which just launched a new arms race as the campaigns would be disguised for the bots doing the checkups, and aged facebook profiles would start selling for like 5K USD - these people were making 30-40k a day, they could afford to spend that much to continue running the shop.
speaking of facebook - it came just about the right time for the shit to brew max total. first they were unprecedented in the amount of data they were getting off of their users, and they came just in time to catch the full swing of what we call the 'responsive web' - that no user at the same time would see the same thing on their page, it was all allocated through an intricate web of recommendations, running real time, based on previously gathered and forecast behavioral data.
it also ran on one simple premise: take over the starting page position from google for most people, then they do not have to justify, ever, any ad spend that takes place on their platform, as long as it performs. furthermore, it was completely lacking any revenue share sort of scheme (save for the short period of facebook gaming, see Zynga), thus there was no incentive for the amount of bot traffic that the previous internet era had bred. instead, it came with an entirely different one - bots that would offer social proof in the way of shares and likes, but would not directly risk the business model, thus giving no incentive for facebook to fight them. (note that google didn't do much jack shit either besides indiscriminately penalizing websites it deemed suspicious when they reached critical payout thresholds)
the rest of the story you kind of sort of know. how the obama campaign was brilliant in using the new social media to inspire hope and blah blah blah, kicking the door open for big money politics who could hire the best snake oil salesmen in the market, who had the data and as you can see from the above, had the ethical standards of a shoe. at around 2014-2015 the press (the mainstream media) started to raise question about the duopoly, the buzzword of filter bubbles started appearing, not entirely unrelated to the fact that facebook by this time cannibalized their traffic with a fucking embedded share / like button and started charging money for them to reach their own audience. after 2016 the cries of fake news were everywhere, because there was no online space left which everyone was viewing the same way, and you had no way to verify what the person next to you was looking at.
since then, we've all become grandpa yelling at the television set, with nobody around us seeing what we're seeing on the screen, so we're being accused as bots and looking for bots under the carpet.
but it's been a long way coming, and the bots are honestly the least of our worries. trust me, I went bankrupt over that one. truth or fake doesn't even begin to describe the magnitude of the problem: more like we entered the phase where every word, event or picture is defined by who ever the fuck wins the auction over it, as the marketers of human attention grind the gears of the money mill without even understanding how fast they're digging towards hell.
don't believe me? look around the marketing and advertising related subs these days. the priests are eating the indulgences, and we're only now entering the period of deep fakes, good algo generated audio and good enough NLP. and in the meantime, the shadowrunners running up between two corp headquarter-highrises are skinning your belief systems.
so the best you can do is really, not litter the remnants of cyberspace which are not being mined, astroturfed or being pulled apart by the algos. no human connections on a nuclear trash heap mate.
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Hibiscus Petroleum Berhad (5199.KL)


https://preview.redd.it/gp18bjnlabr41.jpg?width=768&format=pjpg&auto=webp&s=6054e7f52e8d52da403016139ae43e0e799abf15
Download PDF of this article here: https://docdro.id/6eLgUPo
In light of the recent fall in oil prices due to the Saudi-Russian dispute and dampening demand for oil due to the lockdowns implemented globally, O&G stocks have taken a severe beating, falling approximately 50% from their highs at the beginning of the year. Not spared from this onslaught is Hibiscus Petroleum Berhad (Hibiscus), a listed oil and gas (O&G) exploration and production (E&P) company.
Why invest in O&G stocks in this particularly uncertain period? For one, valuations of these stocks have fallen to multi-year lows, bringing the potential ROI on these stocks to attractive levels. Oil prices are cyclical, and are bound to return to the mean given a sufficiently long time horizon. The trick is to find those companies who can survive through this downturn and emerge into “normal” profitability once oil prices rebound.
In this article, I will explore the upsides and downsides of investing in Hibiscus. I will do my best to cater this report to newcomers to the O&G industry – rather than address exclusively experts and veterans of the O&G sector. As an equity analyst, I aim to provide a view on the company primarily, and will generally refrain from providing macro views on oil or opinions about secular trends of the sector. I hope you enjoy reading it!
Stock code: 5199.KL
Stock name: Hibiscus Petroleum Berhad
Financial information and financial reports: https://www.malaysiastock.biz/Corporate-Infomation.aspx?securityCode=5199
Company website: https://www.hibiscuspetroleum.com/

Company Snapshot

Hibiscus Petroleum Berhad (5199.KL) is an oil and gas (O&G) upstream exploration and production (E&P) company located in Malaysia. As an E&P company, their business can be basically described as:
· looking for oil,
· drawing it out of the ground, and
· selling it on global oil markets.
This means Hibiscus’s profits are particularly exposed to fluctuating oil prices. With oil prices falling to sub-$30 from about $60 at the beginning of the year, Hibiscus’s stock price has also fallen by about 50% YTD – from around RM 1.00 to RM 0.45 (as of 5 April 2020).
https://preview.redd.it/3dqc4jraabr41.png?width=641&format=png&auto=webp&s=7ba0e8614c4e9d781edfc670016a874b90560684
https://preview.redd.it/lvdkrf0cabr41.png?width=356&format=png&auto=webp&s=46f250a713887b06986932fa475dc59c7c28582e
While the company is domiciled in Malaysia, its two main oil producing fields are located in both Malaysia and the UK. The Malaysian oil field is commonly referred to as the North Sabah field, while the UK oil field is commonly referred to as the Anasuria oil field. Hibiscus has licenses to other oil fields in different parts of the world, notably the Marigold/Sunflower oil fields in the UK and the VIC cluster in Australia, but its revenues and profits mainly stem from the former two oil producing fields.
Given that it’s a small player and has only two primary producing oil fields, it’s not surprising that Hibiscus sells its oil to a concentrated pool of customers, with 2 of them representing 80% of its revenues (i.e. Petronas and BP). Fortunately, both these customers are oil supermajors, and are unlikely to default on their obligations despite low oil prices.
At RM 0.45 per share, the market capitalization is RM 714.7m and it has a trailing PE ratio of about 5x. It doesn’t carry any debt, and it hasn’t paid a dividend in its listing history. The MD, Mr. Kenneth Gerard Pereira, owns about 10% of the company’s outstanding shares.

Reserves (Total recoverable oil) & Production (bbl/day)

To begin analyzing the company, it’s necessary to understand a little of the industry jargon. We’ll start with Reserves and Production.
In general, there are three types of categories for a company’s recoverable oil volumes – Reserves, Contingent Resources and Prospective Resources. Reserves are those oil fields which are “commercial”, which is defined as below:
As defined by the SPE PRMS, Reserves are “… quantities of petroleum anticipated to be commercially recoverable by application of development projects to known accumulations from a given date forward under defined conditions.” Therefore, Reserves must be discovered (by drilling, recoverable (with current technology), remaining in the subsurface (at the effective date of the evaluation) and “commercial” based on the development project proposed.)
Note that Reserves are associated with development projects. To be considered as “commercial”, there must be a firm intention to proceed with the project in a reasonable time frame (typically 5 years, and such intention must be based upon all of the following criteria:)
- A reasonable assessment of the future economics of the development project meeting defined investment and operating criteria; - A reasonable expectation that there will be a market for all or at least the expected sales quantities of production required to justify development; - Evidence that the necessary production and transportation facilities are available or can be made available; and - Evidence that legal, contractual, environmental and other social and economic concerns will allow for the actual implementation of the recovery project being evaluated.
Contingent Resources and Prospective Resources are further defined as below:
- Contingent Resources: potentially recoverable volumes associated with a development plan that targets discovered volumes but is not (yet commercial (as defined above); and) - Prospective Resources: potentially recoverable volumes associated with a development plan that targets as yet undiscovered volumes.
In the industry lingo, we generally refer to Reserves as ‘P’ and Contingent Resources as ‘C’. These ‘P’ and ‘C’ resources can be further categorized into 1P/2P/3P resources and 1C/2C/3C resources, each referring to a low/medium/high estimate of the company’s potential recoverable oil volumes:
- Low/1C/1P estimate: there should be reasonable certainty that volumes actually recovered will equal or exceed the estimate; - Best/2C/2P estimate: there should be an equal likelihood of the actual volumes of petroleum being larger or smaller than the estimate; and - High/3C/3P estimate: there is a low probability that the estimate will be exceeded.
Hence in the E&P industry, it is easy to see why most investors and analysts refer to the 2P estimate as the best estimate for a company’s actual recoverable oil volumes. This is because 2P reserves (‘2P’ referring to ‘Proved and Probable’) are a middle estimate of the recoverable oil volumes legally recognized as “commercial”.
However, there’s nothing stopping you from including 2C resources (riskier) or utilizing 1P resources (conservative) as your estimate for total recoverable oil volumes, depending on your risk appetite. In this instance, the company has provided a snapshot of its 2P and 2C resources in its analyst presentation:
https://preview.redd.it/o8qejdyc8br41.png?width=710&format=png&auto=webp&s=b3ab9be8f83badf0206adc982feda3a558d43e78
Basically, what the company is saying here is that by 2021, it will have classified as 2P reserves at least 23.7 million bbl from its Anasuria field and 20.5 million bbl from its North Sabah field – for total 2P reserves of 44.2 million bbl (we are ignoring the Australian VIC cluster as it is only estimated to reach first oil by 2022).
Furthermore, the company is stating that they have discovered (but not yet legally classified as “commercial”) a further 71 million bbl of oil from both the Anasuria and North Sabah fields, as well as the Marigold/Sunflower fields. If we include these 2C resources, the total potential recoverable oil volumes could exceed 100 million bbl.
In this report, we shall explore all valuation scenarios giving consideration to both 2P and 2C resources.
https://preview.redd.it/gk54qplf8br41.png?width=489&format=png&auto=webp&s=c905b7a6328432218b5b9dfd53cc9ef1390bd604
The company further targets a 2021 production rate of 20,000 bbl (LTM: 8,000 bbl), which includes 5,000 bbl from its Anasuria field (LTM: 2,500 bbl) and 7,000 bbl from its North Sabah field (LTM: 5,300 bbl).
This is a substantial increase in forecasted production from both existing and prospective oil fields. If it materializes, annual production rate could be as high as 7,300 mmbbl, and 2021 revenues (given FY20 USD/bbl of $60) could exceed RM 1.5 billion (FY20: RM 988 million).
However, this targeted forecast is quite a stretch from current production levels. Nevertheless, we shall consider all provided information in estimating a valuation for Hibiscus.
To understand Hibiscus’s oil production capacity and forecast its revenues and profits, we need to have a better appreciation of the performance of its two main cash-generating assets – the North Sabah field and the Anasuria field.

North Sabah oil field
https://preview.redd.it/62nssexj8br41.png?width=1003&format=png&auto=webp&s=cd78f86d51165fb9a93015e49496f7f98dad64dd
Hibiscus owns a 50% interest in the North Sabah field together with its partner Petronas, and has production rights over the field up to year 2040. The asset contains 4 oil fields, namely the St Joseph field, South Furious field, SF 30 field and Barton field.
For the sake of brevity, we shall not delve deep into the operational aspects of the fields or the contractual nature of its production sharing contract (PSC). We’ll just focus on the factors which relate to its financial performance. These are:
· Average uptime
· Total oil sold
· Average realized oil price
· Average OPEX per bbl
With regards to average uptime, we can see that the company maintains relative high facility availability, exceeding 90% uptime in all quarters of the LTM with exception of Jul-Sep 2019. The dip in average uptime was due to production enhancement projects and maintenance activities undertaken to improve the production capacity of the St Joseph and SF30 oil fields.
Hence, we can conclude that management has a good handle on operational performance. It also implies that there is little room for further improvement in production resulting from increased uptime.
As North Sabah is under a production sharing contract (PSC), there is a distinction between gross oil production and net oil production. The former relates to total oil drawn out of the ground, whereas the latter refers to Hibiscus’s share of oil production after taxes, royalties and expenses are accounted for. In this case, we want to pay attention to net oil production, not gross.
We can arrive at Hibiscus’s total oil sold for the last twelve months (LTM) by adding up the total oil sold for each of the last 4 quarters. Summing up the figures yields total oil sold for the LTM of approximately 2,075,305 bbl.
Then, we can arrive at an average realized oil price over the LTM by averaging the average realized oil price for the last 4 quarters, giving us an average realized oil price over the LTM of USD 68.57/bbl. We can do the same for average OPEX per bbl, giving us an average OPEX per bbl over the LTM of USD 13.23/bbl.
Thus, we can sum up the above financial performance of the North Sabah field with the following figures:
· Total oil sold: 2,075,305 bbl
· Average realized oil price: USD 68.57/bbl
· Average OPEX per bbl: USD 13.23/bbl

Anasuria oil field
https://preview.redd.it/586u4kfo8br41.png?width=1038&format=png&auto=webp&s=7580fc7f7df7e948754d025745a5cf47d4393c0f
Doing the same exercise as above for the Anasuria field, we arrive at the following financial performance for the Anasuria field:
· Total oil sold: 1,073,304 bbl
· Average realized oil price: USD 63.57/bbl
· Average OPEX per bbl: USD 23.22/bbl
As gas production is relatively immaterial, and to be conservative, we shall only consider the crude oil production from the Anasuria field in forecasting revenues.

Valuation (Method 1)

Putting the figures from both oil fields together, we get the following data:
https://preview.redd.it/7y6064dq8br41.png?width=700&format=png&auto=webp&s=2a4120563a011cf61fc6090e1cd5932602599dc2
Given that we have determined LTM EBITDA of RM 632m, the next step would be to subtract ITDA (interest, tax, depreciation & amortization) from it to obtain estimated LTM Net Profit. Using FY2020’s ITDA of approximately RM 318m as a guideline, we arrive at an estimated LTM Net Profit of RM 314m (FY20: 230m). Given the current market capitalization of RM 714.7m, this implies a trailing LTM PE of 2.3x.
Performing a sensitivity analysis given different oil prices, we arrive at the following net profit table for the company under different oil price scenarios, assuming oil production rate and ITDA remain constant:
https://preview.redd.it/xixge5sr8br41.png?width=433&format=png&auto=webp&s=288a00f6e5088d01936f0217ae7798d2cfcf11f2
From the above exercise, it becomes apparent that Hibiscus has a breakeven oil price of about USD 41.8863/bbl, and has a lot of operating leverage given the exponential rate of increase in its Net Profit with each consequent increase in oil prices.
Considering that the oil production rate (EBITDA) is likely to increase faster than ITDA’s proportion to revenues (fixed costs), at an implied PE of 4.33x, it seems likely that an investment in Hibiscus will be profitable over the next 10 years (with the assumption that oil prices will revert to the mean in the long-term).

Valuation (Method 2)

Of course, there are a lot of assumptions behind the above method of valuation. Hence, it would be prudent to perform multiple methods of valuation and compare the figures to one another.
As opposed to the profit/loss assessment in Valuation (Method 1), another way of performing a valuation would be to estimate its balance sheet value, i.e. total revenues from 2P Reserves, and assign a reasonable margin to it.
https://preview.redd.it/o2eiss6u8br41.png?width=710&format=png&auto=webp&s=03960cce698d9cedb076f3d5f571b3c59d908fa8
From the above, we understand that Hibiscus’s 2P reserves from the North Sabah and Anasuria fields alone are approximately 44.2 mmbbl (we ignore contribution from Australia’s VIC cluster as it hasn’t been developed yet).
Doing a similar sensitivity analysis of different oil prices as above, we arrive at the following estimated total revenues and accumulated net profit:
https://preview.redd.it/h8hubrmw8br41.png?width=450&format=png&auto=webp&s=6d23f0f9c3dafda89e758b815072ba335467f33e
Let’s assume that the above average of RM 9.68 billion in total realizable revenues from current 2P reserves holds true. If we assign a conservative Net Profit margin of 15% (FY20: 23%; past 5 years average: 16%), we arrive at estimated accumulated Net Profit from 2P Reserves of RM 1.452 billion. Given the current market capitalization of RM 714 million, we might be able to say that the equity is worth about twice the current share price.
However, it is understandable that some readers might feel that the figures used in the above estimate (e.g. net profit margin of 15%) were randomly plucked from the sky. So how do we reconcile them with figures from the financial statements? Fortunately, there appears to be a way to do just that.
Intangible Assets
I refer you to a figure in the financial statements which provides a shortcut to the valuation of 2P Reserves. This is the carrying value of Intangible Assets on the Balance Sheet.
As of 2QFY21, that amount was RM 1,468,860,000 (i.e. RM 1.468 billion).
https://preview.redd.it/hse8ttb09br41.png?width=881&format=png&auto=webp&s=82e48b5961c905fe9273cb6346368de60202ebec
Quite coincidentally, one might observe that this figure is dangerously close to the estimated accumulated Net Profit from 2P Reserves of RM 1.452 billion we calculated earlier. But why would this amount matter at all?
To answer that, I refer you to the notes of the Annual Report FY20 (AR20). On page 148 of the AR20, we find the following two paragraphs:
E&E assets comprise of rights and concession and conventional studies. Following the acquisition of a concession right to explore a licensed area, the costs incurred such as geological and geophysical surveys, drilling, commercial appraisal costs and other directly attributable costs of exploration and appraisal including technical and administrative costs, are capitalised as conventional studies, presented as intangible assets.
E&E assets are assessed for impairment when facts and circumstances suggest that the carrying amount of an E&E asset may exceed its recoverable amount. The Group will allocate E&E assets to cash generating unit (“CGU”s or groups of CGUs for the purpose of assessing such assets for impairment. Each CGU or group of units to which an E&E asset is allocated will not be larger than an operating segment as disclosed in Note 39 to the financial statements.)
Hence, we can determine that firstly, the intangible asset value represents capitalized costs of acquisition of the oil fields, including technical exploration costs and costs of acquiring the relevant licenses. Secondly, an impairment review will be carried out when “the carrying amount of an E&E asset may exceed its recoverable amount”, with E&E assets being allocated to “cash generating units” (CGU) for the purposes of assessment.
On page 169 of the AR20, we find the following:
Carrying amounts of the Group’s intangible assets, oil and gas assets and FPSO are reviewed for possible impairment annually including any indicators of impairment. For the purpose of assessing impairment, assets are grouped at the lowest level CGUs for which there is a separately identifiable cash flow available. These CGUs are based on operating areas, represented by the 2011 North Sabah EOR PSC (“North Sabah”, the Anasuria Cluster, the Marigold and Sunflower fields, the VIC/P57 exploration permit (“VIC/P57”) and the VIC/L31 production license (“VIC/L31”).)
So apparently, the CGUs that have been assigned refer to the respective oil producing fields, two of which include the North Sabah field and the Anasuria field. In order to perform the impairment review, estimates of future cash flow will be made by management to assess the “recoverable amount” (as described above), subject to assumptions and an appropriate discount rate.
Hence, what we can gather up to now is that management will estimate future recoverable cash flows from a CGU (i.e. the North Sabah and Anasuria oil fields), compare that to their carrying value, and perform an impairment if their future recoverable cash flows are less than their carrying value. In other words, if estimated accumulated profits from the North Sabah and Anasuria oil fields are less than their carrying value, an impairment is required.
So where do we find the carrying values for the North Sabah and Anasuria oil fields? Further down on page 184 in the AR20, we see the following:
Included in rights and concession are the carrying amounts of producing field licenses in the Anasuria Cluster amounting to RM668,211,518 (2018: RM687,664,530, producing field licenses in North Sabah amounting to RM471,031,008 (2018: RM414,333,116))
Hence, we can determine that the carrying values for the North Sabah and Anasuria oil fields are RM 471m and RM 668m respectively. But where do we find the future recoverable cash flows of the fields as estimated by management, and what are the assumptions used in that calculation?
Fortunately, we find just that on page 185:
17 INTANGIBLE ASSETS (CONTINUED)
(a Anasuria Cluster)
The Directors have concluded that there is no impairment indicator for Anasuria Cluster during the current financial year. In the previous financial year, due to uncertainties in crude oil prices, the Group has assessed the recoverable amount of the intangible assets, oil and gas assets and FPSO relating to the Anasuria Cluster. The recoverable amount is determined using the FVLCTS model based on discounted cash flows (“DCF” derived from the expected cash in/outflow pattern over the production lives.)
The key assumptions used to determine the recoverable amount for the Anasuria Cluster were as follows:
(i Discount rate of 10%;)
(ii Future cost inflation factor of 2% per annum;)
(iii Oil price forecast based on the oil price forward curve from independent parties; and,)
(iv Oil production profile based on the assessment by independent oil and gas reserve experts.)
Based on the assessments performed, the Directors concluded that the recoverable amount calculated based on the valuation model is higher than the carrying amount.
(b North Sabah)
The acquisition of the North Sabah assets was completed in the previous financial year. Details of the acquisition are as disclosed in Note 15 to the financial statements.
The Directors have concluded that there is no impairment indicator for North Sabah during the current financial year.
Here, we can see that the recoverable amount of the Anasuria field was estimated based on a DCF of expected future cash flows over the production life of the asset. The key assumptions used by management all seem appropriate, including a discount rate of 10% and oil price and oil production estimates based on independent assessment. From there, management concludes that the recoverable amount of the Anasuria field is higher than its carrying amount (i.e. no impairment required). Likewise, for the North Sabah field.
How do we interpret this? Basically, what management is saying is that given a 10% discount rate and independent oil price and oil production estimates, the accumulated profits (i.e. recoverable amount) from both the North Sabah and the Anasuria fields exceed their carrying amounts of RM 471m and RM 668m respectively.
In other words, according to management’s own estimates, the carrying value of the Intangible Assets of RM 1.468 billion approximates the accumulated Net Profit recoverable from 2P reserves.
To conclude Valuation (Method 2), we arrive at the following:

Our estimates Management estimates
Accumulated Net Profit from 2P Reserves RM 1.452 billion RM 1.468 billion

Financials

By now, we have established the basic economics of Hibiscus’s business, including its revenues (i.e. oil production and oil price scenarios), costs (OPEX, ITDA), profitability (breakeven, future earnings potential) and balance sheet value (2P reserves, valuation). Moving on, we want to gain a deeper understanding of the 3 statements to anticipate any blind spots and risks. We’ll refer to the financial statements of both the FY20 annual report and the 2Q21 quarterly report in this analysis.
For the sake of brevity, I’ll only point out those line items which need extra attention, and skip over the rest. Feel free to go through the financial statements on your own to gain a better familiarity of the business.
https://preview.redd.it/h689bss79br41.png?width=810&format=png&auto=webp&s=ed47fce6a5c3815dd3d4f819e31f1ce39ccf4a0b
Income Statement
First, we’ll start with the Income Statement on page 135 of the AR20. Revenues are straightforward, as we’ve discussed above. Cost of Sales and Administrative Expenses fall under the jurisdiction of OPEX, which we’ve also seen earlier. Other Expenses are mostly made up of Depreciation & Amortization of RM 115m.
Finance Costs are where things start to get tricky. Why does a company which carries no debt have such huge amounts of finance costs? The reason can be found in Note 8, where it is revealed that the bulk of finance costs relate to the unwinding of discount of provision for decommissioning costs of RM 25m (Note 32).
https://preview.redd.it/4omjptbe9br41.png?width=1019&format=png&auto=webp&s=eaabfc824134063100afa62edfd36a34a680fb60
This actually refers to the expected future costs of restoring the Anasuria and North Sabah fields to their original condition once the oil reserves have been depleted. Accounting standards require the company to provide for these decommissioning costs as they are estimable and probable. The way the decommissioning costs are accounted for is the same as an amortized loan, where the initial carrying value is recognized as a liability and the discount rate applied is reversed each year as an expense on the Income Statement. However, these expenses are largely non-cash in nature and do not necessitate a cash outflow every year (FY20: RM 69m).
Unwinding of discount on non-current other payables of RM 12m relate to contractual payments to the North Sabah sellers. We will discuss it later.
Taxation is another tricky subject, and is even more significant than Finance Costs at RM 161m. In gist, Hibiscus is subject to the 38% PITA (Petroleum Income Tax Act) under Malaysian jurisdiction, and the 30% Petroleum tax + 10% Supplementary tax under UK jurisdiction. Of the RM 161m, RM 41m of it relates to deferred tax which originates from the difference between tax treatment and accounting treatment on capitalized assets (accelerated depreciation vs straight-line depreciation). Nonetheless, what you should take away from this is that the tax expense is a tangible expense and material to breakeven analysis.
Fortunately, tax is a variable expense, and should not materially impact the cash flow of Hibiscus in today’s low oil price environment.
Note: Cash outflows for Tax Paid in FY20 was RM 97m, substantially below the RM 161m tax expense.
https://preview.redd.it/1xrnwzm89br41.png?width=732&format=png&auto=webp&s=c078bc3e18d9c79d9a6fbe1187803612753f69d8
Balance Sheet
The balance sheet of Hibiscus is unexciting; I’ll just bring your attention to those line items which need additional scrutiny. I’ll use the figures in the latest 2Q21 quarterly report (2Q21) and refer to the notes in AR20 for clarity.
We’ve already discussed Intangible Assets in the section above, so I won’t dwell on it again.
Moving on, the company has Equipment of RM 582m, largely relating to O&G assets (e.g. the Anasuria FPSO vessel and CAPEX incurred on production enhancement projects). Restricted cash and bank balances represent contractual obligations for decommissioning costs of the Anasuria Cluster, and are inaccessible for use in operations.
Inventories are relatively low, despite Hibiscus being an E&P company, so forex fluctuations on carrying value of inventories are relatively immaterial. Trade receivables largely relate to entitlements from Petronas and BP (both oil supermajors), and are hence quite safe from impairment. Other receivables, deposits and prepayments are significant as they relate to security deposits placed with sellers of the oil fields acquired; these should be ignored for cash flow purposes.
Note: Total cash and bank balances do not include approximately RM 105 m proceeds from the North Sabah December 2019 offtake (which was received in January 2020)
Cash and bank balances of RM 90m do not include RM 105m of proceeds from offtake received in 3Q21 (Jan 2020). Hence, the actual cash and bank balances as of 2Q21 approximate RM 200m.
Liabilities are a little more interesting. First, I’ll draw your attention to the significant Deferred tax liabilities of RM 457m. These largely relate to the amortization of CAPEX (i.e. Equipment and capitalized E&E expenses), which is given an accelerated depreciation treatment for tax purposes.
The way this works is that the government gives Hibiscus a favorable tax treatment on capital expenditures incurred via an accelerated depreciation schedule, so that the taxable income is less than usual. However, this leads to the taxable depreciation being utilized quicker than accounting depreciation, hence the tax payable merely deferred to a later period – when the tax depreciation runs out but accounting depreciation remains. Given the capital intensive nature of the business, it is understandable why Deferred tax liabilities are so large.
We’ve discussed Provision for decommissioning costs under the Finance Costs section earlier. They are also quite significant at RM 266m.
Notably, the Other Payables and Accruals are a hefty RM 431m. What do they relate to? Basically, they are contractual obligations to the sellers of the oil fields which are only payable upon oil prices reaching certain thresholds. Hence, while they are current in nature, they will only become payable when oil prices recover to previous highs, and are hence not an immediate cash outflow concern given today’s low oil prices.
Cash Flow Statement
There is nothing in the cash flow statement which warrants concern.
Notably, the company generated OCF of approximately RM 500m in FY20 and RM 116m in 2Q21. It further incurred RM 330m and RM 234m of CAPEX in FY20 and 2Q21 respectively, largely owing to production enhancement projects to increase the production rate of the Anasuria and North Sabah fields, which according to management estimates are accretive to ROI.
Tax paid was RM 97m in FY20 and RM 61m in 2Q21 (tax expense: RM 161m and RM 62m respectively).

Risks

There are a few obvious and not-so-obvious risks that one should be aware of before investing in Hibiscus. We shall not consider operational risks (e.g. uptime, OPEX) as they are outside the jurisdiction of the equity analyst. Instead, we shall focus on the financial and strategic risks largely outside the control of management. The main ones are:
· Oil prices remaining subdued for long periods of time
· Fluctuation of exchange rates
· Customer concentration risk
· 2P Reserves being less than estimated
· Significant current and non-current liabilities
· Potential issuance of equity
Oil prices remaining subdued
Of topmost concern in the minds of most analysts is whether Hibiscus has the wherewithal to sustain itself through this period of low oil prices (sub-$30). A quick and dirty estimate of annual cash outflow (i.e. burn rate) assuming a $20 oil world and historical production rates is between RM 50m-70m per year, which considering the RM 200m cash balance implies about 3-4 years of sustainability before the company runs out of cash and has to rely on external assistance for financing.
Table 1: Hibiscus EBITDA at different oil price and exchange rates
https://preview.redd.it/gxnekd6h9br41.png?width=670&format=png&auto=webp&s=edbfb9621a43480d11e3b49de79f61a6337b3d51
The above table shows different EBITDA scenarios (RM ‘m) given different oil prices (left column) and USD:MYR exchange rates (top row). Currently, oil prices are $27 and USD:MYR is 1:4.36.
Given conservative assumptions of average OPEX/bbl of $20 (current: $15), we can safely say that the company will be loss-making as long as oil remains at $20 or below (red). However, we can see that once oil prices hit $25, the company can tank the lower-end estimate of the annual burn rate of RM 50m (orange), while at RM $27 it can sufficiently muddle through the higher-end estimate of the annual burn rate of RM 70m (green).
Hence, we can assume that as long as the average oil price over the next 3-4 years remains above $25, Hibiscus should come out of this fine without the need for any external financing.
Customer Concentration Risk
With regards to customer concentration risk, there is not much the analyst or investor can do except to accept the risk. Fortunately, 80% of revenues can be attributed to two oil supermajors (Petronas and BP), hence the risk of default on contractual obligations and trade receivables seems to be quite diminished.
2P Reserves being less than estimated
2P Reserves being less than estimated is another risk that one should keep in mind. Fortunately, the current market cap is merely RM 714m – at half of estimated recoverable amounts of RM 1.468 billion – so there’s a decent margin of safety. In addition, there are other mitigating factors which shall be discussed in the next section (‘Opportunities’).
Significant non-current and current liabilities
The significant non-current and current liabilities have been addressed in the previous section. It has been determined that they pose no threat to immediate cash flow due to them being long-term in nature (e.g. decommissioning costs, deferred tax, etc). Hence, for the purpose of assessing going concern, their amounts should not be a cause for concern.
Potential issuance of equity
Finally, we come to the possibility of external financing being required in this low oil price environment. While the company should last 3-4 years on existing cash reserves, there is always the risk of other black swan events materializing (e.g. coronavirus) or simply oil prices remaining muted for longer than 4 years.
Furthermore, management has hinted that they wish to acquire new oil assets at presently depressed prices to increase daily production rate to a targeted 20,000 bbl by end-2021. They have room to acquire debt, but they may also wish to issue equity for this purpose. Hence, the possibility of dilution to existing shareholders cannot be entirely ruled out.
However, given management’s historical track record of prioritizing ROI and optimal capital allocation, and in consideration of the fact that the MD owns 10% of outstanding shares, there is some assurance that any potential acquisitions will be accretive to EPS and therefore valuations.

Opportunities

As with the existence of risk, the presence of material opportunities also looms over the company. Some of them are discussed below:
· Increased Daily Oil Production Rate
· Inclusion of 2C Resources
· Future oil prices exceeding $50 and effects from coronavirus dissipating
Increased Daily Oil Production Rate
The first and most obvious opportunity is the potential for increased production rate. We’ve seen in the last quarter (2Q21) that the North Sabah field increased its daily production rate by approximately 20% as a result of production enhancement projects (infill drilling), lowering OPEX/bbl as a result. To vastly oversimplify, infill drilling is the process of maximizing well density by drilling in the spaces between existing wells to improve oil production.
The same improvements are being undertaken at the Anasuria field via infill drilling, subsea debottlenecking, water injection and sidetracking of existing wells. Without boring you with industry jargon, this basically means future production rate is likely to improve going forward.
By how much can the oil production rate be improved by? Management estimates in their analyst presentation that enhancements in the Anasuria field will be able to yield 5,000 bbl/day by 2021 (current: 2,500 bbl/day).
Similarly, improvements in the North Sabah field is expected to yield 7,000 bbl/day by 2021 (current: 5,300 bbl/day).
This implies a total 2021 expected daily production rate from the two fields alone of 12,000 bbl/day (current: 8,000 bbl/day). That’s a 50% increase in yields which we haven’t factored into our valuation yet.
Furthermore, we haven’t considered any production from existing 2C resources (e.g. Marigold/Sunflower) or any potential acquisitions which may occur in the future. By management estimates, this can potentially increase production by another 8,000 bbl/day, bringing total production to 20,000 bbl/day.
While this seems like a stretch of the imagination, it pays to keep them in mind when forecasting future revenues and valuations.
Just to play around with the numbers, I’ve come up with a sensitivity analysis of possible annual EBITDA at different oil prices and daily oil production rates:
Table 2: Hibiscus EBITDA at different oil price and daily oil production rates
https://preview.redd.it/jnpfhr5n9br41.png?width=814&format=png&auto=webp&s=bbe4b512bc17f576d87529651140cc74cde3d159
The left column represents different oil prices while the top row represents different daily oil production rates.
The green column represents EBITDA at current daily production rate of 8,000 bbl/day; the orange column represents EBITDA at targeted daily production rate of 12,000 bbl/day; while the purple column represents EBITDA at maximum daily production rate of 20,000 bbl/day.
Even conservatively assuming increased estimated annual ITDA of RM 500m (FY20: RM 318m), and long-term average oil prices of $50 (FY20: $60), the estimated Net Profit and P/E ratio is potentially lucrative at daily oil production rates of 12,000 bbl/day and above.
2C Resources
Since we’re on the topic of improved daily oil production rate, it bears to pay in mind the relatively enormous potential from Hibiscus’s 2C Resources. North Sabah’s 2C Resources alone exceed 30 mmbbl; while those from the yet undiagnosed Marigold/Sunflower fields also reach 30 mmbbl. Altogether, 2C Resources exceed 70 mmbbl, which dwarfs the 44 mmbbl of 2P Reserves we have considered up to this point in our valuation estimates.
To refresh your memory, 2C Resources represents oil volumes which have been discovered but are not yet classified as “commercial”. This means that there is reasonable certainty of the oil being recoverable, as opposed to simply being in the very early stages of exploration. So, to be conservative, we will imagine that only 50% of 2C Resources are eligible for reclassification to 2P reserves, i.e. 35 mmbbl of oil.
https://preview.redd.it/mto11iz7abr41.png?width=375&format=png&auto=webp&s=e9028ab0816b3d3e25067447f2c70acd3ebfc41a
This additional 35 mmbbl of oil represents an 80% increase to existing 2P reserves. Assuming the daily oil production rate increases similarly by 80%, we will arrive at 14,400 bbl/day of oil production. According to Table 2 above, this would yield an EBITDA of roughly RM 630m assuming $50 oil.
Comparing that estimated EBITDA to FY20’s actual EBITDA:
FY20 FY21 (incl. 2C) Difference
Daily oil production (bbl/day) 8,626 14,400 +66%
Average oil price (USD/bbl) $68.57 $50 -27%
Average OPEX/bbl (USD) $16.64 $20 +20%
EBITDA (RM ‘m) 632 630 -
Hence, even conservatively assuming lower oil prices and higher OPEX/bbl (which should decrease in the presence of higher oil volumes) than last year, we get approximately the same EBITDA as FY20.
For the sake of completeness, let’s assume that Hibiscus issues twice the no. of existing shares over the next 10 years, effectively diluting shareholders by 50%. Even without accounting for the possibility of the acquisition of new oil fields, at the current market capitalization of RM 714m, the prospective P/E would be about 10x. Not too shabby.
Future oil prices exceeding $50 and effects from coronavirus dissipating
Hibiscus shares have recently been hit by a one-two punch from oil prices cratering from $60 to $30, as a result of both the Saudi-Russian dispute and depressed demand for oil due to coronavirus. This has massively increased supply and at the same time hugely depressed demand for oil (due to the globally coordinated lockdowns being implemented).
Given a long enough timeframe, I fully expect OPEC+ to come to an agreement and the economic effects from the coronavirus to dissipate, allowing oil prices to rebound. As we equity investors are aware, oil prices are cyclical and are bound to recover over the next 10 years.
When it does, valuations of O&G stocks (including Hibiscus’s) are likely to improve as investors overshoot expectations and begin to forecast higher oil prices into perpetuity, as they always tend to do in good times. When that time arrives, Hibiscus’s valuations are likely to become overoptimistic as all O&G stocks tend to do during oil upcycles, resulting in valuations far exceeding reasonable estimates of future earnings. If you can hold the shares up until then, it’s likely you will make much more on your investment than what we’ve been estimating.

Conclusion

Wrapping up what we’ve discussed so far, we can conclude that Hibiscus’s market capitalization of RM 714m far undershoots reasonable estimates of fair value even under conservative assumptions of recoverable oil volumes and long-term average oil prices. As a value investor, I hesitate to assign a target share price, but it’s safe to say that this stock is worth at least RM 1.00 (current: RM 0.45). Risk is relatively contained and the upside far exceeds the downside. While I have no opinion on the short-term trajectory of oil prices, I can safely recommend this stock as a long-term Buy based on fundamental research.
submitted by investorinvestor to SecurityAnalysis [link] [comments]

Suggest any free forex back tester or forex simulator.

I am new to Forex. I am reading a lot. Practicing on demo account. I want to back test my strategy but i can't find anything good.
I have come across few simulators online but they are trail variants and their scale is limited as well.
They download small amount of history data. For longer variant they are asking for payment. They are whoopingpy expensive . I have yet to make anything on Forex. Let alone pay them 200-300 dollars for their full subscription .
Or please suggest a free way to do it. Is it possible on meta trader to practice on history data?
I think problem with meta trader is that you see the future easily. It doesn't hide it for the sake of practice.
submitted by nasir9998 to Forex [link] [comments]

How to optimise the speed of my Pandas code?

Hi learnpython,
My first attempt at writing my own project. Prior to this I had never used classes or Pandas so it's been a difficult learning curve. I was hoping to get some feedback on the overall structure - does everything look sensible? Are there better ways of writing some bits?
I also wanted to specifically check how I can increase the execution speed. I currently iterate rows which Pandas did say will be slow, but I couldn't see a workaround. The fact it is quite slow makes me think there is a better solution that I'm missing.
To run the code yourself download a .csv of Forex data and store in same folder as script - I used Yahoo finance GBP USD.
"""This program simulates a Double SMA (single moving average) trading strategy. The user provides a .csv file containing trade history and two different window sizes for simple moving averages (smallest number first). The .csv must contain date and close columns - trialled on Yahoo FX data). The program will generate a 'buy' signal when the short SMA is greater than the long SMA, and vice versa. The results of each trade are stored and can be output to a .csv file.""" import pandas as pd class DoubleSMA(): """Generates a Double SMA trading system.""" def __init__(self, name, sma_a, sma_b): """Don't know what goes here.""" self.name = name self.sma_a = sma_a self.sma_b = sma_b self.index = 0 self.order = 'Start' self.signal = '' def gen_sma(self, dataset, sma): """Calculates SMA and adds as column to dataset.""" col_title = 'sma' + str(sma) dataset[col_title] = dataset['Close'].rolling(sma).mean() return dataset def gen_signal(self, row, dataset): """Generates trade signal based on comparison of SMAs.""" if row[0] == (dataset.shape[0] - 1): #Reached final line of dataset; close current trade. self.order = 'Finish' elif row[3] > row[4]: self.signal = 'Buy' elif row[3] < row[4]: self.signal = 'Sell' def append_result(row, result, order): """Adds 'entry' details to results dataframe (i.e. opens trade).""" result = result.append({"Entry date": row[1], "Pair": "GBPUSD", "Order": order, "Entry price": row[2]}, ignore_index=True) return result def trade(row, order, signal, index, result): """Executes a buy or sell routine depending on signal. Flips between 'buy' and 'sell' on each trade.""" if order == 'Start': order = signal result = append_result(row, result, order) elif order == 'Finish': result.iloc[index, 1] = row[1] result.iloc[index, 5] = row[2] elif order != signal: #Close current trade result.iloc[index, 1] = row[1] result.iloc[index, 5] = row[2] index += 1 order = signal result = append_result(row, result, order) return order, index, result def result_df(): """Creates a dataframe to store the results of each trade.""" result = pd.DataFrame({"Entry date": [], "Exit date": [], "Pair": [], "Order": [], "Entry price": [], "Exit price": [], "P/L": []}) return result def dataset_df(): """Opens and cleans up the data to be analysed.""" dataset = pd.read_csv('GBPUSD 2003-2020 Yahoo.csv', usecols=['Date', 'Close']) dataset.dropna(inplace=True) dataset['Close'] = dataset['Close'].round(4) return dataset def store_result(result): """Outputs results table to .csv.""" result.to_csv('example.csv') def calc_pl(result): """Calculates the profil/loss of each row of result dataframe.""" pass #Complete later dataset = dataset_df() result = result_df() sma_2_3 = DoubleSMA('sma_2_3', 2, 3) dataset = sma_2_3.gen_sma(dataset, sma_2_3.sma_a) dataset = sma_2_3.gen_sma(dataset, sma_2_3.sma_b) dataset.dropna(inplace=True) dataset.reset_index(inplace=True, drop=True) for row in dataset.itertuples(): sma_2_3.gen_signal(row, dataset) sma_2_3.order, sma_2_3. index, result = trade(row, sma_2_3.order, sma_2_3.signal, sma_2_3.index, result) calc_pl(result) print(result) store_result(result) 
submitted by tbYuQfzB to learnpython [link] [comments]

Trip Planning Step-by-Step Guide

Planning for a trip is a very tiring, yet one of the most exciting thing in my opinion.
I do a very extensive planning, and at times I plan an itinerary even when I don't have any upcoming travel plans. I also enjoy taking inspiration fro other people itinerary.
Although, I feel that there is too much information out there. Lot of effort goes into figuring out, what information is to be used and what is to be ignored.
Step by Step guide on how to plan your next trip - deciding destinations, booking flight and stay, preparing for trip and having fun while traveling.

1. Shortlisting BROAD region or country to visit

Deciding the region to travel to depends on below two factors, in the order decided by personal situation and priority (a must-try experience or activity Versus save some money)

Interest or Flavour of the trip and Dates

Budget

Number of travellers is a factor. I travel economy and I stay in affordable hotels.
Output: Broad region or country which can be visited (i.e. Europe/ SEA / LatAm/ Asia / Oceania / NorAm or slightly more specific Maldives/ Mauritius / French Polynesia / Caribbean)

2. Narrow down on Country to visit -

Decide on the primary/ landing country

Flight search for deals around travel dates Seasonality: not extreme temperature, not too hot (I prefer shoulder seasons) Crowd: not very crowded, but also not deserted ( else, lot of attractions and restaurants are closed down) Remove countries which have any safety concerns around travel dates

For tie-breaker between 2 destinations

Popular festivals Food preference Language comfort and friendly locals What wife says!
Output: Landing country for the trip is finalised

3. Book the onward/return Flight or Train tickets

With good research, (one-way to A + A to B + one-way from B) is cheaper than (two-way from A)
Output: Flight tickets booked

4. Finalise Itinerary - Cities and number of days

I try to stay a minimum of 3 days in a city, so that the only things I remember isn't airport, transit, and hotel check-ins.
Output: A day-wise and city-wise itinerary planned out in a shareable Google sheet, Excel or tool

5. Book Internal commute

Commute can broadly be
(An overnight commute, will save you from booking a stay in the hotel for that night)
Output: Internal Commute booked and and added to itinerary tool

6. Book Stay or Hotels

This is the impossible trinity of stay. Normally, you get only 2 out of the 3 among ( Good Location, Good Amenities, Good Price)

Location or area of city to stay in

*City - Centre (nearby attractions, happening, expensive) VS Outskirts ( faraway attractions, quiet, affordable)

Amenities or comfort of Stay

Budget or Price per might

I research on hotel booking website and read detailed reviews.
Output: Stay Booked and added to itinerary tool

7. To-Do list and shared folder -

Create and execute To-Do List

Shared Folder: Add all flight and hotel bookings and important docs to this and share with co-travellers

Output: All basic and mandatory things planned for the trip

8. Plan activities for the trip

I keep this part of the trip slightly flexible. While, I have 4-5 top things to do( per city ) figured out before the start of trip, for other things, I explore things on the go.
Output: You are 90% ready for the trip

9. Get ready for the trip

Output: Start your trip

10. Enjoy your trip

Output: Have a fun trip

11. Go back to Step 1. Start planning for your next trip

While I come back with great memories and share my travel stories. I start planning for my next trip, having no idea of when to go or where to go.
Original post at Best Trip planning guide on Tripspell
submitted by Tripspell to TripPlanners [link] [comments]

How do you plan your travel? What tools do you use? I follow the following steps.

How do you plan your travel? What tools do you use? I follow the following steps.
Planning for a trip is a very tiring, yet one of the most exciting thing in my opinion.
I do a very extensive planning, and at times I plan an itinerary even when I don't have any upcoming travel plans. I also enjoy taking inspiration from other people itinerary.
Although, I feel that there is too much information out there. Lot of effort goes into figuring out, what information is to be used and what is to be ignored. (List of tools I use are mentioned at the end of the article)

Disclaimer: There is no perfect way to plan a trip. This is how I do it, and still there are lot more things to it, and one post won't do justice to it. I would love to know how you plan your travel.

1. Shortlisting BROAD region or country to visit

Below two factors in the order decided by personal situation and priority (a must-try experience or activity Versus save some money)
  • Interest / Flavour of the trip + Dates
  1. Activities: Mountains / Beach / Museums/ History / Road trip/ Culture (Adventure/ Relaxing/ Romantic / Family)
  2. Dates of Travel: broad idea of month or holiday season

  • Budget (number of travellers is a factor) ( I travel economy and I stay in affordable hotels)
  1. Flight/ Train prices: broad range around $100 / $500 / $1000 two-way ( nearby / little far / very far)
  2. Stay prices: broad range around $30 / $100 / $200 per night ($100 in Bali gets a private villa with pool VS a dingy room in Paris, but assuming a viable stay experience, some places won't offer anything decent for less than $200/300)
  3. Intercity/ Intracity commute: affordable public transport VS semi-affordable car rental (toll/ one way/ two way) VS depends-on-location Taxi services VS expensive private drops (island destinations)

Output: Broad region or country which can be visited eg: Europe/ SEA / LatAm 

2. Narrow down on Country to visit

  • Decide on the primary/ landing country
  1. Flight search for deals around travel dates
  2. Seasonality: not extreme temperature, not too hot (I prefer shoulder seasons)
  3. Crowd: not very crowded, but also not deserted ( else, lot of attractions and restaurants are closed down)
  4. Remove countries which have any safety concerns around travel dates
  • For tie-breaker between 2 destinations
  1. Popular festivals
  2. Food preference
  3. Language comfort and friendly locals
  4. What wife says!

Output: Landing country for the trip is finalised 

3. Book the onward/return Flight or Train tickets

  • Two-way flight: if budget is a constraint + good return flight deal is available + not going to far away destinations on trip
  • One-way flight: if return destination could be different then just book onward flight
with good research, (one-way to A + A to B + one-way from B) is cheaper than (two-way from A)
Output: Flight tickets booked

4. Finalise Itinerary - Cities and number of days

  1. Interests of yours and co-travellers
  2. Commute time/ convenience
  3. Return city is same or different (circular trip or linear trip)
I try to stay a minimum of 3 days in a city, so that the only things I remember isn't airport, transit, and hotel check-ins.
Output: A day-wise and city-wise itinerary planned out in a shareable Google sheet, Excel or tool 

5. Book Internal commute

Commute can broadly be
  • Public transport: I prefer Trains/Buses in Europe, Flights in Asia/SEA
  • Car Rental: Freedom to explore at will. Two-way rental only makes sense. Toll fee, Parking to be taken into account.
  • Private Transport: Island destinations where the resorts arrange your commute
(An overnight commute, will save you from booking a stay in the hotel for that night)
Output: Internal Commute booked and and added to itinerary tool 

6. Book Stay or Hotels

This is the impossible trinity of stay. You get normally only 2 out of the 3 among ( Good Location, Good Amenities, Good Price)
  • Location or area of city to stay in
  1. City - Centre (nearby attractions, happening, expensive) VS Outskirts ( faraway attractions, quiet, affordable)
  2. Prefer day-trips VS spending time in the city (parking is a concern)
  3. Safety of area and connectivity
  • Amenities or comfort of Stay
  1. Hotel VS Hostel VS Airbnb ( meet fellow travellers, party, stay with locals, keep to yourself)
  2. Amenities: Kitchen, Pool, Gym, Pet-Friendly
  • Budget or Price per might
I research on hotel booking website and read detailed reviews.
Output: Stay Booked and added to itinerary tool 

7. To-Do list and shared folder

  • Create and execute To-Do List
  1. Visa, Travel Insurance, Health care
  2. Packing list: based on season, activity planned
  3. Shopping list: based on season, activity planned
  • Shared Folder: Add all flight and hotel bookings and important docs to this and share with co-travellers

Output: All basic and mandatory things planned for the trip 

8. Plan activities for the trip

I keep this part of the trip slightly flexible. While, I have 4-5 top things to do( per city ) figured out before the start of trip, for other things, I explore things on the go.
  • Research things to do: read about activities and food you want to try on blogs, google, tripadvisor, lonelyplanet, youtube etc.
  • Bookmark activities: bookmark or notes tool, Google maps, Tripadvisor, Trip Planing tool
  • Book activities: some popular activities, events tickets or a restaurant you want to visit, have to be booked in advance

Output: You are 90% ready for the trip 

9. Get ready for the trip

  • Pack your things - necessary travel documents (passport, visa)
  • Online check-in of flights
  • Download offline Google maps
  • Sort out payment (Currency, Forex cards etc.)
  • Google translate or popular phrases
  • Important and emergency contacts noted down
  • Inform your family about your plan in case of emergency
  • Sort out your airport/station to hotel commute
  • Inform hotels about any additional requirements
  • Research any safety and local tips

Output: Start your trip 

10. Enjoy your trip

  • Keep flexible schedule
  • Meet local people and fellow travellers
  • Eat local cuisine
  • Don't waste time sleeping in the room
  • Respect the local customs and culture
  • Travel responsibly
  • Do some shopping and bring back gifts for friends and family

Output: Have a fun trip 

11. Go back to Step 1. Start planning for next trip

While I come back with great memories and share my travel stories. I start planning for my next trip, having no idea of when to go or where to go.
Tools I use for
  • Destination Research: Google Search, Blogs, Articles, Forums
  • Flight Deals: Google Flights, Skyscanner, Kayak, Airfarewatchdog, Secretflying
  • Hotel Deals: Airbnb, Booking, Hotels, Agoda, Google Hotels
  • Itinerary: Google Sheet/ Excel/ Notes
  • Packing list: To do/ List apps
  • Things to do: Blogs, Google maps, Viator, Getyourguide, Tripadvisor
submitted by Tripspell to travel [link] [comments]

Looking back 18 months.

I was going through old emails today and came across this one I sent out to family on January 4, 2018. It was a reflection on the 2017 crypto bull market and where I saw it heading, as well as some general advice on crypto, investment, and being safe about how you handle yourself in cryptoland.
I feel that we are on the cusp of a new bull market right now, so I thought that I would put this out for at least a few people to see *before* the next bull run, not after. While the details have changed, I don't see a thing in this email that I fundamentally wouldn't say again, although I'd also probably insist that people get a Yubikey and use that for all 2FA where it is supported.
Happy reading, and sorry for some of the formatting weirdness -- I cleaned it up pretty well from the original email formatting, but I love lists and indents and Reddit has limitations... :-/
Also, don't laught at my token picks from January 2018! It was a long time ago and (luckliy) I took my own advice about moving a bunch into USD shortly after I sent this. I didn't hit the top, and I came back in too early in the summer of 2018, but I got lucky in many respects.
----------------------------------------------------------------------- Jan-4, 2018
Hey all!
I woke up this morning to ETH at a solid $1000 and decided to put some thoughts together on what I think crypto has done and what I think it will do. *******, if you could share this to your kids I’d appreciate it -- I don’t have e-mail addresses, and it’s a bit unwieldy for FB Messenger… Hopefully they’ll at least find it thought-provoking. If not, they can use it as further evidence that I’m a nutjob. 😉
Some history before I head into the future.
I first mined some BTC in 2011 or 2012 (Can’t remember exactly, but it was around the Christmas holidays when I started because I had time off from work to get it set up and running.) I kept it up through the start of summer in 2012, but stopped because it made my PC run hot and as it was no longer winter, ********** didn’t appreciate the sound of the fans blowing that hot air into the room any more. I’ve always said that the first BTC I mined was at $1, but looking back at it now, that’s not true – It was around $2. Here’s a link to BTC price history.
In the summer of 2013 I got a new PC and moved my programs and files over before scrapping the old one. I hadn’t touched my BTC mining folder for a year then, and I didn’t even think about salvaging those wallet files. They are now gone forever, including the 9-10BTC that were in them. While I can intellectually justify the loss, it was sloppy and underlines a key thing about cryptocurrency that I believe will limit its widespread adoption by the general public until it is addressed and solved: In cryptoland, you are your own bank, and if you lose your password or account number, there is no person or organization that can help you reset it so that you can get access back. Your money is gone forever.
On April 12, 2014 I bought my first BTC through Coinbase. BTC had spiked to $1000 and been in the news, at least in Japan. This made me remember my old wallet and freak out for a couple of months trying to find it and reclaim the coins. I then FOMO’d (Fear Of Missing Out”) and bought $100 worth of BTC. I was actually very lucky in my timing and bought at around $430. Even so, except for a brief 50% swing up almost immediately afterwards that made me check prices 5 times a day, BTC fell below my purchase price by the end of September and I didn’t get back to even until the end of 2015.
In May 2015 I bought my first ETH at around $1. I sent some guy on bitcointalk ~$100 worth of BTC and he sent me 100 ETH – all on trust because the amounts were small and this was a small group of people. BTC was down in the $250 range at that point, so I had lost 30-40% of my initial investment. This was of the $100 invested, so not that much in real terms, but huge in percentages. It also meant that I had to buy another $100 of BTC on Coinbase to send to this guy. A few months after I purchased my ETH, BTC had doubled and ETH had gone down to $0.50, halving the value of my ETH holdings. I was even on the first BTC purchase finally, but was now down 50% on the ETH I had bought.
The good news was that this made me start to look at things more seriously. Where I had skimmed white papers and gotten a superficial understanding of the technology before FOMO’ing, I started to act as an investor, not a speculator. Let me define how I see those two different types of activity:
So what has been my experience as an investor? After sitting out the rest of 2015 because I needed to understand the market better, I bought into ETH quite heavily, with my initial big purchases being in March-April of 2016. Those purchases were in the $11-$14 range. ETH, of course, dropped immediately to under $10, then came back and bounced around my purchase range for a while until December of 2016, when I purchased a lot more at around $8.
I also purchased my first ICO in August of 2016, HEAT. I bought 25ETH worth. Those tokens are now worth about half of their ICO price, so about 12.5ETH or $12500 instead of the $25000 they would be worth if I had just kept ETH. There are some other things with HEAT that mean I’ve done quite a bit better than those numbers would suggest, but the fact is that the single best thing I could have done is to hold ETH and not spend the effort/time/cost of working with HEAT. That holds true for about every top-25 token on the market when compared to ETH. It certainly holds true for the many, many tokens I tried to trade in Q1-Q2 of 2017. In almost every single case I would have done better and slept better had I just held ETH instead of trying to be smarter than Mr. Market.
But, I made money on all of them except one because the crypto market went up more in USD terms than any individual coin went down in ETH or BTC terms. This underlines something that I read somewhere and that I take to heart: A rising market makes everyone seem like a genius. A monkey throwing darts at a list of the top 100 cryptocurrencies last year would have doubled his money. Here’s a chart from September that shows 2017 year-to-date returns for the top 10 cryptocurrencies, and all of them went up a *lot* more between then and December. A monkey throwing darts at this list there would have quintupled his money.
When evaluating performance, then, you have to beat the monkey, and preferably you should try to beat a Wall Street monkey. I couldn’t, so I stopped trying around July 2017. My benchmark was the BLX, a DAA (Digital Asset Array – think fund like a Fidelity fund) created by ICONOMI. I wasn’t even close to beating the BLX returns, so I did several things.
  1. I went from holding about 25 different tokens to holding 10 now. More on that in a bit.
  2. I used those funds to buy ETH and BLX. ETH has done crazy-good since then and BLX has beaten BTC handily, although it hasn’t done as well as ETH.
  3. I used some of those funds to set up an arbitrage operation.
The arbitrage operation is why I kept the 11 tokens that I have now. All but a couple are used in an ETH/token pair for arbitrage, and each one of them except for one special case is part of BLX. Why did I do that? I did that because ICONOMI did a better job of picking long-term holds than I did, and in arbitrage the only speculative thing you must do is pick the pairs to trade. My pairs are (No particular order):
I also hold PLU, PLBT, and ART. These two are multi-year holds for me. I have not purchased BTC once since my initial $200, except for a few cases where BTC was the only way to go to/from an altcoin that didn’t trade against ETH yet. Right now I hold about the same 0.3BTC that I held after my first $100 purchase, so I don’t really count it.
Looking forward to this year, I am positioning myself as follows:
Looking at my notes, I have two other things that I wanted to work into this email that I didn’t get to, so here they are:
  1. Just like with free apps and other software, if you are getting something of value and you didn’t pay anything for it, you need to ask why this is. With apps, the phrase is “If you didn’t pay for the product, you are the product”, and this works for things such as pump groups, tips, and even technical analysis. Here’s how I see it.
    1. People don’t give tips on stocks or crypto that they don’t already own that stock or token. Why would they, since if they convince anyone to buy it, the price only goes up as a result, making it more expensive for them to buy in? Sure, you will have friends and family that may do this, but people in a crypto club, your local cryptocurrency meetup, or online are generally not your friends. They are there to make money, and if they can get you to help them make money, they will do it. Pump groups are the worst of these, and no matter how enticing it may look, stay as far away as possible from these scams. I even go so far as to report them when I see them advertise on FB or Twitter, because they are violating the terms of use.
    2. Technical analysis (TA) is something that has been argued about for longer than I’ve been alive, but I think that it falls into the same boat. In short, TA argues that there are patterns in trading that can be read and acted upon to signal when one must buy or sell. It has been used forever in the stock and foreign exchange markets, and people use it in crypto as well. Let’s break down these assumptions a bit.
i. First, if crypto were like the stock or forex markets we’d all be happy with 5-7% gains per year rather than easily seeing that in a day. For TA to work the same way in crypto as it does in stocks and foreign exchange, the signals would have to be *much* stronger and faster-reacting than they work in the traditional market, but people use them in exactly the same way.
ii. Another area where crypto is very different than the stock and forex markets centers around market efficiency theory. This theory says that markets are efficient and that the price reflects all the available information at any given time. This is why gold in New York is similar in price to gold in London or Shanghai, and why arbitrage margins are easily <0.1% in those markets compared to cryptoland where I can easily get 10x that. Crypto simply has too much speculation and not enough professional traders in it yet to operate as an efficient market. That fundamentally changes the way that the market behaves and should make any TA patterns from traditional markets irrelevant in crypto.
iii. There are services, both free and paid that claim to put out signals based on TA for when one should buy and sell. If you think for even a second that they are not front-running (Placing orders ahead of yours to profit.) you and the other people using the service, you’re naïve.
iv. Likewise, if you don’t think that there are people that have but together computerized systems to get ahead of people doing manual TA, you’re naïve. The guys that I have programming my arbitrage bots have offered to build me a TA bot and set up a service to sell signals once our position is taken. I said no, but I am sure that they will do it themselves or sell that to someone else. Basically they look at TA as a tip machine where when a certain pattern is seen, people act on that “tip”. They use software to see that “tip” faster and take a position on it so that when slower participants come in they either have to sell lower or buy higher than the TA bot did. Remember, if you are getting a tip for free, you’re the product. In TA I see a system when people are all acting on free preset “tips” and getting played by the more sophisticated market participants. Again, you have to beat that Wall Street monkey.
  1. If you still don’t agree that TA is bogus, think about it this way: If TA was real, Wall Street would have figured it out decades ago and we would have TA funds that would be beating the market. We don’t.
  2. If you still don’t agree that TA is bogus and that its real and well, proven, then you must think that all smart traders use them. Now follow that logic forward and think about what would happen if every smart trader pushing big money followed TA. The signals would only last for a split second and would then be overwhelmed by people acting on them, making them impossible to leverage. This is essentially what the efficient market theory postulates for all information, including TA.
OK, the one last item. Read this weekly newsletter – You can sign up at the bottom. It is free, so they’re selling something, right? 😉 From what I can tell, though, Evan is a straight-up guy who posts links and almost zero editorial comments.
Happy 2018.
submitted by uetani to CryptoCurrency [link] [comments]

Super Scalp 2.0 mt4 Indicator

Super Scalp 2.0 mt4 Indicator
Download free mt4 Indicators: https://www.forexwinners.in/
Super Scalp 2.0 mt4 Indicator is a combination of Metatrader 4 (MT4) indicator(s) and template. Super Scalping technique is an excellent technique that uses 5 minutes timeframe using AUDUSD, EURUSD, GBPUSD pairs. Indicators used are:
Super Scalp 2.0 mt4 Indicator is a combination of Metatrader 4 (MT4) indicator(s) and template. provides an opportunity to detect various peculiarities and patterns in price dynamics that are invisible to the naked eye. The essence of this forex strategy is to transform the accumulated history data and trading signals.

https://preview.redd.it/3h3htoer4hd41.png?width=997&format=png&auto=webp&s=f2ca4b3eb95df4f2cb4cc0cf9bfa186b66a8ccf8
submitted by mt4indicators to u/mt4indicators [link] [comments]

Can I get into Stanford without any hooks?

Rising senior. Unsure what to major in, and therefore don't really have much of a hook or spike in terms of ECs and all that. I do have some 'impressive' ECs according to people I know, but I'm not sure whether they 1) stack up against other top applicants or 2) tell any sort of coherent story. My dream school is Stanford, and of course it's a crapshoot for literally everyone, but I want to know if I have any chance at all.

Background:

State: Missouri

Race: Indian

Gender: Male

Income: About 130K after taxes

Stats:

GPA: 4.0 weighted, 4.3 weighted

ACT: 35

SAT (subject tests): 1550 (800 in US History, 750 in Math ll)

ECs:


As you can see, I have no clear idea of goal of what I want to do with my life, since I enjoy everything from entrepreneurship to history. Do I have any chance, or is my application too scattered?
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How to Download Historial Forex Data - Metatrader 4 ... How to get FOREX historical data into metatrader? 90% modeling quality Download MT4 history data How to Download Free Forex Historical Data - YouTube Download historical Forex data for FREE in 3 Simple Steps How To Download Historic Data Using The MetaTrader 4 ... How to View or Download Historical Data in MT4  See Upto 50 Year Previous Chart In Metatrader 4

Download and copy the history forex files: Load the necessary data in Forex Strategy Builder (CSV) format. 100 000 bars is a good start. Copy and paste the downloaded forex data files in the new Data Source directory. Now the new data will be available in the Editor. Load Historical Forex Data in Excel . Loading CSV (Comma Separated Values) files in Excel is straightforward. Download the ... How important is Forex Historical Data Download CSV? In fact, past data is so important, that Forex platforms commit a lot of capital to source that information. Some reports claim that Forex brokers and platforms allocate as much as $27 billion for such information globally. Now that is a big sign of how important past data is. Therefore it is ... Tickstory is the trader's historical data downloader and database. Get free historical tick data across various markets and use it in your trading platform. Forex Forum The Global-View Forex Forum is the hub for currency trading on the web. Founded in 1996, it was the original forex forum and is still the place where forex traders around the globe come 24/7 looking for currency trading ideas, breaking forex news, fx trading rumors, fx flows and more. This is where you can find a full suite of forex trading tools, including a complete fx database ... There are a few ways to download historical Forex data. I provide my latest finds on the Resources page. Most quality sources provide data back to about 2001. If you can find clean data sources that go back further than that, let me know in the comments below. But for all intents and purposes, 14+ years of data is good for most testing purposes. I would list the data sources here, but blog ... Do you know of another website that has historical forex data that I can download with the definition of a period? I appreciate your feedback. Greetings Ton. Reply. Sourabh. June 20, 2014 at 7:19 am Hi Ton, I have checked the sheet, it’s working perfectly fine. I don’t know what issues, you are facing. I guess, you have not downloaded correct sheet. Reply. currency exchange rates. June 28 ... Cara Data History Forex For Rates. Forex Historical Data In A Csv File Scalping. Mt4 Tick Data. Mq4 Mt4 Expert Advisor Exports Forex Real Time Historical Data Files. Forex Historical Data Csv. Historical Forex Data For A Specific Timeframe. Free Daily Historical Forex Data. Dukascopy Historic Data Forex Factory . Forex History Data Csv. Forex Historical Data Ea Academy. Myfxbook Updates. How ... Download stock quote history quickly and easily in a variety of formats to suit your needs. Download: ... FOREX: Foreign Exchange: MS7: To make things easier, here are a number of quick links for your daily downloads: Nov 06 2020: Nov 05 2020: Nov 04 2020: Nov 03 2020: Nov 02 2020: Oct 30 2020: Oct 29 2020 : Oct 28 2020: Oct 27 2020: Oct 26 2020: Oct 23 2020: Oct 22 2020: Oct 21 2020: Oct 20 ... If you’re looking for Free Forex Historical Data, you’re in the right place! Here, you’ll be able to find free forex historical data ready to be imported into your favorite application like MetaTrader, NinjaTrader, MetaStock or any other trading platform.. Since the data is delivered in .CSV format (comma separated values), you can use it in any almost any application that allows you to ... Download Free Forex Data. Download Step 1: Please, select the Application/Platform and TimeFrame! In this section you'll be able to select for which platform you'll need the data. MetaTrader 4 / MetaTrader 5. This platform allows the usage of M1 (1 Minute Bar) Data only. These files are well suited for backtesting trading strategies under MetaTrader 4 and MetaTrader 5 platform. Please, select ...

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How to Download Historial Forex Data - Metatrader 4 ...

This video is for those traders that find their charts do not display enough bars. In this video I will show you how to download fresh historical data for yo... How to View or Download Historical Data in MT4 See Upto 50 Year Previous Chart In Metatrader 4 Registration Link Forex Brokers : https://goo.gl/JRFCZe Forex Broker Local Deposit & Withdrawal ... Download historical Forex data for FREE in 3 Simple Steps - Duration: 7:24. Tickstory Software 7,277 views. 7:24. How To Download Free Historical Data With Metatrader 4 - Duration: 8:09. ... Download historical Forex data for FREE in 3 Simple Steps - Duration: 7:24. Tickstory Software 7,206 views. 7:24. Language: English Location: United States Restricted Mode: Off ... How to Backtest and download forex history data to you computer - Duration: 30:02. Fit Money 62,454 views. 30:02. Get FREE historical data for Amibroker in 3 Simple Steps - Duration: 5:05. ... You may not be seeing all of the Forex historical data that is available and that can be a bad thing. ★ Get clean, Daylight Savings Time adjusted MT4 data he... Free Forex Historical Data is a free lecture from Algorithmic Trading Course for Beginners+ 40 EAs. Enroll in the course on our website: https://eaforexacade...

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